CN118102976A

CN118102976A - Sensing systems and methods for diagnosis, staging, treatment and risk assessment of liver disease using monitored analyte data

Info

Publication number: CN118102976A
Application number: CN202380014011.2A
Authority: CN
Inventors: P·P·雷; M·L·约翰逊; Q·安; J·M·哈拉克; R·巴特莱特; J·帕代里
Original assignee: Dexcom Inc
Current assignee: Dexcom Inc
Priority date: 2022-02-02
Filing date: 2023-02-02
Publication date: 2024-05-28

Abstract

Certain aspects of the present disclosure relate to methods and systems for generating and utilizing analyte measurements. In certain aspects, a monitoring system comprises: a continuous analyte sensor configured to generate an analyte measurement associated with an analyte level of a patient; and a sensor electronics module coupled to the continuous analyte sensor and configured to receive and process the analyte measurement.

Description

Sensing systems and methods for diagnosis, staging, treatment and risk assessment of liver disease using monitored analyte data

Cross Reference to Related Applications

The present application claims priority and benefit from U.S. provisional patent application No. 63/267,447 filed 2 month 2 of 2022, U.S. provisional patent application No. 63/403,568 filed 9 month 2 of 2022, and U.S. provisional patent application No. 63/403,582 filed 9 month 2 of 2022, which are hereby assigned to the assignee of the present application and are expressly incorporated herein by reference in their entirety as if fully set forth below and for all applicable purposes.

Background

Liver disease, also known as liver disease, is any liver dysfunction that results in illness. The liver is responsible for many key functions in the human body from protein production and blood clotting to cholesterol, lactate, glucose and iron metabolism. If the liver is diseased or damaged, such impairment or loss of function can cause serious damage to the human body.

Liver disease is generally classified as acute or chronic based on the duration of the disease. Liver diseases may be caused by infection, injury, exposure to drugs or toxic compounds, alcohol, impurities in food, abnormal accumulation of normal substances in the blood stream, autoimmune processes, genetic defects (e.g., hemochromatosis), and/or unknown causes. Common liver diseases include cirrhosis, liver fibrosis, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), hepatic ischemia reperfusion injury, primary Biliary Cirrhosis (PBC), and hepatitis.

The U.S. liver foundation estimates that more than 20% of the population suffers from NAFLD. It has been suggested that obesity, unhealthy diet, and sedentary lifestyles may lead to high prevalence of NAFLD. If untreated, NAFLD may progress to NASH, causing serious adverse effects on the body. Once NASH is developed, a person may experience liver swelling and scarring (i.e., cirrhosis) over time.

Lactate is measured and analyzed using a variety of methods, including central laboratory methods, near-patient blood gas analysis, and analysis using portable point-of-care (POC) hand-held devices. The central laboratory method involves transporting a patient's blood sample to a laboratory via a carrier or tracheal system. Unfortunately, central laboratory methods often suffer from the problem of lengthy time between drawing blood and the clinician knowing the test results, resulting in possible delays in clinical decisions.

With advances in POC technology, near-patient bench-top blood gas analyzers have become available for lactate testing. However, these devices are not portable and their usability is typically limited to a single specialized unit, such as an emergency room (ED) and an Intensive Care Unit (ICU). Furthermore, when samples are drawn outside of these main units, the sample turnaround time of the test results may be affected by delays in transportation to the ED or ICU.

For this reason, small hand-held devices much like blood glucose meters have been available for lactate measurement and analysis. The user may carry a self-monitoring lactate monitor that typically requires the user to prick his or her finger to measure his or her lactate level. However, in view of the inconveniences associated with conventional finger prick methods, it is unlikely that a user will make timely lactate measurements. Thus, lack of use of the device by the user may have catastrophic consequences. In particular, users who forego using a device may also be unable to manage their condition outside of the use of the device. In the event that the condition is not managed for too long, the user's liver condition may deteriorate significantly, additional health problems may occur, and in some cases, result in increased risk of death or likelihood.

Drawings

So that the manner in which the above recited features of the present disclosure can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to various aspects, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only certain typical aspects of this disclosure and are therefore not to be considered limiting of its scope, for the description may admit to other equally effective aspects.

FIG. 1 illustrates aspects of an exemplary decision support system that may be used in connection with implementing embodiments of the present disclosure.

FIG. 2 is a diagram conceptually illustrating an exemplary continuous analyte monitoring system including an exemplary continuous analyte sensor having sensor electronics, in accordance with certain aspects of the present disclosure.

FIG. 3 illustrates exemplary inputs and exemplary metrics calculated based on the inputs for use by the decision support system of FIG. 1, according to some embodiments disclosed herein.

Fig. 4 is a flow chart illustrating an exemplary method for providing decision support using a continuous analyte sensor including at least a continuous lactate sensor, according to some exemplary aspects of the present disclosure.

Fig. 5 is an exemplary workflow for determining liver lactate clearance rate using at least a continuous lactate monitor in accordance with certain embodiments of the present disclosure.

Fig. 6 is a flow chart depicting a method for training a machine learning model to provide predictions of liver disease diagnosis in accordance with certain embodiments of the present disclosure.

Fig. 7 is a block diagram depicting an exemplary computing device configured to execute a decision support engine in accordance with certain embodiments of the present disclosure.

Fig. 8A-8B depict exemplary enzyme domain configurations for continuous multi-analyte sensors according to certain embodiments of the present disclosure.

Fig. 8C-8D depict exemplary enzyme domain configurations for continuous multi-analyte sensors according to certain embodiments of the present disclosure.

Fig. 8E depicts an exemplary enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure.

Fig. 9A-9B depict alternative views of an exemplary dual electrode enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure.

Fig. 9C-9D depict alternative views of exemplary dual electrode enzyme domain configurations for continuous multi-analyte sensors, according to certain embodiments of the present disclosure.

Fig. 9E depicts an exemplary dual electrode configuration for a continuous multi-analyte sensor in accordance with certain embodiments of the present disclosure.

FIG. 10A depicts an exemplary enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure.

Fig. 10B-10C depict alternative exemplary enzyme domain configurations for continuous multi-analyte sensors according to certain embodiments of the present disclosure.

FIG. 11 depicts an exemplary enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure.

Fig. 12A-12D depict alternative views of exemplary dual electrode enzyme domain configurations G1-G4 for continuous multi-analyte sensors, according to certain embodiments of the present disclosure.

Fig. 13A depicts an exemplary lactate sensor according to certain embodiments of the present disclosure.

Fig. 13B depicts a cross-sectional view of an electroactive section of the exemplary lactate sensor of fig. 13A, according to certain embodiments of the present disclosure.

Fig. 14A-14C depict exemplary embodiments of a continuous analyte sensor system implemented as a wearable lactate sensor, according to certain embodiments of the present disclosure.

To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements disclosed in one aspect may be beneficially employed in other aspects without specific recitation.

Detailed Description

Liver diseases are not easily found. In particular, the liver is sometimes referred to as a silent organ, because symptoms are often not noticed even when liver failure occurs. In some cases, liver disease may have reached an advanced stage when symptoms such as jaundice become apparent. Thus, early liver disease diagnosis and staging is critical to effective treatment and in some cases to reverse the disease.

Disease diagnosis is a behavior or process that identifies or determines the nature and cause of a disease, while disease staging is a clinical-based severity metric that uses objective medical criteria to evaluate the identified stage of disease progression. More specifically, disease staging may provide important information about the extent of liver disease in a patient and the patient's expected response to different forms of treatment.

Physicians use information from patient history, physical examination, laboratory tests, and other diagnostic tests to diagnose and stage disease to prescribe appropriate treatments. For example, a doctor may use liver function tests to screen liver infections, monitor the progress of liver disease, evaluate the effectiveness of different treatments of liver disease, and monitor the possible side effects of drugs on the liver of a patient, etc. Liver function tests examine the levels of certain enzymes and proteins in the blood of a patient. In some cases, levels above or below normal may indicate liver problems.

However, such conventional liver disease diagnosis and staging methods face a number of challenges in providing the efficiency, accuracy and delay of liver diagnosis for therapeutic decisions. For example, it may be difficult to determine which particular diagnosis a particular combination of symptoms indicates, particularly if the symptoms are non-specific, such as fatigue. Liver disease may also appear atypical, with a range of unusual and unexpected symptoms. Currently, the standard treatment for definitive diagnosis of liver disease is liver biopsy. Liver biopsy is a non-scalable diagnostic modality for invasive liver disease. Thus, making an accurate diagnosis has proven to be particularly challenging for doctors. Furthermore, when a patient seeks medical care, there is an iterative process of information collection, information integration and interpretation, and determination of the most likely diagnosis, and throughout the diagnosis process, there is a continuous assessment of whether sufficient information has been collected. If the physician does not consider that the necessary information has been collected to explain the health problem of the patient or to accurately diagnose the patient as suffering from liver disease, or that the available information is inconsistent with the diagnosis of liver disease, the process of information collection, information integration and interpretation and formation of the most probable diagnosis may continue. Thus, diagnosis of liver disease may be delayed and, in some cases, in view of the time-dependent nature of many diseases (including liver disease), the patient may experience worsening symptoms, reduced overall health, and even death.

Furthermore, prior art such as point of contact (POC) devices have been introduced to enable timely assessment of patients suffering from or at risk of liver disease. As mentioned previously, one such POC device may include a portable self-monitoring lactate monitor that typically requires the user to prick his or her finger to give a single independent reading indicative of his or her lactate level for diagnosing liver health. To date, POC devices include almost exclusively diagnostic devices-devices that can analyze a patient to give a single independent reading. As such, existing devices suffer from the technical problem of being unable to continuously (and/or semi-continuously and/or periodically) monitor the concentration of a changing analyte (such as lactate) to give a continuous (and/or semi-continuous and/or periodic) readout. Continuous monitoring of such analytes is advantageous for diagnosing and staging a disease in a patient, as continuous measurements provide continuous up-to-date measurements as well as information about the trend and rate of change of the analyte over a continuous period of time.

Continuous measurements as presented herein provide a more accurate indication of liver metabolic function and liver health than single point-in-time readings. A single point in time reading may be affected by patient activity, such as exercise or dietary changes near or during that point in time. Additionally, imaging techniques that determine structural aspects of the liver are unable to provide information about the metabolic functional properties of liver cell tissue. Measuring analytes (e.g., lactate) in continuous readouts as set forth herein may increase understanding of the metabolic function of the liver, to confirm good liver performance, or to determine the presence and/or extent of metabolic dysfunction of the liver. Such information may also be used to make more informed decisions in liver health assessment and liver disease treatment. Due to this technical problem, diagnosing liver diseases or the risk thereof may be inaccurate, which may prove to be life threatening for patients suffering from liver diseases in some cases.

Thus, certain embodiments described herein provide a technical solution to the above-described technical problems by providing decision support around liver disease using a continuous analyte monitoring system comprising at least a continuous lactate sensor. As used herein, the term "continuous" may refer to fully continuous, semi-continuous, periodic, and the like. Decision support for liver disease treatment may be provided in the form of risk assessment, diagnosis, staging and/or recommendations, as described in more detail herein. As used herein, risk assessment may refer to an assessment or estimation of a patient's liver disease reaching a more advanced stage, risk of death, risk of liver cancer, etc.

In certain embodiments, the continuous analyte monitoring system may provide decision support to the patient based on various collected data including analyte data, patient information, secondary sensor data (e.g., non-analyte data), and the like. For example, the analyte data may include continuously monitored lactate data as well as other continuously monitored analyte data such as glucose, ketones, and potassium.

Lactate data continuously monitored may be indicative of or used to determine lactate levels, lactate production rates, lactate metabolism, and/or lactate clearance rates of the patient. Certain embodiments of the present disclosure provide techniques and systems for more accurately determining a patient's lactate clearance rate using continuously monitored lactate data and correcting the patient's lactate clearance rate by using measurements associated with non-analyte sensor data and/or other patient information, as described further below. As described above, the collected data also includes patient information, which may include information related to age, gender, family history of liver disease, other health conditions, and the like. The secondary sensor data may include accelerometer data, heart rate data, temperature, blood pressure, or any other sensor data other than analyte data.

According to embodiments of the present disclosure, the decision support system presented herein is designed to provide diagnosis as well as disease decision support to patients suffering from or at risk of liver disease to assist patients in managing their liver disease or risk thereof. Providing liver disease decision support may involve using a large collection of data, including, for example, the analyte data, patient information, and secondary sensor data mentioned above, to (1) automatically detect and classify abnormal liver conditions, (2) evaluate the presence and severity of liver disease, (3) risk stratification of patients to identify those at higher risk of liver disease, (4) identify risks associated with current liver disease diagnosis (e.g., risk of mortality, risk of liver cancer, etc.), (5) make patient-specific treatment decisions or recommendations for liver disease, and (6) provide information about the effects of interventions (e.g., effects of changes in patient lifestyle, effects of surgery, effects of patients taking new medications, etc.). In other words, the decision support systems presented herein may provide information to guide and help improve care for patients suffering from or at risk of liver disease.

In certain embodiments, the decision support systems described herein may use various algorithms or Artificial Intelligence (AI) models, such as machine learning models, trained based on patient-specific data and/or population data to provide real-time decision support to a patient based on collected information about the patient. For example, certain aspects relate to algorithms and/or machine learning models designed to assess the presence and severity of liver disease in a patient. Algorithms and/or machine learning models may be used in conjunction with one or more continuous analyte sensors including at least a continuous lactate sensor to provide liver disease assessment and staging, e.g., on a periodic basis (e.g., daily, weekly, etc.). In particular, the algorithm and/or machine learning model may consider patient parameters such as lactate clearance rate (including lactate half-life), lactate levels, lactate change rate, fasting lactate, postprandial lactate, lactate production rate, and lactate line when diagnosing and staging liver disease.

Based on these parameters, algorithms and/or machine learning models may provide a risk assessment of one or more liver disease types and severity, as well as progress that the patient has made toward one or more of these liver disease types. Algorithms and/or machine learning models may consider population data, personalized patient-specific data, or a combination of both in diagnosing and staging liver disease in a patient.

According to some embodiments, the machine learning model is trained using training data (e.g., including population data) prior to deployment of the machine learning model. As described in more detail herein, population data comprising data records of historical patients suffering from different stages of liver disease may be provided in the form of data sets. Each data record may then be characterized (e.g., refined into a set of one or more features, or predicted variables) and marked. Data tagging is the process of adding one or more meaningful and informative tags to provide context to data for machine learning model learning. In certain embodiments, each data record is labeled with its corresponding liver disease diagnosis, assigned disease score, risk assessment, and the like. Features associated with each data record may be used as inputs to the machine learning model, and the generated output may be compared to the labels assigned to each of the data records. The model may calculate the loss based on the difference between the generated output and the provided signature. This loss can then be used to modify the internal parameters or weights of the model. By iteratively processing the features associated with each data record corresponding to each historic patient, the model can be iteratively refined to generate an accurate prediction of the presence and severity of liver disease for the patient.

The combination of a continuous analyte monitoring system with machine learning models and/or algorithms provided by the decision support system described herein for diagnosis, staging and risk assessment of liver disease enables real-time diagnosis to allow early intervention. In particular, the decision support system may be used to provide early warning of liver decompensation and/or to deliver information about other complications related to the liver. Early detection of such decompensation and/or other complications may allow intervention at as early a stage as possible to ultimately improve liver disease outcome. For example, in some cases, early intervention may reduce hospitalization, complications, and death. Further, the baseline lactate levels and lactate level changes provided by the continuous analyte monitoring system may be used as inputs to a machine learning model and/or algorithm to classify patients for more urgent care. In patients at risk of sepsis or septic shock, traumatic brain injury, acute kidney injury, hepatic encephalopathy, or end-stage liver disease, an increase in lactate may be used to notify emergency medical intervention.

Furthermore, by combining a continuous analyte monitoring system with machine learning and/or algorithms for diagnosis, staging and risk assessment of liver disease, the decision support system described herein may provide the necessary accuracy and reliability desired by a patient. For example, when assessing the presence and severity of liver disease in a patient, bias, human error, and emotional impact may be minimized. Furthermore, the combination of machine learning models and algorithms with analyte monitoring systems may provide insight into patterns and/or trends of patient health degradation, at least in terms of liver, which may have been missed previously. Thus, the decision support system described herein may assist in identifying liver health for diagnostic, prophylactic and therapeutic purposes.

Exemplary decision support System for diagnosis, staging, treatment and Risk assessment of liver diseases including exemplary analyte Sensors

Fig. 1 shows an exemplary decision support system (also referred to as a "monitoring system") 100 for diagnosing, staging, treating, and risk assessing liver disease in a user 102 (referred to herein as a user alone and referred to herein as a user collectively) using a continuous analyte monitoring system 104 including at least a continuous lactate sensor. In certain embodiments, the user may be a patient, or in some cases, a caregiver of the patient. In certain embodiments, the system 100 includes a continuous analyte monitoring system 104, a display device 107 executing an application 106, a decision support engine 114, a user database 110, a history database 112, a training server system 140, and a decision support engine 114, each of which are described in more detail below.

As used herein, the term "analyte" is a broad term used in its ordinary sense, including but not limited to, to a substance or chemical constituent in a biological fluid (e.g., blood, interstitial fluid, cerebral spinal fluid, lymph fluid, or urine) that can be analyzed. Analytes may include naturally occurring substances, artificial substances, metabolites and/or reaction products. Analytes for measurement by the devices and methods may include, but are not limited to, potassium, glucose, carboxyprothrombin; acyl carnitines; adenine phosphoribosyl transferase; adenosine deaminase; albumin; alpha fetoprotein; amino acid profile (arginine (krebs cycle), histidine/urocanic acid, homocysteine, phenylalanine/tyrosine, tryptophan); androstenedione; antipyrine; an enantiomer of arabitol; arginase; benzoyl ecgonine (cocaine); a biotin enzyme; biopterin; c peptide; c-reactive protein; carnitine; a carnosine enzyme; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-beta hydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MM isozymes; cyclosporin a; d-penicillamine; de-ethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylase polymorphism, alcohol dehydrogenase, alpha 1-antitrypsin, cystic fibrosis, dunaliella/Beck muscular dystrophy, glucose-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F, D-bystander, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, leibbean hereditary optic neuropathy, MCAD, RNA, PKU, plasmodium vivax, sexual differentiation, 21-deoxycortisol); debutyl halofanning; dihydropteridine reductase; diphtheria/tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acid/acyl glycine; free beta-human chorionic gonadotrophin; free erythrocyte porphyrin; free thyroxine (FT 4); free triiodothyronine (FT 3); fumarylacetoacetate; galactose/gal-1-phosphate; galactose-1-phosphate uridyltransferase; gentamicin; glucose-6-phosphate dehydrogenase; glutathione; glutathione peroxidase; glycocholic acid; glycosylated hemoglobin; a halofanning group; a hemoglobin variant; hexosaminidase a; human erythrocyte carbonic anhydrase I; 17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase; immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, beta); lysozyme; mefloquine; netilmicin; phenobarbital; phenytoin; phytanic acid/pristanic acid; progesterone; prolactin; a proline enzyme; purine nucleoside phosphorylase; quinine; reverse triiodothyronine (rT 3); selenium; serum pancreatic lipase; sisomicin; growth regulator C; specific antibodies (adenovirus, antinuclear antibody, anti-zeta antibody, arbovirus, oyersinia virus, dengue virus, meinariana nematode, echinococcus granulosus, america dysentery, enterovirus, giardia, helicobacter pylori, hepatitis B virus, herpes virus, HIV-1, igE (atopic disease), influenza virus, leishmania donovani, leptospira, measles/mumps/rubella, mycobacterium leprosy, mycoplasma pneumoniae, myoglobin, wire tail worm, parainfluenza virus, plasmodium falciparum, poliovirus, pseudomonas aeruginosa, respiratory syncytial virus, rickettsia (tsiosis), chikungunyi, Schistosoma mansoni, toxoplasma gondii, treponema pallidum, trypanosoma cruzi/trypanosoma lanuginosa, vesicular stomatitis virus, evohizome bane, yellow fever virus); specific antigen (hepatitis B virus, HIV-1); succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine (T4); thyroxine-binding globulin; trace elements; transferrin; UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A; white blood cells; zinc protoporphyrin. In certain embodiments, naturally occurring salts, sugars, proteins, fats, vitamins, and hormones in the blood or interstitial fluid may also constitute the analyte. Analytes may occur naturally in biological fluids, e.g., metabolites, hormones, antigens, antibodies, etc. Alternatively, the analyte may be introduced into the body or the analyte may be exogenous, such as imaging contrast agents, radioisotopes, chemical agents, fluorocarbon-based synthetic blood, stimulator analytes (e.g., introduced for the purpose of measuring an increase and/or decrease in the rate of change of concentration of the stimulator analyte introduced or other analytes) or drugs or pharmaceutical compositions, including but not limited to insulin (e.g., exogenous or endogenous); glucagon, ethanol; cannabis (cannabis, tetrahydrocannabinol, cannabis indiana); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorinated hydrocarbons, hydrocarbons); cocaine (cleaved cocaine); stimulants (amphetamine, methamphetamine, ritaline, cylert, preludin, didrex, preState, voranil, sandrex, plegine); sedatives (barbiturates, mequinones, tranquilizers such as diazepam, chlordiazepoxide, sulning, me Ding Shuang urea, tranxene); hallucinogens (phencyclidine, lysergic acid, mo Sika, pinabout, nuda, etc.); anesthetic (heroin, codeine, morphine, opium, pethidine, percocet, percodan, tussionex, fentanyl, darvon, talwin, lomotil); specially-produced drugs (analogues of fentanyl, pethidine, amphetamine, methamphetamine, and phencyclidine, e.g., headshaking); anabolic steroids; and nicotine. Metabolites of drugs and pharmaceutical compositions are also contemplated analytes. Analytes produced in vivo such as neurochemicals and other chemicals, for example, ascorbic acid, uric acid, dopamine, norepinephrine, 3-methoxytyramine (3 MT), 3, 4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5-hydroxytryptamine (5 HT) and 5-hydroxyindoleacetic acid (FHIAA), and intermediates in the citric acid cycle can also be analyzed.

While the analytes measured and analyzed by the devices and methods described herein include lactate, and in some cases glucose, ketone, and/or potassium, other analytes listed above are also contemplated, but are not limited to.

In certain embodiments, the continuous analyte monitoring system 104 is configured to continuously measure one or more analytes and transmit the analyte measurements to an Electronic Medical Record (EMR) system and/or interface engine (not shown in fig. 1). EMR systems are software platforms that allow digital medical data to be electronically entered, stored, and maintained. The interface engine is a data synchronization tool for ensuring that EMR databases and other system databases are synchronized over a network. EMR systems are commonly used in hospitals and/or other caregivers facilities to record clinical information of patients over a long period of time. The EMR system organizes and presents data in a manner that assists the clinician, for example, in interpreting health conditions and providing ongoing care, scheduling, billing, and follow-up. The data contained in the EMR system can also be used to create reports of clinical care and/or disease management for the patient.

In certain embodiments, the continuous analyte monitoring system 104 is configured to continuously measure one or more analytes and transmit the analyte measurements to the display device 107 for use by the application 106. In some embodiments, continuous analyte monitoring system 104 transmits analyte measurements to display device 107 via a wireless connection (e.g., a bluetooth connection, a WiFi connection, and/or NFC). The transmission of analyte measurements may be broadcast or on-demand and continuous (e.g., fully continuous, semi-continuous, or periodic). In some embodiments, the display device 107 is a smart phone. However, in certain other embodiments, the display device 107 may alternatively be any other type of computing device, such as a laptop computer, a smartwatch, a tablet computer, or any other computing device capable of executing the application 106. The continuous analyte monitoring system 104 may be described in more detail with reference to FIG. 2.

Application 106 is a mobile health application configured to receive and analyze analyte measurements from analyte monitoring system 104. In particular, the application 106 stores information about the user, including analyte measurements of the user, in a user profile 118 associated with the user for processing and analysis, and for use by the decision support engine 114 to provide decision support recommendations or guidelines to the user.

Decision support engine 114 refers to a set of software instructions having one or more software modules including a Data Analysis Module (DAM) 116. In some embodiments, the decision support engine 114 executes entirely on one or more computing devices in the private or public cloud. In such implementations, the application 106 communicates with the decision support engine 114 over a network (e.g., the internet). In some other embodiments, the decision support engine 114 executes in part on one or more local devices, such as the display device 107, and in part on one or more computing devices in the private or public cloud. In some other embodiments, the decision support engine 114 executes entirely on one or more local devices, such as the display device 107 or the analyte sensor system 104 (e.g., the sensor electronics module 204 of fig. 2). As discussed in more detail herein, the decision support engine 114 may provide decision support recommendations to the user via the application 106. Decision support engine 114 provides decision support recommendations based on information included in user profile 118.

The user profile 118 may include information about the user collected from the application 106. For example, the application 106 provides a set of inputs 128 that include analyte measurements received from the continuous analyte monitoring system 104, which are stored in the user profile 118. In certain embodiments, the input 128 provided by the application 106 includes other data in addition to the analyte measurements received from the continuous analyte monitoring system 104. For example, the application 106 may obtain additional input 128 through manual user input, one or more other non-analyte sensors or devices, other applications executing on the display device 107, and so forth. Non-analyte sensors and devices include, but are not limited to, one or more of the following: insulin pumps, respiration sensors, sensors or devices provided by the display device 107 (e.g., accelerometers, cameras, global Positioning System (GPS), heart rate monitors, etc.) or other user accessories (e.g., smart watches) or any other sensor or device that provides relevant information about the user. The input 128 of the user profile 118 provided by the application 106 is described in more detail below with reference to FIG. 3.

The DAM 113 of the decision support engine 114 is configured to process the set of inputs 128 to determine one or more metrics 130. The metrics 130 discussed in more detail below with reference to fig. 3 may generally indicate, at least in some cases, a health or state of the user, such as one or more of a physiological state of the user, a trend associated with the health or state of the user, and the like. In some embodiments, the metrics 130 may then be used by the decision support engine 114 as input to provide guidance to the user. As shown, metrics 130 are also stored in user profile 118.

The user profile 118 also includes demographic information 120, disease progression information 122, and/or medication information 124. In certain embodiments, such information may be provided through user input or obtained from certain data stores (e.g., electronic Medical Records (EMR), etc.). In certain embodiments, the demographic information 120 may include one or more of a user's age, body Mass Index (BMI), race, gender, etc. In certain embodiments, the disease progression information 122 may include information about the user's disease, such as whether the user has been previously diagnosed with cirrhosis, liver fibrosis, NAFLD, NASH, hepatic ischemia reperfusion injury, primary cholangitis (PBC), primary Sclerosing Cholangitis (PSC), or whether the user has been previously diagnosed with a liver disease caused by a virus (such as hepatitis a, hepatitis b, or hepatitis c). In certain embodiments, the information about the user's disease may also include a duration since diagnosis, a degree of disease control, a degree of compliance with liver disease management therapy, predicted liver function, other types of diagnosis (e.g., heart disease, obesity) or health metrics (e.g., heart rate, exercise, stress, sleep, etc.), and the like.

In certain embodiments, the medication information 124 may include information regarding the amount, frequency, and type of medication taken by the user.

In certain embodiments, the drug information may include information regarding the ingestion of one or more drugs known to damage the liver (e.g., affect lactate clearance) and/or cause hepatotoxicity. The one or more drugs known to damage the liver and/or cause hepatotoxicity may include: antibiotics such as amoxicillin/clavulanate, clindamycin, erythromycin, nitrofurantoin, rifampin, sulfonamides, tetracyclines, trimethoprim/sulfamethoxazole, and drugs for the treatment of tuberculosis (isoniazid and pyrazinamide); anticonvulsants such as tarbamazepine, thenobarbital, phenytoin, and valproate; antidepressants such as bupropion, fluoxetine, mirtazapine, paroxetine, sertraline, trazodone, and tricyclic antidepressants such as amitriptyline; antifungal agents such as ketoconazole and terbinafine; antihypertensives (e.g., drugs for treating hypertension, or sometimes for treating kidney or heart diseases) such as captopril, enalapril, irbesartan, lisinopril, losartan, and verapamil; antipsychotics such as phenothiazines (e.g., chlorpromazine) and risperidone; cardiology drugs such as amiodarone and clopidogrel; hormonal modulators such as anabolic steroids, contraceptives (oral contraceptives) and estrogens; analgesics, such as acetaminophen and non-steroidal anti-inflammatory drugs (NSAIDs); and other drugs such as acarbose (e.g., for treating diabetes), allopurinol (e.g., for treating gout), antiretroviral therapy (ART) drugs (e.g., for treating Human Immunodeficiency Virus (HIV) infection), baclofen (e.g., a muscle relaxant), cyproheptadine (e.g., an antihistamine), azathioprine (e.g., for preventing organ transplant rejection), methotrexate (e.g., for treating cancer), omeprazole (e.g., for treating gastroesophageal reflux), PD-1/PD-L1 inhibitors (e.g., anticancer drugs), statins (e.g., for treating high cholesterol levels), and many types of chemotherapy, including immune checkpoint inhibitors.

In certain embodiments, the drug information may include information regarding the ingestion of one or more drugs known to improve liver function. The one or more drugs known to improve liver function may include ademetionine, atorvastatin, dehydro-emetine, entecavir, ganciclovir and pirenz, lamivudine, metadoxine, methionine, sofosbuvir, velpatavir and Fu Xirui, telbivudine, tenofovir, trientine, ursodeoxycholic acid, and the like.

In some embodiments, the user profile 118 is dynamic in that at least a portion of the information stored in the user profile 118 may be modified over time and/or new information may be added to the user profile 118 by the decision support engine 114 and/or the application 106. Thus, the information stored in the user profile 118 in the user database 110 provides an up-to-date repository of information related to the user.

In some embodiments, the user database 110 refers to a storage server operating in a public cloud or a private cloud. The user database 110 may be implemented as any type of data store, such as a relational database, a non-relational database, a key-value data store, a file system including a hierarchical file system, and the like. In some exemplary embodiments, the user database 110 is distributed. For example, user database 110 may include a distributed plurality of persistent storage devices. Further, the user database 110 may be replicated such that the storage devices are geographically dispersed.

The user database 110 includes user profiles 118 associated with a plurality of users that similarly interact with the applications 106 executing on the other users' display devices 107. The user profiles stored in the user database 110 are accessible not only by the application 106, but also by the decision support engine 114. The user profiles in the user database 110 may be accessed by the application 106 and the decision support engine 114 over one or more networks (not shown). As described above, the decision support engine 114, and more particularly the DAM 116 of the decision support engine 114, may retrieve the input 128 from the user database 110 and calculate a plurality of metrics 130, which may then be stored as application data 126 in the user profile 118.

In some embodiments, user profiles 118 stored in user database 110 may also be stored in history database 112. The user profile 118 stored in the historian database 112 may be a repository that each user of the application 106 provides up-to-date and historical information. Thus, the historian database 112 provides substantially all of the data related to each user of the application 106, with the data stored according to the associated timestamp. The time stamp associated with the information stored in the historian database 112 can identify, for example, when the information related to the user has been obtained and/or updated.

Further, the data stored in the historian database 112 may be maintained as time series data collected by users during a period of time during which such users use the continuous analyte monitoring system 104 and the application 106. For example, a user's analyte data that has been managed for five years of liver condition using the continuous analyte monitoring system 104 and application 106 may maintain time-series analyte data associated with the user over a period of five years.

Further, in certain embodiments, the historian database 112 may include data for one or more patients that are not users of the continuous analyte monitoring system 104 and/or the application 106. For example, the historian database 112 may include information (e.g., user profiles) related to one or more patients analyzed by, for example, a medical doctor (or other known method) and not previously diagnosed with liver disease, and information (e.g., user profiles) related to one or more patients analyzed by, for example, a medical doctor (or other known method) and previously diagnosed with liver disease. The data stored in the historian database 112 may be referred to herein as population data.

The data associated with each patient stored in the historian database 112 may provide time series data collected throughout the course of the patient. For example, the data may include information about the patient prior to being diagnosed with the liver disease and information associated with the patient throughout the course of the disease, including information related to each stage of progression and/or regression of the liver disease in the patient, as well as information related to other diseases, such as kidney disease or co-existence of similar diseases related to liver disease. Such information may indicate the patient's symptoms, the patient's physiological state, the patient's lactate level, the patient's glucose level, the patient's ketone level, the patient's potassium level, the patient's state/condition of one or more organs, the patient's habits (e.g., alcohol intake, activity level, food intake, etc.), prescribed medications, and the like throughout the course of the disease.

Although depicted as separate databases for conceptual clarity, in some embodiments, user database 110 and history database 112 may operate as a single database. That is, historical data and current data relating to users of continuous analyte monitoring system 104 and application 106, as well as historical data relating to patients who were not previously users of continuous analyte monitoring system 104 and application 106, may be stored in a single database. The single database may be a storage server operating in a public cloud or a private cloud.

As mentioned previously, the decision support system 100 is configured to diagnose, stage, treat, and risk evaluate liver disease of a user using a continuous analyte monitoring system 104 that includes at least a continuous lactate sensor. In certain embodiments, to enable such diagnosis and staging, the decision support engine 114 is configured to provide real-time and/or non-real-time liver disease decision support to the user and/or others, including but not limited to medical providers, family members of the user, caregivers of the user, researchers, artificial Intelligence (AI) engines, and/or other individuals, systems, and/or communities that support care or learning from data. In particular, the decision support engine 114 may be operable to collect information associated with a user in the user profile 118 stored in the user database 110 to perform an analysis thereon to determine the probability and/or severity of the user's liver disease, and to provide one or more treatment recommendations based at least in part on the determination. The decision support engine 114 may access the user profile 118 over one or more networks (not shown) to perform such analysis.

In some embodiments, decision support engine 114 may utilize one or more trained machine learning models capable of performing analysis on information that decision support engine 114 has collected/received from user profile 118. In the embodiment illustrated in fig. 1, decision support engine 114 may utilize a trained machine learning model provided by training server system 140. Although depicted as separate servers for conceptual clarity, in some embodiments the training server system 140 and decision support engine 114 may operate as a single server. That is, the model may be trained and used by a single server, or may be trained by one or more servers and deployed for use on one or more other servers. In some embodiments, the model may be trained on one or more Virtual Machines (VMs) running at least in part on one or more physical services in a relational database format and/or a non-relational database format.

The training server system 140 is configured to train a machine learning model using training data, which may include data (e.g., from a user profile) associated with one or more patients (e.g., users or non-users of the continuous analyte monitoring system 104 and/or the application 106) previously diagnosed with a different stage of liver disease and patients (e.g., healthy patients) not previously diagnosed with liver disease. The training data may be stored in the historian database 112 and may be accessed by the training server system 140 over one or more networks (not shown) to train the machine learning model.

Training data refers to a data set that has been characterized and labeled. For example, the data set may include a plurality of data records, each data record including information corresponding to a different user profile stored in the user database 110, wherein each data record is characterized and marked. In machine learning and pattern recognition, features are individually measurable attributes or features. Typically, features that best characterize the patterns in the data are selected to create a predictive machine learning model. Data tagging is the process of adding one or more meaningful and informative tags to provide context to data for machine learning model learning. As an illustrative example, each relevant characteristic of the user reflected in the corresponding data record may be a feature for training a machine learning model. Such characteristics may include age, gender, average change in lactate clearance from a first time stamp to a second time stamp (e.g., average delta), average change in liver disease diagnosis from a first time stamp to a subsequent time stamp (e.g., average delta), differences in the derivative of the linear system of measurement of lactate measurements at points of a particular time stamp and/or the derivative of the rate of change of slope used to determine the increase or decrease of value, etc. Further, the data record is marked with an indication regarding diagnosis of liver disease, a specified disease score, and/or an identified risk of liver disease, etc., associated with the patient of the user profile.

The training server system 140 then trains the model using the characterized and labeled training data. In particular, the features of each data record may be used as inputs to a machine learning model, and the generated outputs may be compared to the labels associated with the corresponding data record. The model may calculate the loss based on the difference between the generated output and the provided signature. This loss is then used to modify the internal parameters or weights of the model. By iteratively processing each data record corresponding to each historic patient, the model can be iteratively refined to generate accurate predictions of patient liver disease risk, presence, progression, improvement, and severity.

As shown in FIG. 1, the training server system 140 deploys these trained models to the decision support engine 114 for use during runtime. For example, the decision support engine 114 may obtain a user profile 118 associated with the user and stored in the user database 110, use information in the user profile 118 as input to the trained model, and output a prediction (e.g., shown as output 144 in fig. 1) indicative of the presence and/or severity of liver disease of the user. The output 144 generated by the decision support engine 114 may also provide one or more treatment recommendations based on the prediction. The output 144 may be provided to the user (e.g., via the application 106), to a caretaker of the user (e.g., parent, relative, guardian, teacher, nurse, etc.), to a doctor of the user, or to any other individual interested in the health of the user for the purpose of improving the health of the user (such as, in some cases, by performing recommended treatments).

In some embodiments, the output 144 generated by the decision support engine 114 may be stored in the user profile 118. The output 144 may indicate the current health of the user, the liver status of the user, and/or the current therapy recommended to the user. The output 144 stored in the user profile 118 may be continuously updated by the decision support engine 114. Thus, previous diagnoses, initially stored as output 144 in the user profile 118 in the user database 110 and then transferred to the history database 112, may provide an indication of the progress of the user's liver disease over time, as well as an indication of the effectiveness of different treatments recommended to the user to help stop disease progression.

In some embodiments, the training server system 140 may use the user's own historical data to train a personalized model for the user that provides decision support and insight around the user's liver disease. For example, historical data of a patient may be used as a baseline to indicate improvement or worsening of liver function in the patient. As an illustrative example, from 1 week ago; before 2 weeks; before 1 month; before 6 months; or patient data 1 year ago, may be used as a baseline that may be compared to current data of the patient to identify whether the liver function of the patient has improved or deteriorated. In certain embodiments, the model can also predict or infer a patient's liver function or future improvement/deterioration thereof based on the user's most recent data patterns (e.g., exercise data, food intake data, drug use data, etc.).

In some embodiments, the model may be trained to provide food, exercise, therapeutic interventions, drug types and dosages, and other types of decision support recommendations to help the user improve their liver function based on the user's historical data, including how different types of food and/or exercise have affected the user's liver function in the past. For example, where the model is trained to provide food and/or exercise recommendations, the model may be trained by the training server system 140 based at least in part on historical postprandial glucose and lactate measurements.

In general, it may be desirable to monitor lactate levels over time for a user suffering from liver disease as a measure of liver health to provide the user with positive or negative feedback regarding specific lifestyle choices (including exercise, diet choices, drug types, and dose recommendations).

In certain embodiments, where the model is trained to provide therapeutic intervention recommendations, the model may be trained to provide recommendations for a particular type of therapy to the user based on the severity of liver disease, historical lactate data (such as baseline lactate levels and lactate thresholds), and other analyte or non-analyte sensor data of the user. After the user has performed the treatment recommendation, the model may continue to monitor lactate data, as well as other analyte and non-analyte sensor data, to determine the effect of the recommended treatment on the user's liver health. Based on data collected after the treatment recommendation is administered, the model may measure liver health over time to provide positive or negative feedback to the user regarding the therapeutic intervention.

In certain embodiments, the model may be trained by the training server system 140 based on historical glucose and lactate to provide drug type and dose recommendations. For example, the model may be trained to provide recommendations for drug type and dosage based on the severity of liver disease, historical lactate data (such as baseline lactate levels and lactate thresholds) and other historical analyte or non-analyte sensor data of the user. In general, monitoring lactate levels of a user over time may prove an effect of the type or dosage of the drug on the liver health of the user. For example, lactate levels over time may prove that the user is sensitive to a particular type of statin and may recommend that the user use an alternative statin.

In certain embodiments, the model may be trained to predict potential causes of certain improvements or exacerbations in liver function in a patient. For example, the application 106 may display a user interface with a chart showing the patient's liver functionality or its score with trend lines, and for example retrospectively indicating how the functionality is compromised at certain points in time.

In certain embodiments that use a rule-based model to provide decision support, historical glucose and/or lactate measurements may be used to determine "healthy" and/or "unhealthy" thresholds or ranges of postprandial glucose and/or lactate levels. Thereafter, a health and/or unhealthy threshold or range of glucose and/or lactate may be used to inform the user whether a meal is healthy or unhealthy to the user based on real-time measurements of glucose and/or lactate.

Fig. 2 is a diagram 200 conceptually illustrating an example continuous analyte monitoring system 104 including an example continuous analyte sensor with sensor electronics, in accordance with certain aspects of the present disclosure. For example, according to certain aspects of the present disclosure, the system 104 may be configured to continuously monitor one or more analytes of a user for which a continuous analyte sensor is attached.

The continuous analyte monitoring system 104 in the illustrated embodiment includes a sensor electronics module 204 and one or more continuous analyte sensors 202 (individually referred to herein as continuous analyte sensors 202 and collectively referred to herein as continuous analyte sensors 202) associated with the sensor electronics module 204. The sensor electronics module 204 may communicate wirelessly (e.g., directly or indirectly) with one or more of the display devices 210, 220, 230, and 240. In certain embodiments, the sensor electronics module 204 may also be in wireless communication (e.g., directly or indirectly) with one or more medical devices such as a medical device 208 (individually referred to herein as a medical device 208 and collectively referred to herein as a medical device 208) and/or one or more other non-analyte sensors 206 (individually referred to herein as a non-analyte sensor 206 and collectively referred to herein as a non-analyte sensor 206).

In certain embodiments, continuous analyte sensor 202 may include a sensor for detecting and/or measuring an analyte. The continuous analyte sensor 202 may be a multi-analyte sensor configured to continuously measure two or more analytes or a single analyte sensor configured to continuously measure a single analyte, such as a non-invasive device, a subcutaneous device, a transcutaneous device, a transdermal device, and/or an intravascular device. In certain embodiments, the continuous analyte sensor 202 may be configured to continuously measure the analyte level of the user using one or more measurement techniques, such as enzymatic, chemical, physical, electrochemical, spectrophotometry, polarization, calorimetry, iontophoresis, radiation, immunochemistry, and the like. In certain aspects, the continuous analyte sensor 202 provides a data stream indicative of the concentration of one or more analytes in a user. The data stream may comprise the raw data signal and then be converted into a calibrated and/or filtered data stream for providing the estimated analyte value to the user.

In certain embodiments, the continuous analyte sensor 202 may be a multi-analyte sensor configured to continuously measure multiple analytes in a user's body. For example, in certain embodiments, the continuous multi-analyte sensor 202 may be a single sensor configured to measure lactate, glucose, ketone, and/or potassium in the user's body.

In certain embodiments, one or more multi-analyte sensors may be used in combination with one or more single analyte sensors. As an illustrative example, a multi-analyte sensor may be configured to continuously measure lactate and glucose, and in some cases may be used with an analyte sensor configured to measure only ketones. Information from each of the multi-analyte sensor and the single-analyte sensor may be combined to provide liver disease decision support using the methods described herein. In further embodiments, other non-contact and/or periodic or semi-continuous but time-limited measurements of physiological information may be integrated into the system, such as height, weight or other parameter estimation without physical contact by including weight scale information or non-contact heart rate monitoring from sensor pads under a user sitting in a chair or bed, by an infrared camera detecting the temperature and/or blood flow patterns of the user, and/or by a vision camera with machine vision.

In certain embodiments, the sensor electronics module 204 includes electronics circuitry associated with measuring and processing continuous analyte sensor data, including look-ahead algorithms associated with processing and calibrating sensor data. The sensor electronics module 204 may be physically connected to the continuous analyte sensor 202 and may be integral with (unreleasably attached to) or releasably attached to the continuous analyte sensor 202. The sensor electronics module 204 may include hardware, firmware, and/or software that enables the measurement of the level of an analyte via the continuous analyte sensor 202. For example, the sensor electronics module 204 may include a potentiostat, a power source for powering the sensor, other components for signal processing and data storage, and a telemetry module for transmitting data from the sensor electronics module to one or more display devices. The electronic device may be fixed to a Printed Circuit Board (PCB) or the like, and may take various forms. For example, the electronic device may take the form of an Integrated Circuit (IC), such as an Application Specific Integrated Circuit (ASIC), a microcontroller, and/or a processor.

The display devices 210, 220, 230, and/or 240 are configured to display displayable sensor data including analyte data that can be transmitted by the sensor electronics module 204. Each of the display devices 210, 220, 230, or 240 may include a display, such as touch screen displays 212, 222, 232, and/or 242, for displaying sensor data to a user and/or receiving input from a user. For example, a Graphical User Interface (GUI) may be presented to the user for such purposes. In some embodiments, the display device may include other types of user interfaces, such as a voice user interface, in place of or in addition to the touch screen display for transmitting sensor data to a user of the display device and/or receiving user input. Display devices 210, 220, 230, and 240 may be examples of display device 107 shown in fig. 1 for displaying sensor data to and/or receiving input from a user of fig. 1.

In some embodiments, one, some, or all of the display devices are configured to display or otherwise transmit the sensor information as it is when sensor data is transmitted from the sensor electronics module (e.g., in a data packet transmitted to the respective display device) without any additional look-ahead processing required for calibration and real-time display of the sensor data.

The plurality of display devices may include a custom display device specifically designed to display certain types of displayable sensor data associated with analyte data received from the sensor electronics module. In some embodiments, the plurality of display devices may be configured to provide an alarm/alert based on displayable sensor data. Display device 210 is an example of such a custom device. In some embodiments, one of the plurality of display devices is a smartphone, such as display device 220, that represents the mobile phone using a commercial Operating System (OS) and is configured to display a graphical representation of continuous sensor data (e.g., including current data and historical data). Other display devices may include other handheld devices, such as display device 230 representing a tablet, display device 240 representing a smart watch, medical device 208 (e.g., an insulin delivery device or a blood glucose meter), and/or a desktop or laptop computer.

Since different display devices provide different user interfaces, the content of the data packets (e.g., the amount, format, and/or type of data to be displayed, alarms, etc.) may be customized (e.g., programmed differently by the manufacturer and/or by the end user) for each particular display device. Thus, in certain embodiments, a plurality of different display devices may be in direct wireless communication with the sensor electronics module (e.g., such as the on-skin sensor electronics module 204 physically connected to the continuous analyte sensor 202) during a sensor session to enable a plurality of different types and/or levels of display and/or functionality associated with displayable sensor data.

As mentioned, the sensor electronics module 204 may be in communication with a medical device 208. In some exemplary embodiments of the present disclosure, the medical device 208 may be a passive device. For example, the medical device 208 may be an insulin pump for administering insulin to a user. For various reasons, it may be desirable for such insulin pumps to receive and track glucose, lactate and potassium values transmitted from the continuous analyte monitoring system 104, wherein the continuous analyte sensor 202 is configured to measure glucose, lactate and/or potassium.

Further, as mentioned, the sensor electronics module 204 may also be in communication with other non-analyte sensors 206. Non-analyte sensors 206 may include, but are not limited to, altimeter sensors, accelerometer sensors, temperature sensors, respiration rate sensors. The non-analyte sensor 206 may also include monitors such as heart rate monitors, blood pressure monitors, pulse oximeters, caloric intake and drug delivery devices. One or more of these non-analyte sensors 206 may provide data to the decision support engine 114, described further below. In some aspects, a user may manually provide some of the data for processing by training server system 140 and/or decision support engine 114 of fig. 1.

In certain embodiments, non-analyte sensors 206 may be combined in any other configuration, such as, for example, with one or more continuous analyte sensors 202. As an illustrative example, a non-analyte sensor, such as a temperature sensor, may be combined with the continuous lactate sensor 202 to form a lactate/temperature sensor for transmitting sensor data to the sensor electronics module 204 using a common communication circuit. As another illustrative example, a non-analyte sensor, such as a temperature sensor, may be combined with the multi-analyte sensor 202 configured to measure lactate and glucose to form a lactate/glucose/temperature sensor for transmitting sensor data to the sensor electronics module 204 using a common communication circuit.

In certain embodiments, a Wireless Access Point (WAP) may be used to couple one or more of the continuous analyte monitoring system 104, the plurality of display devices, the medical device 208, and/or the non-analyte sensor 206 to each other. For example, WAP 138 may provide Wi-Fi and/or cellular connectivity between these devices. Near Field Communication (NFC) and/or bluetooth may also be used between the devices depicted by diagram 200 of fig. 2.

FIG. 3 illustrates exemplary inputs and exemplary metrics calculated based on the inputs for use by the decision support system of FIG. 1, according to some embodiments disclosed herein. Specifically, FIG. 3 provides a more detailed illustration of the exemplary inputs and exemplary metrics introduced in FIG. 1.

FIG. 3 shows an exemplary input 128 on the left, an application 106 in the middle and DAM 116, and an index 130 on the right. In certain embodiments, each of the metrics 130 may correspond to one or more values, e.g., discrete values, ranges, or qualitative values (high/medium/low, stable/unstable, etc.). The application 106 obtains the input 128 through one or more channels (e.g., manual user input, sensors, other applications executing on the display device 107, etc.). As previously mentioned, in certain embodiments, the input 128 may be processed by the DAM 113 to output the metrics 130. The inputs 128 and metrics 130 may be used by the training server system 140 to train and deploy one or more machine learning models for use by the decision support engine 114 in diagnosing, staging and risk assessment of liver disease.

In certain embodiments, starting from input 128, the food intake information may include information about one or more of a meal, snack, and/or beverage, such as one or more of size, content (carbohydrates, fat, proteins, etc.), order of intake, and time of intake. In some embodiments, food intake information may be provided by manual input by a user, by an application configured to identify food types and amounts, and/or by scanning a bar code or menu. In various examples, the meal size may be manually entered as one or more of calories, quantity (e.g., "three biscuits"), menu items (e.g., "Royal cheese"), and/or food exchanges (1 fruit, 1 dairy product). In some examples, the meal information may be received through a convenient user interface provided by the application 106.

In certain embodiments, food intake information (type of food (e.g., liquid or solid, snack or meal, etc.), and/or composition of food (e.g., carbohydrate, fat, protein, etc.), may be automatically determined based on information provided by one or more sensors. Some example sensors may include body sound sensors (e.g., abdominal sounds may be used to detect the type of meal, e.g., liquid/solid food, snack/meal, etc.), radio frequency sensors, cameras, hyperspectral cameras, and/or analyte (e.g., insulin, glucose, lactate, etc.) sensors to determine the type and/or composition of the food.

In certain embodiments, the food intake entered by the user may be related to lactate intake by the user. Lactate for ingestion may include any natural or artificial designed food or beverage containing lactate, such as a lactate beverage, yogurt or whole milk. Lactate for ingestion may also include any natural or artificial designed food or beverage that is converted to lactate when absorbed by the body, such as a fructose beverage. As will be described in greater detail with respect to the metrics 130 calculated by the DAM 116, such lactate intake may be used by the DAM 116 to calculate the lactate clearance rate of the user.

In some embodiments, exercise information is also provided as input. The exercise information may be any information about an activity that requires physical activity of the user. For example, the range of exercise information may be information related to low intensity (e.g., walking) and high intensity (e.g., sprinting) physical activities. In some embodiments, the exercise information may be provided by accelerometer sensors on, for example, wearable devices such as watches, fitness trackers, and/or eye shields. In certain embodiments, exercise information may also be provided by manual user input and/or by alternative sensors and predictive algorithms that measure changes in heart rate (or other cardiac markers). When predicting that the user is exercising based on his/her sensor data, the user may be asked to confirm whether an exercise is being performed, the type of exercise, and/or the level of strenuous activity being used during an exercise during a particular period of time. This data may be used to train the system to learn the user's exercise pattern to reduce the need for confirmation problems over time and optimization of the training algorithm. Other analytes and sensor data may also be included in the training set, including analytes and other measurement elements described herein, including time elements, such as time and date.

In some embodiments, user statistics, such as one or more of age, height, weight, BMI, body composition (e.g., percent body fat), stature, body shape, or other information may also be provided as input. In some embodiments, the user statistics are provided through a user interface, through connection with an electronic source such as an electronic medical record, and/or from a measurement device. In some embodiments, the measurement device includes one or more wireless (e.g., bluetooth-enabled) weight scales and/or cameras that may, for example, communicate with the display device 107 to provide user data.

In certain embodiments, treatment/medication information is also provided as input. The medication information may include information regarding the type, dosage, and/or time of the one or more medications that the user is about to take. The treatment information may include information about different living habits recommended by the user's physician. For example, a user's doctor may recommend that the user drink a small amount of alcohol, exercise for a minimum of thirty minutes per day, or decrease 500 to 1,000 calories per day to improve liver health. In certain embodiments, the treatment/medication information may be provided by manual user input.

In certain embodiments, analyte sensor data may also be provided as input, such as by continuous analyte monitoring system 104. In certain embodiments, the analyte sensor data may include lactate data (e.g., lactate values of a user) measured by at least one lactate sensor (or multiple analyte sensors) in the continuous analyte monitoring system 104. In certain embodiments, the analyte sensor data may include glucose data measured by at least one glucose sensor (or multiple analyte sensors) in the continuous analyte monitoring system 104. In certain embodiments, the analyte sensor data may include ketone data measured by at least one ketone sensor (or multiple analyte sensors) in the continuous analyte monitoring system 104. In certain embodiments, the analyte sensor data may include potassium data measured by at least one potassium sensor (or multiple analyte sensors) in the continuous analyte monitoring system 104.

In certain embodiments, the input may also be received from one or more non-analyte sensors, such as non-analyte sensor 206 described with reference to fig. 2. Inputs from such non-analyte sensors 206 may include information related to a user's heart rate, respiration rate, oxygen saturation, or body temperature (e.g., to detect illness, physical activity, etc.). In certain embodiments, the electromagnetic sensor may also detect a low power Radio Frequency (RF) field emitted from an object or tool that is in contact with or in close proximity to the object, which may provide information about the user's activity or location.

In certain embodiments, the input received from the non-analyte sensor may include input related to insulin infusion by the user. In particular, input related to the user's insulin infusion may be received via a wireless connection on the smart pen, via user input, and/or from the insulin pump. The insulin infusion information may include one or more of insulin volume, infusion time, etc. Other parameters, such as insulin action time or duration of insulin action, may also be received as inputs.

In some embodiments, time may also be provided as an input, such as time of day, or time from a real-time clock. For example, in certain embodiments, the input analyte data may be time stamped to indicate to the user the date and time that the analyte measurement was made. However, in some embodiments, the time of day may not support determining whether the user is asleep or awake. When determining whether the user is asleep or awake, input received from a non-analyte sensor (e.g., an activity monitor), an analyte sensor (e.g., lactate or glucose increase), and/or user input may inform the user of the determination of whether to sleep or awake.

The user input in any of the above-mentioned inputs 128 may be through a user interface, such as the user interface of the display device 107 of fig. 1.

As described above, in certain embodiments, the DAM 116 determines or calculates the user's metrics 130 based on the inputs 128. An exemplary list of metrics 130 is shown in fig. 3.

In certain embodiments, lactate levels may be determined from sensor data (e.g., lactate measurements obtained from continuous lactate sensors of continuous analyte monitoring system 104). For example, lactate level refers to a time-stamped lactate measurement or a value that is continuously generated and stored over time.

In certain embodiments, the lactate production rate may be determined from sensor data (e.g., lactate measurements obtained from successive lactate sensors of the continuous analyte monitoring system 104). Specifically, during normal metabolism and exercise lactate is produced by lactate dehydrogenase from pyruvate (e.g., glucose is broken down into pyruvate). In certain embodiments, the lactate production rate may be determined by assessing an increase in lactate levels over a specified amount of time. In certain embodiments, the lactate production rate may be expressed as a percentage of the maximum heart rate (e.g., 85% of the maximum heart rate) or a percentage of the maximum oxygen uptake (e.g., 75%). In certain other embodiments, lactate production rate may be expressed as a function of accelerometer data. For example, the accelerometer data may indicate a user's stepping rate over time (e.g., an increasing stepping rate is shown by increasing the accelerometer data, and vice versa). Each of these step rates may be related to the lactate level of the user at a specified time. Thus, the stepping rate (e.g., accelerometer data) analyzed over time and its corresponding lactate level may provide information about the user's lactate production rate with respect to the accelerometer data. The DAM 116 may continuously, semi-continuously, or periodically measure the lactate production rate of the user over time and store the lactate production rate with a time stamp in the user profile 118. The lactate production rate may be time stamped to allow for identification of a decrease or increase in lactate production by the user over time.

In certain embodiments, lactate baselines may be determined from sensor data (e.g., lactate measurements obtained from successive lactate sensors of the continuous analyte monitoring system 104). Lactate baseline represents normal lactate levels during periods of time when the user's normal expected lactate production will not fluctuate. The baseline lactate of the user is typically expected to remain constant over time unless stimulated by an action such as ingestion of lactate or metabolism of food by the user or exercise by the user. Furthermore, the lactate baseline may be different for each user. In certain embodiments, the lactate baseline for the user may be determined by calculating an average lactate level over a specified amount of time that the lactate is expected not to fluctuate. For example, the baseline lactate of the user may be determined during periods when the user is sleeping, sitting in a chair, or during other periods when the user is sedentary and does not ingest food or medications that would reduce or increase lactate levels. In certain embodiments, the DAM 116 may calculate lactate baselines and time stamps continuously, semi-continuously, or periodically and store the corresponding information in the user profile 118. In certain embodiments, the DAM 116 may calculate the lactate baseline using lactate levels measured during periods when the user is sedentary, the user is not taking lactate, and there are no external conditions that would affect the lactate baseline. In certain other embodiments, the DAM 116 may use lactate levels measured over a period of time, wherein the user is engaged in exercise and/or intake of lactate and/or the presence of external conditions that would affect lactate baseline for at least a subset of the period of time. In such a case, in some examples, the DAM 116 may first identify which measured lactate values will be used to calculate baseline lactate by identifying lactate values that may have been affected by external events such as food intake, exercise, medications, or other disturbances that may interfere with the capture of lactate line measurements. The DAM 116 may exclude such measurements when calculating the lactate baseline for the user. In some other examples, the DAM 116 may calculate the lactate baseline by first determining a percentage of the number of lactate values measured during the time period, the percentage representing the lowest lactate value measured. The DAM 116 may then average this percentage to determine lactate line levels.

In certain embodiments, the lactate removal rate may be determined from sensor data (e.g., lactate measurements obtained from successive lactate sensors of the continuous analyte monitoring system 104). Specifically, the lactate clearance rate by the user indicates that lactate metabolism is greater than the rate of lactate production. Lactate clearance rate may be indicative of liver function (e.g., the slope of the lactate clearance curve may be indicative of liver function). In certain embodiments, the lactate clearance rate may be determined by calculating the slope between the initial lactate value (e.g., during a lactate level increase period) and a lactate line associated with the user. In certain embodiments, lactate clearance rate may be calculated over time until the lactate level of the user reaches a certain value relative to the lactate baseline of the user (e.g., 50% or 75% of the lactate baseline). In certain embodiments, lactate removal rate may be calculated over time until the user's lactate level reaches a certain value relative to the lactate peak level measured for the user at a previous time (e.g., the user's lactate level reaches 25%, 50%, and/or 75% of the user's lactate peak level).

Furthermore, monitoring lactate clearance rate after exercise or after meal may demonstrate improvement or progression of liver disease. Because liver disease generally progresses slowly, monitoring lactate clearance rate over time (e.g., after exercise or after meal) and comparing the current lactate clearance rate to past lactate clearance rate (after exercise or after meal) can help determine the progression of the disease over time. For example, if lactate clearance rate becomes significantly delayed or tends to deteriorate over time (e.g., lactate clearance rate is X at time Z and Q lactate clearance rate is X-Y at some later time), liver disease is progressing to a more severe state. In some embodiments, the decision support engine 114 will provide daily updates to the user as the lactate clearance rate is continuously monitored.

In certain embodiments, lactate clearance rate may be expressed as a function of lactate half-life of the user. In particular, there is a reverse relationship between lactate clearance rate and lactate half-life. In diseased liver, the slope of lactate clearance decreases with increasing calculated lactate half-life. As liver disease progresses, the slope of lactate clearance further decreases and the calculated lactate half-life further increases. Thus, lactate half-life may be indicative of the user's lactate clearance rate. The lactate removal rate calculated over time may be time stamped and stored in the user profile 118.

In some embodiments, a classifier may be used to determine whether the data corresponds to a disturbance in the rate of lactate increase or decrease followed by a sudden increase or decrease in lactate of the user in a different direction (e.g., an unexpected change in slope resulting in lactate clearance by the user). These conditions may include a user being sedentary and then starting exercise, briefly stopping exercise, and then starting exercise again. In this case, the rate of lactate production may not be as constant as the constant rate of clearance, as the increase in lactate produced during an exercise will be proportional to the time dependence of the length of the exercise. Similarly, this may occur where a patient taking lactate measurements on a fasted diet ingests an edible substance that increases the rate of lactate production in a consistent or inconsistent manner (e.g., depending on whether the ingested substance is homogeneous or heterogeneous). In heterogeneous materials, lactate level changes may be non-uniform because some foods digest at different rates based on differences between sugar, protein, and fat of each of these foods. The data classifier system will assist in determining and/or excluding relevant lactate production data (true rate increase) from lactate removal data (true rate decrease). Furthermore, the data classifier system will help determine areas of variably elevated lactate or otherwise rapidly changing data that is proportional to adjustments from lactate production from intake balance based on overlapping increasing and decreasing signals.

In certain embodiments, the lactate trend may be determined based on lactate levels over a certain period of time. In certain embodiments, lactate trend may be determined based on the rate of lactate production over a certain period of time. In certain embodiments, lactate trend may be determined based on calculated lactate removal rates over certain time periods.

In certain embodiments, the glucose level may be determined from sensor data (e.g., blood glucose measurements obtained from continuous lactate sensors of continuous analyte monitoring system 104). The elevated glucose level may be used in combination with a lactate level to determine if the user has eaten. To more accurately determine that the user has eaten, elevated glucose levels and lactate levels may also be combined with body sound sensors, as previously described, to confirm that the user has eaten. Furthermore, if glucose levels are combined with data from the activity monitor, high lactate levels and high glucose levels may indicate high intensity exercise rather than meal. Conversely, a combination of lower glucose levels with high lactate levels may prove that the high lactate levels are due to conditions other than meal (e.g., poor liver health, infection, or exercise).

In certain embodiments, the blood glucose trend may be determined based on glucose levels over a period of time.

In certain embodiments, the insulin sensitivity may be determined using historical data, real-time data, or a combination thereof, and the insulin sensitivity may be based on one or more inputs 128, such as one or more of food intake information, continuous analyte sensor data, non-analyte sensor data (e.g., insulin delivery information from an insulin device), and the like, for example. Insulin sensitivity refers to the degree of response of a user's cells to insulin. Improving the insulin sensitivity of a user may help reduce the insulin resistance of the user.

In certain embodiments, body-carried insulin may be determined using non-analyte sensor data input (e.g., insulin infusion information) and/or known or learned (e.g., from user data) insulin timing action curves that may account for basal metabolic rate (e.g., updating insulin to maintain bodily operation) and insulin usage driven by activity or food intake.

In certain embodiments, insulin and/or glucose sensor data (or derived values) may be used in combination with lactate sensor data to generate correction factors for certain activities (e.g., change in anaerobic metabolic rate versus glucose metabolic rate). This is especially important for insulin dependent diabetics where high levels of glycolysis lead to an excess of pyruvate which can be converted to glucose and thus typically has high levels of circulating insulin. Thus, glucose, insulin and/or lactate levels have complementary and inverse relationships that can inform the patient of the health status or status that can be used to diagnose liver health.

Additionally, diagnosing the stage of liver disease in multiple sessions across months may be a useful tool for sequentially determining the progression of the disease over time (e.g., when a user's actions or potential health conditions result in reversal or progression of liver disease). Such disease progression/reversal information may be shared in a display to a patient, caregiver, family member, health insurance company, or other stakeholder interested in the patient's disease state over a longer period of time. Additionally, by measuring the rate of change and absolute levels of insulin, glucose and lactate in a user, acute events in a liver disease user, such as liver decompensation and hypoglycemia, can be identified before severe acute symptoms become extremely debilitating.

In certain embodiments, the ketone level may be determined from sensor data (e.g., ketone measurements obtained from the continuous analyte monitoring system 104). In certain embodiments, ketone levels may be expressed as an indicator of whether a user is in a ketosis state. In particular, ketosis is a metabolic state in which a high concentration of ketone is present in the blood of a user.

In certain embodiments, the rate of ketone production may be determined from sensor data (e.g., ketone measurements obtained from successive ketone sensors of the continuous analyte monitoring system 104). Specifically, ketones (chemically referred to as ketone bodies) are byproducts of fatty acid decomposition. Glucose (e.g., blood glucose) is a preferred fuel source for many cells in the body; however, when the cell has limited access to glucose, the fat may be broken down into fuel, thereby producing ketone as a byproduct. In certain embodiments, the rate of ketone production may be determined by assessing the increase in ketone levels over a prescribed amount of time.

In certain embodiments, the potassium level may be determined from sensor data (e.g., potassium measurements obtained from the continuous analyte monitoring system 104).

In certain embodiments, the health and disease indicators may be determined, for example, based on one or more user inputs (e.g., pregnancy information or known disease information) from physiological sensors (e.g., temperature), activity sensors, or a combination thereof. In certain embodiments, based on the values of the health and disease indicators, for example, the state of the user may be defined as one or more of healthy, ill, resting, or tired.

In certain embodiments, a disease stage index, such as for liver disease, may be determined, for example, based on one or more of the user inputs or outputs provided by the decision support engine 114 shown in fig. 1. In certain embodiments, exemplary disease stages of liver disease may include inflammatory stages (e.g., early stages of swelling or inflammation of a user's liver), fibrotic stages (e.g., stages with signs of scar tissue in an inflamed liver), cirrhosis stages (e.g., stages with signs of severe scar tissue in an inflamed liver), end-stage liver disease (ESLD). In certain embodiments, exemplary disease stages may be represented as NASH scores, NAFLD fibrosis scores, child-Pugh scores, ESLD Model (MELD) scores, meta-analysis of viral hepatitis histological data (METAVIR) scores, and the like. Additionally, hepatocellular carcinoma may typically be present throughout the posterior stages of cirrhosis and/or ESLD.

In certain embodiments, the decision support engine 114 may use a combination of MELD scores (or other liver disease indicators/scores) and lactate data (or other analyte data) to predict liver disease progression and liver decompensation. Combining the MELD score with lactate data may be more efficient or predictive than MELD score alone or lactate data alone. For example, a MELD score alone may indicate liver disease, but the rate of lactate level increase outside of a meal or exercise is correlated with the severity of the condition (e.g., liver injury or other systemic or organ injury). The combination of a high rate of lactate change at rest with the MELD score of the patient may indicate a more severe liver dysfunction than the past rate of lactate change. In other cases, a high rate of lactate change at rest compared to the past rate of lactate change may be indicative of other health conditions.

In certain embodiments, the meal status indicator may indicate a status that the user is in regarding food intake. For example, the meal status may indicate whether the user is in one of a fasting state, a pre-meal state, a post-meal response state, or a steady state. In certain embodiments, the meal status may also indicate the nutrition of the body-borne (e.g., ingested meal, snack, or beverage) and may be determined, for example, from food intake information, meal time information, and/or digestibility information, which may be related to the type, quantity, and/or order of food (e.g., what food/beverage was consumed first). The meal status indicator may be determined based on information provided by user input or automatically based on information provided by one or more sensors (e.g., body sounds sensors as described above).

In certain embodiments, the eating habit indicators are based on the content and time of the user's meal. For example, in an example, if the eating habit index is in the range of 0 to 1, the better/healthier the user eats, the higher the value of his eating habit index will be, the closer to 1. Further, in an example, the more food intake by a user follows a particular schedule, the closer their eating habit index will be to 1. In certain embodiments, the eating habit indicators may indicate whether a user is consistently engaged in a ketogenic diet (e.g., low carbohydrate, proper amount of protein, and high fat diet) based on the diet, snack, or beverage ingested by the user over a period of time.

In certain embodiments, drug compliance is measured by one or more indicators that indicate the degree of compliance of a user with his or her medication regimen. In certain embodiments, the medication compliance index is calculated based on one or more of the time the user took the medication (e.g., whether the user took the correct type of medication on time or on schedule), the type of medication (e.g., whether the user took the correct type of medication), and the medication dose (e.g., whether the user took the correct dose). In certain embodiments, the user's drug compliance may be determined in clinical trials in which drug intake and the time of such drug intake are monitored.

In some embodiments, the activity level indicator may indicate an activity level of the user. In certain embodiments, the activity level indicator is determined, for example, based on input from an activity sensor or other physiological sensor (such as the non-analyte sensor 206). In certain embodiments, the activity level indicator may be calculated by the DAM 116 based on one or more of the inputs 128, such as one or more of exercise information, non-analyte sensor data (e.g., accelerometer data), time, user input, and the like. In some embodiments, the activity level may be expressed as a user's step rate. The activity level indicators may be time stamped so that they may be correlated to the user's lactate level at the same time.

In some embodiments, the exercise regimen index may indicate one or more of what type of activity the user is engaged in, the corresponding intensity of such activity, the frequency with which the user is engaged in such activity, and the like. In certain embodiments, the exercise regimen index may be calculated based on one or more of a non-analyte sensor data input (e.g., a non-analyte sensor data input from an accelerometer, heart rate monitor, respiratory rate sensor, etc.), a calendar input, a user input, etc.

In certain embodiments, the metabolic rate is an indicator that may indicate or include a basal metabolic rate (e.g., energy consumed at rest) and/or an active metabolism (e.g., energy consumed by an activity such as physical activity). In some examples, basal metabolic rate and active metabolism may be tracked as separate outcome indicators. In certain embodiments, the metabolic rate may be calculated by the DAM 113 based on one or more of the inputs 128 (such as one or more of exercise information, non-analyte sensor data, time, etc.).

In certain embodiments, the body temperature index may be calculated by the DAM 113 based on the input 128, and more specifically, based on non-analyte sensor data from the temperature sensor. In certain embodiments, the heart rate indicator may be calculated by the DAM 113 based on the input 128, and more specifically, based on non-analyte sensor data from the heart rate sensor. In certain embodiments, the respiratory index may be calculated by the DAM 113 based on the input 128, and more specifically, based on non-analyte sensor data from the respiratory rate sensor.

Exemplary methods and systems for diagnosing, staging, treating and risk assessing liver diseases using monitored analyte data

Fig. 4 is a flow chart illustrating an exemplary method 400 for providing decision support using a continuous analyte sensor including at least a continuous lactate sensor in accordance with certain exemplary aspects of the present disclosure. For example, the method 400 may be performed to provide decision support to a user using the continuous analyte monitoring system 104 including at least the continuous lactate sensor 202 as shown in fig. 1 and 2. The method 400 may be performed by the decision support system 100 to collect data including, for example, the analyte data, patient information, and non-analyte sensor data mentioned above to (1) automatically detect and classify abnormal liver conditions, (2) evaluate the presence and severity of liver disease, (3) risk stratification of patients to identify those at higher risk of liver disease, (4) identify risks associated with current liver disease diagnosis (e.g., risk of mortality, risk of liver cancer, etc.), (5) make patient-specific treatment decisions or recommendations for liver disease, (6) provide information about the effects of interventions (e.g., effects of patient lifestyle changes, effects of surgery, effects of patients taking new medications, etc.). In other words, the decision support systems presented herein may provide information to guide and help improve care for patients suffering from or at risk of liver disease. Method 400 is described below with reference to fig. 1 and 2 and its components.

At block 402, the method 400 begins by continuously monitoring a patient during a first period of time, such as one or more analytes of the user 102 shown in fig. 1, to obtain analyte data, the one or more analytes including at least lactate. In certain embodiments, block 402 may be performed by continuous analyte monitoring system 104 shown in fig. 1 and 2, and more specifically, continuous analyte sensor 202 shown in fig. 2. For example, the continuous analyte monitoring system 104 may include a continuous lactate sensor 202 configured to measure lactate levels of a user.

Lactate is the conjugate base of lactic acid. During normal metabolism and exercise lactate is produced by lactate dehydrogenase from pyruvate (e.g., glucose is broken down into pyruvate). Up to about 70% of lactate is metabolized by the liver. However, in very early liver diseases such as NAFLD, lactate metabolism is altered, which can lead to elevated lactate levels in the body. Furthermore, as liver disease progresses, lactate production rate further increases, lactate metabolism is impaired, and lactate half-life is prolonged. Such increased production, decreased clearance, or a combination of both may cause an increase in lactate. Thus, continuous monitoring of lactate may be required to continuously evaluate parameters such as lactate clearance rate (also indicative of lactate half-life), lactate levels, lactate production rate and lactate line for real-time diagnosis, staging, treatment and risk assessment of liver disease.

While the primary analyte for measurement described herein is lactate, in certain embodiments, other analytes are also contemplated. In particular, combining lactate measurements with additional analyte data may help inform the analysis further around diagnosing and staging liver disease. For example, monitoring additional types of analytes (such as glucose, ketone, and/or potassium) measured by continuous analyte monitoring system 104 may provide additional insight into diagnosis of liver disease and/or supplement information for determining optimal treatment for preventing disease progression (and in some cases, for disease regression).

Additional insight gained from the use of a combination of analytes, not just lactate, may improve the accuracy of liver disease diagnosis. For example, the probability of accurately diagnosing and/or staging liver disease may be a function of the amount of analyte measured for the user. More specifically, in some examples, the probability of accurately staging liver disease using lactate data (and other non-analyte data) alone may be less than the probability of accurately staging liver disease using lactate and glucose data (and other non-analyte data), which may also be less than the probability of accurately staging liver disease using lactate, glucose, ketone and potassium data (and other non-analyte data) for analysis.

In certain embodiments described herein, the combination of analytes for liver disease staging, e.g., as measured and collected by one (e.g., multiple analytes) or multiple sensors, includes lactate and at least one of glucose, ketone, or potassium; however, other combinations of analytes are also contemplated for diagnosing and staging liver disease.

For example, in certain embodiments, at block 402, the continuous analyte monitoring system 104 may continuously monitor the glucose level of the user during the first period of time. In some embodiments, glucose levels may be monitored in combination with or in lieu of other analytes (e.g., lactate, ketone, etc.). In such embodiments, the measured glucose concentration may be used to further inform the analysis for diagnosis and staging of liver disease. In particular, in some cases, glucose levels are an indicator of the likelihood of developing insulin resistance and/or type II diabetes (T2D), and these pathologies increase liver disease risk.

For example, during digestion, food containing carbohydrates is converted to glucose, which is then fed into the blood stream, resulting in an increase in blood glucose levels. This elevation of blood glucose typically signals the pancreas to produce insulin. Insulin mediates precise regulation of glucose metabolism and plasma concentration not only by promoting uptake of glucose by skeletal muscle, liver and adipose tissue, but also by inhibiting hepatic glucose production. Insulin plays an important role in lipid metabolism by binding to its receptor to promote fatty acid esterification, storage of fatty acids in lipid droplets, and also inhibit lipolysis. However, in the case of insulin resistance, cells in muscle, liver and tissue respond poorly to insulin and cannot use glucose in the blood to supply energy. In response, the pancreas is stimulated to increase insulin secretion, resulting in higher insulin levels in the liver and higher glucose concentrations in the blood. High concentrations of insulin can affect enzymes in the body, resulting in an increase in Free Fatty Acids (FFA) that can flow into the liver. An increase in FFA can lead to an excess of fat stored in hepatocytes and, in some cases, NAFLD. In other words, insulin resistant patients (typically found in T2D patients) may be at a higher risk of developing NAFLD. Furthermore, since T2D is a disease that may lead to worsening liver function, continuous glucose measurements may indicate the likelihood or status of T2D, which may predict liver disease and/or NAFLD. Glucose indicators that may be used include glucose base statistics (e.g., average median, variance, quartile spacing, etc.), time within a glucose range, glucose peak indicators (e.g., peak count, frequency, width, etc.), autocorrelation correlation indicators (e.g., correlation coefficients, hysteresis, etc.), and/or frequency domain indicators (e.g., peak frequency, width of frequency peaks, etc.). Thus, monitoring the glucose level of the user may help inform the user of an assessment of the likelihood of developing liver disease.

Furthermore, liver diseases and impaired liver function can lead to frequent hyperglycemia, especially after meals, as described above. As liver disease progresses, patients suffering from liver disease may also experience nocturnal hypoglycemia. Thus, frequent postprandial hyperglycemia and nocturnal hypoglycemia in combination with lactate measurements (e.g., higher baseline or resting lactate levels, postprandial lactate levels, and impaired lactate clearance rates) may provide a more complete prediction of improvement or progression of liver disease, and/or may inform the user of an assessment of the likelihood of developing liver disease.

In some embodiments, glucose and lactate levels are in complementary and inverse relationship, which can inform the patient of the health status or status that can be used to diagnose liver health. For example, in healthy users, lactate and glucose trends may be closely related (e.g., lactate and glucose levels may peak at similar times in response to events such as exercise or meal intake). Thus, divergent lactate and glucose trends may indicate renal or liver dysfunction in the user. For example, a larger and/or delayed lactate peak compared to a glucose peak may indicate that the user has a progressive liver disease or is metabolically poorly sound because the patient's body may not be able to effectively clear substrate and/or may not be able to effectively switch between lactate and glucose clearance.

In another example, at block 402, the continuous analyte monitoring system 104 may continuously monitor the ketone level of the user during the first period of time. In some embodiments, ketone levels may be monitored in combination with or in lieu of other analytes (e.g., glucose, lactate, etc.). In such embodiments, the measured ketone concentrations may be used to diagnose, stage, risk evaluate, and/or evaluate different treatments of liver disease. For example, in some cases, the ketone specific indicator may help recommend a specific diet to a user diagnosed with liver disease, and also provide real-time feedback regarding improvement of liver dysfunction after the recommended diet is administered.

In some cases where the user has been previously diagnosed with NAFLD, the measured ketone concentration of the user (e.g., continuously measured using the continuous ketone sensor 202) may inform the disease treatment recommendation. For example, in some cases, a ketogenic diet may be recommended to the user based on the user's ketone concentration. Ketogenic diets are basically aimed at forcing the body to use different types of fuel. Ketogenic diets rely on ketone bodies, i.e., acetoacetate, acetone, and beta-hydroxybutyrate (βhb), which are a fuel produced by the liver from stored fat, rather than on sugars (e.g., glucose) from carbohydrates such as grains, beans, vegetables, and fruits. Ketogenic diets can be used to bring the user's body into ketosis (e.g., metabolic states where high concentrations of ketone are present in the blood) to ultimately reverse the effects of NAFLD. For users diagnosed with fatty liver disease (e.g., NAFLD), eating more fat may seem counterintuitive; however, placing the user's body in ketosis triggers the body to begin burning body fat, as well as dietary fat. This may help improve the health of the user's liver, as the end user's body will begin to eradicate the problem causing fatty liver. In this case, the improvement of the liver may be directly related to an increase in the ketone concentration in the user, for example, due to the implementation of a ketogenic diet.

In yet another example, at block 402, the continuous analyte monitoring system 104 may continuously monitor a combination of two or more of lactate, glucose, and ketone of the user during a first period of time. In such embodiments, the measured concentration is used to further inform the analysis around diagnosing and staging liver disease.

In particular, in certain embodiments, as previously mentioned, insulin mediates precise regulation of glucose metabolism and plasma concentration by promoting uptake of glucose by skeletal muscle, liver and adipose tissue. Therefore, at low insulin levels, glucose uptake by skeletal muscle, liver and adipose tissue is limited. Such limited access to glucose (at least the liver) results in the liver decomposing fat as a fuel (e.g., ketogenic action). Given that ketones (e.g., ketone bodies) are byproducts of fatty acid decomposition, when the liver decomposes such acids, the concentration of ketones in the blood is expected to be high. However, in cases where the user is diagnosed with liver disease, the ability of the liver to produce ketone may be impaired (e.g., ketone-producing effects are impaired); thus, the ketone concentration may not be as high as expected.

In some cases, this may lead to users believing that they are in a healthy state, while in fact, users suffer from Diabetic Ketoacidosis (DKA) (e.g., the bloodstream is filled with extremely high levels of ketone). Given that the ketone concentration expected by users with DKA is reduced by the liver's ability to produce such ketones (e.g., masked by liver disease), users may not be aware that they have DKA. Thus, a combination of low insulin and high blood glucose concentrations with a low ketone concentration by the user may be a good indicator for informing the diagnosed liver injury. This indication, in combination with the continuously measured lactate concentration by the user, may help to increase the accuracy of predicting the presence and/or severity of liver disease in the user. Conversely, a combination of low blood glucose and high ketone concentration may indicate that the user is experiencing ketosis, which is typically caused by a ketogenic diet. Ketogenic diets can improve liver health over time and thus lead to a decrease in lactate levels in the user. Thus, a ketogenic diet may be recommended to users with liver disease to improve their liver health over time.

In another example, at block 402, the continuous analyte monitoring system 104 may continuously monitor the potassium level of the user during the first period of time. In some embodiments, potassium levels may be monitored in combination with or in lieu of other analytes (e.g., glucose, lactate, ketone, etc.). Measuring potassium can help inform liver disease diagnosis and staging, as reduced potassium excretion may be associated with fatty liver disease. Alternatively, increased potassium excretion may be associated with chronic liver failure, and increased lactate may provide additional confirmation of liver failure diagnosis, particularly in examples where the user also suffers from acute kidney injury. Acute kidney injury is a common complication in patients with liver failure or cirrhosis. Thus, increased lactate and potassium levels may indicate that the user has acute kidney injury, which may be associated with liver failure. However, if lactate levels increase while potassium levels remain stable, kidney damage may not be a factor or cause of liver failure. In other examples, uptake of potassium by the liver in response to insulin may be impaired, resulting in hyperkalemia. In this case, hyperkalemia may occur independently of acute kidney injury. As such, combining potassium measurements with lactate, ketone, and/or glucose measurements may allow for more accurate diagnosis of liver disease.

In certain embodiments, AI models (such as machine learning models and/or algorithms) may be used to provide real-time decision support for liver disease diagnosis and staging. In certain embodiments, such models may be configured to diagnose, stage, treat, and risk evaluate liver disease using inputs from one or more sensors measuring a variety of analyte data. Thus, in view of the interaction of such complications (e.g., as shown above with respect to examples of users having DKA and liver disease), parameters and/or thresholds of such algorithms and/or models may be varied based at least in part on the number of analytes measured for input to reflect the information obtained from each of the other analytes measured/morbidity associated with the additional analytes measured.

In addition to continuously monitoring the user's one or more analytes during the first period of time at block 402 to obtain analyte data, optionally, at block 404, the method 400 may further include monitoring other sensor data during the first period of time using one or more other non-analyte sensors or devices. In certain embodiments, block 404 may be performed by non-analyte sensor 206 and/or medical device 208 of fig. 2.

As previously mentioned, the non-analyte sensors and devices may include, but are not limited to, one or more of the following: insulin pumps, respiration sensors, heart rate monitors, accelerometers, sensors or devices provided by the display device 107 (e.g., accelerometers, cameras, global Positioning System (GPS), etc.), or any other sensor or device that provides relevant information about the user. Measurement data from each of these additional sensors may be used to calculate an index, such as index 130 shown in fig. 3. As further shown in fig. 3, the indicators 130 calculated from the non-analyte sensor or device data may include metabolic rate, body temperature, heart rate, respiration rate, and the like. In certain embodiments, as described in more detail below, the metrics 130 calculated from the non-analyte sensor or device data may be used to further inform the analysis for diagnosis and/or staging of liver disease.

In certain embodiments, one or more non-analyte sensors and/or devices may be worn by the user to help detect periods of increased physical activity of the user. Such non-analyte sensors and/or devices may include accelerometers, electrocardiogram (ECG) sensors, blood pressure sensors, heart rate monitors, and the like. In certain embodiments, the measured and collected data from periods of increased physical activity and periods of sedentary activity of the user may be used to analyze at least kidney, heart, skeletal muscle and/or liver function during each of these identified periods. In particular, during periods of sedentary activity of the user, up to about 70% of lactate is cleared by the liver, while kidney, heart and skeletal muscle also contribute. During periods of physical activity of the user, the amount of lactate cleared by the liver may be less than 70% (e.g., due to skeletal muscle and heart clearance of additional lactate). Thus, in certain embodiments, the measured and collected data from periods of increased physical activity and periods of sedentary activity of the user may be used to understand lactate clearance performed by the liver, kidneys, heart and/or muscles during each of these identified periods. As described in more detail below, understanding the percentage of lactate clearance performed by different organs of the body may help isolate lactate clearance performed by the liver alone to better understand liver function and any injury that may be present, thereby informing the liver diagnosis and staging techniques described herein.

At block 406, the method 400 continues with processing the analyte data from the first time period to determine at least one lactate removal rate. In certain implementations, block 406 may be performed by decision support engine 114. As mentioned, even in very early liver diseases such as NAFLD, metabolism of lactate by the liver is impaired and thus lactate has a longer half-life (compared to that of healthy individuals). Thus, lactate clearance rate (and lactate levels) may provide necessary information regarding liver health and/or liver disease stage. Note that while certain operations described herein with respect to method 400 involve calculating lactate clearance rates (e.g., block 406) and/or using lactate clearance rates to generate a disease prediction (e.g., block 414), instead of or in addition to lactate clearance rates, one or more other lactate source indicators (e.g., area under lactate curve, lactate baseline, lactate change rate, postprandial lactate, time above a specified lactate range (e.g., 2 mmol), time below a specified lactate range (e.g., 2 mmol), median lactate level, number of instances of lactate above or below a specified value, amount of time that lactate level is within a certain range (e.g., 0.5mmol to 1.5 mmol), average or median rate of change of lactate over a certain period of time (e.g., over a24 hour period), number of times lactate change rate (absolute value) is above a specified value, and/or information about these values when exercise or not exercise) may similarly be calculated and used to generate a disease prediction or to generate a treatment as discussed with respect to blocks 414 and 416. Note that the area under the lactate curve refers to the area between the lactate curve on the graph (e.g., a representation of the continuous lactate measurement depicted on the graph with respect to time) and the time axis, where time is measured on the X axis and lactate is measured on the Y axis.

Typically, the slope of lactate clearance is calculated by analyzing the change in lactate measurement over time from (1) peak to baseline lactate after exercise or (2) after lactate intake (e.g., which varies from user to user and within one user based on time of day (e.g., user's morning versus afternoon versus night lactate baseline value)), some value relative to baseline (e.g., 50% or 75% of baseline), or some value relative to peak (e.g., 25%, 50% or 75% of peak). The lactate removal rate calculated using the above method may correspond to an aggregation of lactate removal performed by the liver, kidney, heart and/or skeletal muscle.

Additionally, an increase in lactate levels over time (e.g., a change in peak over time and/or an increased rate of change in lactate) after (1) exercise or (2) ingestion of lactate (e.g., as part of a meal) may be indicative of liver dysfunction. For example, if the lactate peak or the rate of change of lactate increase over time after ingestion of lactate, the user may be experiencing liver health and/or liver function deterioration.

To classify abnormal liver conditions, it may be desirable to separate lactate clearance by the liver from calculated lactate clearance; however, liver lactate removal and isolation can present complex challenges. In particular, given that the kidneys, heart and liver of a user also play an important role in scavenging lactate in the body, liver lactate scavenging may be different for each user being analyzed and may also be different during different periods of physical activity and/or inactivity of each user.

Provided herein are techniques for separating lactate clearance performed by the liver. Specifically, in certain embodiments, the method 400 for determining at least one lactate removal rate comprises: at block 408, identifying at least one lactate increase period by the user during at least a first period of time; at block 410, a first lactate clearance rate of the patient after at least one lactate increase period is calculated; and at block 412, correcting the first lactate removal rate of the patient to isolate lactate removal by the liver of the patient. Blocks 408, 410, and 412 of fig. 4 may be better understood with reference to workflow 500 of fig. 5.

Fig. 5 is an exemplary workflow 500 for isolating liver lactate removal rate using at least a continuous lactate monitor in accordance with certain embodiments of the present disclosure.

The workflow 500 of fig. 5 may be performed by the decision support system 100 including the decision support engine 114. As shown in fig. 5, the workflow 500 begins at block 408 with the decision support engine 114 identifying at least one lactate increase period during at least a first period of time in which one or more of the user's analytes are continuously monitored to obtain analyte data. As an illustrative example, assuming the user is wearing a continuous analyte sensor 202 for continuous measurement of lactate over a period of time (e.g., a 24 hour period), the decision support engine 114 may identify a period of increasing lactate concentration by the user during the 24 hour period. For this example, it may be determined that the user experienced a peak lactate level (determined based on the user's continuously measured lactate) between 9 am to 10 am and 1 pm to 30 pm.

At block 410, the decision support engine 114 calculates a first lactate removal rate for the user after at least one lactate increase period. Using the above example, the decision support system 100 calculates a first lactate removal rate after the identified high lactate period during 9 a.m. to 10 a.m. of the user and another first lactate removal rate after the identified high lactate period during 1 a.m. to 1 a.m. of the user.

Specifically, at block 506, the decision support engine 114 determines a maximum lactate level by the user during at least one lactate increase period. At block 508, the decision support system 100 determines the amount of time it takes the user to decrease the maximum lactate level to a certain percentage of the user's baseline lactate level after at least one lactate increase period. In some cases, the baseline lactate level of the user may be the user's baseline lactate level immediately prior to the user's lactate level increasing. In some cases, the baseline lactate level of the user may be the user's baseline lactate level calculated as an average over a specified time frame. For example, the baseline lactate level of the user may be calculated as an average of the user's morning lactate level, the user's afternoon lactate level, the user's evening lactate level, etc. for one or more days. In certain embodiments, the baseline lactate level of the user may be a fasting baseline lactate level of the user. Although the exemplary embodiment of fig. 4 is shown at block 508, the decision support system 100 determines the amount of time it takes for the user's maximum lactate level to decrease to a certain percentage of the user's baseline lactate level, in certain other embodiments, the decision support system 100 determines the amount of time it takes for the user's maximum lactate level to decrease to a certain percentage of the user's maximum lactate level (e.g., 25%, 50%, and/or 75% of the maximum lactate level). In certain embodiments, one or more of these slopes may be analyzed and compared for analysis. At block 510, the decision support engine 114 calculates a first lactate removal rate for the user using the first lactate level determined at block 506 and the amount of time determined at block 508.

For example, at block 506, the decision support engine 114 determines the maximum lactate level of the user during the identified high lactate concentration periods of the entire 24 hour period (e.g., a first period between 9 am and 10 am and a second period between 1 pm and 30 minutes pm) by analyzing the lactate data collected during these periods. It may be assumed for this example that decision support engine 114 determines a maximum lactate level of 8mmol/L between 9 am and 10 am and a maximum lactate level of 5mmol/L between 1 pm and 30 minutes 1 pm.

At block 508, the decision support engine 114 determines the amount of time it takes for the lactate peak of each of the two identified periods to decrease to the user's baseline lactate level. As mentioned with reference to fig. 3, the baseline lactate level may be indicative of the normal lactate value of the user while the user is resting (e.g., sedentary). Assuming the user's baseline lactate level is 2mmol/L, the decision support system 100 may determine the amount of time it takes for the measured lactate level to reach 2mmol/L after 8mmol/L and 5mmol/L peak lactate concentration.

At block 510, the decision support engine 114 calculates a first lactate removal rate for the user using the 8mmol/L lactate peak concentration determined at block 506 and the amount of time determined at block 508. Further, for a second time period, the decision support engine 114 calculates a second lactate removal rate for the user using the 5mmol/L lactate peak concentration determined at block 506 and the amount of time determined at block 508. In other words, the decision support engine 114 calculates the slope of lactate clearance over time from each of the identified lactate peaks.

In other embodiments, the 8mmol/L lactate peak concentration determined at block 506 and the amount of time determined at block 508 may be used for the user to determine the first lactate purge for the first time period as a portion of the area under the first lactate curve. The area under the curve can be calculated using the lactate peak concentration and the time from lactate baseline before the lactate peak to lactate return to baseline after the lactate peak. Lactate clearance can be used to determine the rate at which lactate returns to baseline after the lactate peak, and thus can be used to calculate the area under the curve. The area under the lactate curve of the first period is compared with the area under the lactate curve of the second period. Further, for a second time period, the decision support engine 114 uses the 5mmol/L lactate peak concentration determined at block 506 and the amount of time determined at block 508 to calculate a second lactate curve area under the user. In other words, the decision support engine 114 may calculate the slope of the lactate area over time from each of the identified lactate peaks.

The first lactate removal rate calculated at block 504 may be indicative of aggregated lactate removed by the user's liver, kidneys, heart and/or skeletal muscle. To isolate lactate clearance performed by the user's liver, the decision support engine 114 corrects the user's first lactate clearance rate at 412 to isolate lactate clearance by the user's liver. Thus, embodiments described herein provide a technical solution to the above-described technical problems by correcting lactate clearance rates to isolate lactate clearance by the user's liver. For example, the decision support engine 114 performs the steps at blocks 512 through 524 of fig. 5 in order to correct the lactate clearance rate to accurately isolate the rate of lactate clearance by the liver and thus more accurately generate a liver disease prediction.

At block 512, the decision support engine 114 determines whether the identified at least one lactate increase period is caused by physical activity of the user. The user's lactic acid level increases when the user exercises to reduce systemic blood and oxygen flow, or when the user ingests lactate (e.g., yogurt or Cytomax). The percentage of lactate clearance performed by each of the user's liver, kidney, heart, and/or skeletal muscle may be different for each of a number of different situations. For example, in situations where the user has engaged in exercise (e.g., a higher level of physical activity) and begins a period of calm (e.g., by, for example, walking isothermal and exercise), the user's muscles may still actively reduce lactate. Alternatively, in situations where the user is sedentary and ingests a lactate beverage (e.g., milk), the user's muscles may not actively reduce lactate. Thus, in contrast to situations where the user is in a period of cold (e.g., where a given muscle performs a greater percentage of lactate clearance) after an increase in physical activity, in situations where the user is sedentary and ingests a lactate beverage, a greater percentage of the calculated lactate clearance may be cleared by the liver. In addition to liver and skeletal muscle, in each of these cases, the heart and/or kidneys may also perform a percentage of lactate clearance.

Thus, to distinguish liver lactate clearance from muscle lactate clearance, heart lactate clearance, and/or kidney lactate clearance, the decision support system 100 may analyze the non-analyte sensor data patterns of the user to identify periods of both physical activity and inactivity to determine liver lactate clearance. In particular, using a mapping of non-analyte sensor data patterns to lactate clearance decomposition (e.g., a percentage of lactate clearance performed by the liver, skeletal muscle, heart, and/or kidneys), the decision support engine 114 may be able to better separate liver lactate clearance from the first lactate clearance value calculated at block 512.

Such mappings may be predefined based on group data and/or user's own data. For each combination of these modes, the mapping may provide a mapping between non-analyte sensor data patterns (including accelerometer data patterns, respiration sensor data patterns, and/or heart rate monitor data patterns) and lactate removal decomposition.

For example, accelerometer data, heart rate data, and/or breathing data patterns exhibiting elevated values may indicate periods of increased physical activity of the user. For periods of increased physical activity, different accelerometer data, heart rate data, and/or breathing data patterns may be mapped to the percentage of lactate clearance (or lactate production) performed by each of the liver, heart, kidney, and skeletal muscle. Different activity types and/or different intensity levels may result in percentage changes of different non-analyte sensor data patterns.

Alternatively, accelerometer data, heart rate data, and/or breathing data patterns exhibiting lower values may indicate periods of minimal physical activity or periods of sedentary activity of the user. For periods of minimal physical activity, different accelerometer data, heart rate data, and/or breathing data patterns may be mapped to the percentage of lactate clearance performed by each of the liver, heart, kidney, and skeletal muscle. Different activity types and/or different levels of low physical activity may result in percentage changes in different non-analyte sensor data patterns.

In certain embodiments, urinary lactate levels may be used as input to the decision support engine 114 to inform such mapping to more accurately predict lactate clearance by the kidneys. Both sedentary and active data pattern mapping can be used to isolate lactate liver clearance for the user. In particular, based on the different modes of non-analyte sensor data, the decision support system 100 may determine whether the user is in an active state or a sedentary state during the lactate removal increase period.

At block 512, the decision support engine 114 determines whether the at least one lactate increase period is due to physical activity of the user. In some cases, the decision support engine 114 may make this determination based on the non-analyte sensor data patterns (e.g., including accelerometer data patterns, respiration sensor data patterns, and/or heart rate monitor data patterns) indicating that the user is active or inactive. In some other cases, the decision support engine 114 may make this determination based on input provided by the user through the application 106 (e.g., a record of exercise, a record of lactate intake, a record of lactate infusion, etc.). In the event that the decision support engine 114 determines that the at least one lactate increase period is not due to physical activity of the user, at block 512, the decision support engine 114 determines that during the identified period the lactate concentration increase of the user is due, at least in part, to lactate ingestion or lactate infusion.

In some cases, the user may be free to ingest lactate at his or her discretion, while in other cases, the user may be instructed to ingest lactate to increase the user's lactate level for measurement. Additionally, pyruvate, pyruvic acid and/or other substances may be ingested to produce lactate in their breakdown. As mentioned, lactate for ingestion may include any naturally or artificially designed food or beverage containing lactate or other molecules designed to stimulate lactate production, metabolism, clearance, ingestion, breakdown or release and/or substitution and/or derivatization of the metabolite into lactate in a measurable manner. Additionally, synthetic lactate molecules or molecular mimics (e.g., using radioactive or unnatural isotopes, enantiomers, quantum dot-labeled probes, and/or other molecular differentiation techniques) with enhanced diagnostic detection elements can be used to differentiate the clearance of synthetic lactate from naturally occurring lactate by direct measurement of lactate and/or breakdown products or indirect measurement of lactate and/or breakdown products by inference of another analyte.

In some other cases, the user may be infused with lactate, ingest lactate, or ingest lactate-producing meal (e.g., fructose) for measurement by the continuous analyte monitoring system 104 to better determine the user's lactate clearance rate. For example, in some cases, lactate infusion may be used as a control to determine lactate clearance by the user. The method may be a manual way of stimulating the user's organ by inputting more lactate into the body to directly know how much lactate was input (e.g., because the operator of the lactate infusion pump controls the infusion and pump) for clearance. For example, the process may involve infusing lactate into the user at a rate of 10mL/hr, 20mL/hr, or 30mL/hr until the lactate reaches a controlled amount and allows the user's organs to clear the infused lactate. In certain embodiments, lactate may be ingested in lieu of lactate infusion. Oral lactate intake experiences first pass liver metabolism and thus, after standardized drink or meal intake, peak levels of lactate may be one way to isolate the liver and evaluate its health, as long as the subject does not exercise during lactate intake. If lactate metabolism is impaired in the liver, patients suffering from liver disease may show a higher rate of lactate increase, longer peak lactate level duration, slower lactate clearance and/or higher than expected after ingestion of lactate meal/drink.

Continuing with the example provided above, based on user input or analysis of one or more data patterns from one or more non-analyte sensors, at block 512, the decision support engine 114 determines that during the identified first lactate level increase period (e.g., between 9 a.m. and 10 a.m.), a maximum lactate level of 8mmol/L is achieved during the period of physical activity of the user. Additionally, the decision support engine 114 determines that during the identified second lactate level increase period of the user (e.g., between 1 pm and 30 minutes afternoon), a maximum lactate level of 5mmol/L is achieved during periods of user inactivity (or no physical activity).

Because at block 512 the decision support engine 114 determines that the second lactate level increase period of the user (e.g., between 1 pm and 30 pm) is not due to physical activity, at block 514 the decision support engine 114 determines whether it is assumed that lactate in the user's body is cleared only by the liver.

In particular, in some embodiments, the decision support engine 114 may be configured to assume that the lactate removal rate calculated at block 504 is primarily indicative of liver lactate removal if it is determined that the user is sedentary (e.g., has no physical activity). In other words, the decision support engine 114 may assume that no other organ is performing a significant amount of clearance when sedentary; thus, no correction is needed to separate liver lactate clearance from the lactate clearance rate calculated at block 504. For example, the decision support engine 114 may assume that the user's muscles may not be actively producing lactate when the user ingests a lactate beverage; thus, no correction is needed to separate liver lactate clearance from the lactate clearance rate calculated at block 504. Thus, at block 516, lactate clearance by the user's liver is determined to be a first lactate clearance rate (e.g., calculated at block 410). As described in more detail below, the decision support engine 114 may use the first lactate purge as an indicator for predicting the presence and/or severity of liver disease of the user.

In certain other embodiments, in the event that the user is determined to be sedentary (e.g., physical activity), the decision support engine 114 may be configured to infer that the lactate clearance rate calculated at block 410 is indicative of lactate clearance performed by the liver and other organs. In other words, while the user is determined to be inactive during high lactate concentrations, the sedentary lactate clearance rate may not be indicative of 100% liver lactate clearance.

Thus, at block 518, the decision support engine 114 compares the first lactate removal rate calculated for the at least one lactate increase period (e.g., at block 410) to one or more lactate removal rates calculated for one or more sedentary behavioral periods of the user, wherein each of the one or more other lactate removal rates is indicative of an aggregation of lactate removal by at least one of the liver, kidney, muscle, and/or heart. In particular, the decision support engine 114 may compare the user's non-analyte sensor data pattern to a mapping of non-analyte sensor data pattern (e.g., exhibiting sedentary behavior) to a predetermined lactate clearance rate decomposition (e.g., percentage of lactate clearance performed by the liver, skeletal muscle, heart, and/or kidneys). Based on the comparison, the decision support engine 114 may identify the non-analyte sensor data pattern in the map that is most similar to the current non-analyte sensor data pattern of the user (e.g., representing sedentary activity). The identified non-analyte sensor data pattern maps to a predetermined lactate removal rate decomposition, which the decision support system 100 identifies as a lactate removal rate decomposition for the user.

As an illustrative example, where a user uses an accelerometer and a respiration monitor, non-analyte data collected for the user may include accelerometer data and respiration data. Accelerometer data collected for a user may represent a first pattern X and breathing data collected for the user may represent a second pattern Y. The decision support engine 114 may compare these two patterns to a mapping of other non-analyte sensor data patterns. The first non-analyte sensor data pattern may include an accelerometer data pattern a and a respiration data pattern B. It may have been previously determined that for this first non-analyte sensor data pattern, the liver cleared 70% of lactate in the body, while the kidneys and muscles cleared the remaining 30%. The second non-analyte sensor data pattern may include an accelerometer data pattern X and a respiration data pattern Y. It may have been previously determined that for this second non-analyte sensor data pattern, the liver cleared 60% and the kidneys and muscles cleared the remaining 40%. During the comparison, the decision support engine 114 may determine the accelerometer data pattern X and the respiration data pattern Y that are most similar to the second non-analyte sensor data pattern. Thus, the decision support engine 114 may determine that 60% of the cleared lactate is cleared by the liver based on a predetermined lactate clearance rate decomposition for the second non-analyte sensor data pattern.

At block 522, the decision support engine 114 determines a second lactate removal rate indicative of lactate removal by the liver alone based at least in part on the comparison performed at block 518. For example, where a similar non-analyte data pattern is located in the map, the decision support engine 114 may determine that the user's liver may only clear 70% of lactate in the user's body based on the user's non-analyte data. Accordingly, the decision support engine 114 may adjust the lactate clearance rate calculated at block 410 based at least in part on a determination that the liver may only contribute 70% of the calculated clearance. Thus, at block 524, the lactate removal rate by the user's liver is determined to be a second lactate removal rate (e.g., calculated at block 522). As described in more detail below, the decision support engine 114 may use the second lactate purge as an indicator for predicting the presence and/or severity of liver disease of the user.

Alternatively, returning to block 512, it has been determined that at least one lactate increase period is due to physical activity, and the decision support engine 114 then determines that during this identified period, the lactate increase concentration by the user is due, at least in part, to an activity increase. For example, because at block 512 the decision support engine 114 determines that the first lactate level increase period (e.g., between 9 a.m. and 10 a.m.) is due to physical activity, the decision support engine 114 may assume that the user is engaged in some physical activity or exercise during the time from 9 a.m. to 10 a.m.

In some cases, the user may be free to exercise at his or her discretion, while in other cases, the user may be instructed to engage in some form of exercise or physical activity (also referred to as exercise-induced lactate analysis). For example, one concept of measuring lactate levels is to have the user exercise with a certain intensity such that the user's lactate level increases to a certain level, e.g. between 4mmol/L and 10mmol/L, or e.g. reaches the user's lactate threshold. Once this level is reached, exercise may be stopped and lactate clearance may be measured. The lactate level may be maintained at a certain value, e.g. 5mmol/L, for a certain period of time (e.g. 5 to 10 minutes) before stopping the exercise.

Continuing with the example provided above, because at block 512 the decision support engine 114 determines that a maximum lactate level of 8mmol/L for the user is achieved during the period of physical activity, at block 520 the decision support engine 114 compares the mapping of the user's non-analyte sensor data pattern to a non-analyte sensor data pattern (e.g., exhibiting physical activity) to a predetermined lactate clearance rate decomposition (e.g., a percentage of lactate clearance performed by the liver, skeletal muscle, heart, and/or kidney). The decision support engine 114 may perform such comparisons to identify a non-analyte sensor data pattern that most closely resembles or correlates with the current non-analyte sensor data pattern of the user (e.g., representing physical activity). The identified non-analyte sensor data pattern maps to a predetermined lactate removal rate decomposition based on which the decision support engine 114 identifies the lactate removal rate of the user being performed by the user given the user's current activity level.

At block 522, the decision support engine 114 determines a second lactate removal rate indicative of lactate removal by the liver alone based at least in part on the comparison at block 520. For example, where similar non-analyte data patterns are located, the decision support engine 114 may determine that the user's liver may only clear 50% of lactate in the user's body based on the user's non-analyte data. Thus, the decision support engine 114 may adjust the lactate clearance rate calculated at block 504 based at least in part on a determination that the liver may only contribute 50% of the calculated clearance. Thus, at block 524, the lactate removal rate by the user's liver is determined to be a second lactate removal rate (e.g., calculated at block 522). As described in more detail below, the decision support engine 114 may use the second lactate purge as an indicator for predicting the presence and/or severity of liver disease of the user.

In certain embodiments, more than one lactate level increase period is identified at block 502 and analyzed to determine a plurality of liver lactate removal rates, each calculated (and in some cases corrected) liver lactate removal rate may be used independently as an input to diagnose and/or stage liver disease in a user. In certain other embodiments, the average liver lactate clearance rate may be determined based on one or more of the calculated liver lactate clearance rates, and the average calculated liver lactate clearance rate may be used independently as an input to diagnose and/or stage liver disease in a user.

Referring back to fig. 4, the method 400 continues at block 414, where the decision support system 100 generates a disease prediction using the analyte data associated with the one or more analytes and the at least one lactate removal rate (e.g., determined and, in some cases, corrected according to the workflow 500 of fig. 5). In certain embodiments, block 414 may be performed by decision support engine 114 shown in fig. 1.

The decision support engine 114 may use different methods for generating disease predictions. In particular, in certain embodiments, the decision support engine 114 may use a rule-based model to provide real-time decision support for liver disease diagnosis and staging. A rule-based model involves manipulating and/or analyzing data using a set of rules. These rules are sometimes referred to as "If statements" because they tend to follow the principle of "If X occurs, then Y occurs". In particular, the decision support engine 114 may apply rule statements (e.g., if the statement) to assess the presence and severity of liver disease of the user, perform liver disease risk stratification for the user, and/or identify risks (e.g., mortality risk, liver cancer risk, etc.) associated with the user's current liver disease diagnosis.

For example, the first rule may be "if the patient's liver lactate clearance rate falls between X and Y, the patient has liver disease stage 1 of a particular scoring system (or corresponds to a first METAVIR score)" and the second rule may be "if the liver lactate clearance falls between Y and Z, the patient has liver disease stage 2 of a particular scoring system (or corresponds to a second METAVIR score"). The determined liver lactate clearance (e.g., as determined at block 408) may be applied against the predefined rules to stage liver disease.

Such rules may be defined and maintained in a reference library by the decision support engine 114. For example, a reference library may maintain a range of liver lactate clearance rates, which may map to liver disease stages. In certain embodiments, such rules may be determined based on training server system 140 analyzing historical patient records of historian database 112.

In some cases, the reference library may become very refined. For example, other factors may be used in the reference library to create such "rules". Other factors may include gender, age, diet, medical history, family medical history, body Mass Index (BMI), and the like. Increasing granularity may provide a more accurate output. For example, including an age range in a rule-based approach (e.g., the approach used by the decision support engine 114) may help to inform of differences in lactate clearance rates, making liver disease prediction, staging, diagnosis, etc., by the decision support engine 114 more accurate. For example, the average liver lactate clearance rate for adolescents (e.g., 13 to 17 years old) may be different from the average liver lactate clearance rate for middle-aged adults (e.g., 30 to 50 years old); thus, in rule-based approaches, age may be an important analytical factor to better predict and stage liver disease in a user.

In certain embodiments, as an alternative to using a rule-based model, an AI model, such as a machine learning model, may be used to provide real-time decision support for liver disease diagnosis and staging. In some embodiments, the decision support engine 114 may deploy one or more of these machine learning models for performing diagnosis, staging, and risk stratification of liver disease for the user. Risk stratification may refer to the process of assigning health risk status to users and using the risk status assigned to users to guide and improve care.

In particular, the decision support engine 114 may obtain information from the user profile 118 stored in the user database 110 that is associated with the user, characterize the user information stored in the user profile 118 as one or more features, and use these features as inputs to such a model. Alternatively, the information provided by the user profile 118 may be characterized by another entity and the features provided to the decision support engine 114 for use as input to a machine learning model. In machine learning, a feature refers to a single measurable attribute or feature that provides useful information for analysis. In some embodiments, features associated with the user may be used as input to one or more of the models to assess the presence and severity of liver disease of the user. In some embodiments, features associated with the user may be used as input to one or more of the models to risk stratification the user to identify whether the user has a high or low risk of developing liver disease. In some embodiments, features associated with the user may be used as input to one or more of the models to identify a risk (e.g., risk of death, risk of liver cancer, etc.) associated with the user's current diagnosis of liver disease.

In some implementations, features associated with a user can be used as inputs to one or more of the models to perform any combination of the above-mentioned functions. Details associated with how one or more machine learning models are trained to provide real-time decision support for liver disease diagnosis and staging are further discussed with respect to fig. 6.

As mentioned, in certain embodiments, at block 414, the decision support engine 114 may use other analyte data other than lactate to generate a disease prediction for the user. Analyte data, including lactate and glucose data, lactate and ketone data, lactate and potassium data, or lactate, glucose, potassium, and ketone data (e.g., measurements from continuous analyte monitoring system 104) may be used as input to such machine learning models and/or rule-based models to predict the presence and severity of liver disease in a user.

The decision support engine 114 may use machine learning models and/or rule-based models to generate disease predictions based on continuous analysis of data (e.g., analyte data and, in some cases, non-analyte data) collected for a user over various time periods. Analysis of data collected for a user over various time periods may provide insight into whether the user's health and/or disease is improving or worsening. For example, a user previously diagnosed with liver disease using the models discussed herein may continue to be continuously monitored (e.g., continuously collected for the user) to determine whether the disease is worsening or improving, etc. For example, a comparison of the lactate clearance rate, lactate peak level, baseline lactate level, and/or lactate production rate (e.g., after ingestion of lactate) by the user over a plurality of months may indicate disease progression in the user.

In some cases, the method 400 continues at block 416 with the decision support engine 114 generating one or more treatment recommendations based at least in part on the disease prediction generated at block 414. In particular, the decision support engine 114 makes liver disease treatment decisions or recommendations for the user. Treatment recommendations may include lifestyle modifications and/or recommendations for one or more drugs prescribed, titrated, or avoided for the user. The decision support engine 114 may output such treatment recommendations to the user (e.g., via the application 106).

As an illustrative example, in some cases, the decision support engine 114 may determine that a user's liver disease is progressing and associate such progress with a drug previously prescribed for the user. For example, when acute hepatotoxicity from a particular drug is present, liver lactate clearance rate may be severely compromised. Thus, based on the user's input drug intake information (among other factors), the decision support engine 114 may determine that such progression of the disease is due to one or more drugs previously prescribed for the user. Thus, in certain embodiments, at block 416, the decision support engine 114 may recommend that the user stop taking the previously prescribed medication, and in some cases, recommend an alternative medication for the user to ingest. In certain other embodiments, at block 416, the decision support engine 114 may recommend that the user take a lower dose of the previously prescribed medication. In certain embodiments, the decision support engine 114 may recommend titration of a previously prescribed dose of the drug to determine an ideal dose for the user (e.g., while monitoring the user's liver health).

In certain embodiments, the machine learning model deployed by the decision support engine 114 includes one or more models trained by the training server system 140, as shown in fig. 1. Fig. 6 depicts in more detail a technique for training a machine learning model deployed by the decision support engine 114 for diagnosing, staging and risk stratification of liver disease of a patient (e.g., a user) in accordance with certain embodiments of the present disclosure.

Fig. 6 is a flow chart depicting a method 600 for training a machine learning model to provide predictions of liver disease diagnosis in accordance with certain embodiments of the present disclosure. In certain embodiments, the method 600 is used to train a model to evaluate the presence and/or severity of liver disease in a patient (e.g., the user shown in fig. 1).

The method 600 begins at block 602 with a training server system (such as the training server system 140 shown in fig. 1) retrieving data from a history database (such as the history database 112 shown in fig. 1). As described herein, the historian database 112 may provide a repository of up-to-date and historical information for continuous analyte monitoring systems and users who have connected mobile health applications (such as the continuous analyte monitoring system 104 and the user of the application 106 shown in fig. 1), and provide data for one or more patients who are not or were not users of the continuous analyte monitoring system 104 and/or the application 106. In certain embodiments, the historian database 112 may include one or more data sets of historic patients who did not have liver disease or who have liver disease at different stages.

At block 602, the training server system 140 retrieving data from the historian database 112 may include retrieving all or any subset of the information maintained by the historian database 112. For example, where the historian database 112 stores information for 100,000 patients (e.g., non-users and users of the continuous analyte monitoring system 104 and the application 106), the data retrieved by the training server system 140 to train one or more machine learning models may include information for all 100,000 patients or only a subset of the data for these patients, such as data associated with only 50,000 patients or data including only the last decade.

As an illustrative example, medical history records for baseline assessment can be aggregated in addition to being able to aggregate unidentifiable patients from a cloud-based repository through integration of fast medical interoperability resources (FHIR), web Application Programming Interfaces (APIs), health level 7 (HL 7), and/or other computer interface languages with a local deployment or cloud-based medical record database.

As an illustrative example, at block 602, the training server system 140 may retrieve information of 100,000 patients suffering from different stages of liver disease stored in the historian database 112 to train a model to predict a user's risk, presence, and/or severity of liver disease. Each of the 100,000 patients may have a corresponding data record (e.g., based on their corresponding user profile) stored in the historian database 112. Each user profile 118 may include information, such as the information discussed with reference to fig. 3.

The training server system 140 then uses the information in each of the records to train an artificial intelligence or ML model (referred to herein as an "ML model" for simplicity). Examples of the types of information contained in the patient's user profile are provided above. The information in each of the records may be characterized (e.g., manually or by training server system 140) to produce features that may be used as input features for training the ML model. For example, the patient record may include or be used to generate features related to the age of the patient, the sex of the patient, the occupation of the patient, the lactate clearance rate, the area under the lactate curve, the average change in lactate clearance of the patient from the first time stamp to the subsequent time stamp (e.g., the average delta), other lactate indicators described herein, the average change in liver disease diagnosis of the patient from the first time stamp to the subsequent time stamp (e.g., the average delta), and/or any other data point in the patient record (e.g., the input 128, the indicator 130, etc.). In various embodiments, the features used to train the machine learning model may vary.

In certain embodiments, each historic patient record retrieved from the historic record database 112 is also associated with a marker indicating whether the patient is healthy or experiencing a certain liver disease change, a previously determined diagnosis of liver disease and/or stage of liver disease for the patient, a previously specified Child-Pugh score, a MELD score and/or METAVIR score, a NAFLD score, a NASH score, a risk assessment, a treatment recommendation, and the like. What label record will depend on what the model is trained to predict.

At block 604, the method 600 continues with the training server system 140 training one or more machine learning models based on the features and labels associated with the historic patient records. In some embodiments, the training server trains by providing features as input to the model. The model may be a new model initialized with random weights and parameters, or may be a partially or fully pre-trained model (e.g., based on previous training rounds). Based on the input features, the model in training generates some outputs. In certain embodiments, the output may be indicative of whether the patient is healthy or experiencing a certain liver disease change, liver disease diagnosis and/or liver disease stage of the patient, child-Pugh score, MELD score, METAVIR score, NAFLD score, NASH score, risk assessment, treatment recommendation, or the like. Note that the output may be in the form of a likelihood, classification, and/or other type of output.

In certain embodiments, the training server system 140 compares the generated output to actual markers associated with the corresponding historical patient records to calculate the loss based on the difference between the actual results and the generated results. The loss is then used to refine one or more internal weights and parameters of the model (e.g., via back propagation) so that the model learns to more accurately predict the presence and/or severity of liver disease (or its recommended treatment).

The model may be trained using one of a variety of machine learning algorithms. For example, one of a supervised learning algorithm, a neural network algorithm, a deep learning algorithm, and the like may be used.

At block 606, the training server system 140 deploys the trained model to make predictions associated with liver disease during run-time. In some embodiments, this includes transmitting some indication (e.g., a weight vector) of the trained model that can be used to instantiate the model on another device. For example, the training server system 140 may transmit the weights of the trained models to the decision support engine 114. The model may then be used to evaluate the presence and/or severity of liver disease of the user in real-time using the application 106, provide treatment recommendations, and/or make other types of predictions as discussed above. In some embodiments, training server system 140 may continue training the model in an "online" manner by using the input features and labels associated with the new patient record.

In addition, a similar approach to training using historic patient records as shown in FIG. 6 may also be used to train models using patient-specific records to create more personalized models to make predictions associated with liver disease. For example, a model trained using historical patient records deployed for a particular user may be further retrained after deployment. For example, the model may be retrained after being deployed for a particular patient to create a more personalized model for that patient. A more personalized model may be able to more accurately make liver disease-related predictions for the patient based on the patient's own data (as opposed to just historical patient record data), including the patient's own inputs 128 and metrics 130.

Fig. 7 is a block diagram depicting a computing device 700 configured to execute a decision support engine (e.g., decision support engine 114) in accordance with certain embodiments disclosed herein. Although depicted as a single physical device, in embodiments computing device 700 may be implemented using a virtual device and/or across multiple devices (such as in a cloud environment). As shown, computing device 700 includes a processor 705, memory 710, storage 715, a network interface 725, and one or more I/O interfaces 720. In the illustrated embodiment, processor 705 retrieves and executes programming instructions stored in memory 710 and stores and retrieves application data residing in storage device 715. Processor 705 generally represents a single CPU and/or GPU, multiple CPUs and/or GPUs, a single CPU and/or GPU with multiple processing cores, and the like. Memory 710 is typically included to represent random access memory. The storage devices 715 may be any combination of disk drives, flash-based storage devices, etc., and may include fixed and/or removable storage devices such as fixed disk drives, removable memory cards, cache memory, optical storage devices, network Attached Storage (NAS), or a Storage Area Network (SAN).

In some embodiments, input and output (I/O) devices 735 (such as keyboards, monitors, etc.) can be coupled via I/O interface 720. Further, via network interface 725, computing device 700 may be communicatively coupled with one or more other devices and components, such as user database 710. In some implementations, the computing device 700 is communicatively coupled with other devices via a network, which may include the internet, a local network, and the like. The network may include wired connections, wireless connections, or a combination of wired and wireless connections. As shown, processor 705, memory 710, storage 715, network interface 725, and I/O interface 720 are communicatively coupled by one or more interconnects 730. In some implementations, the computing device 700 represents the mobile device 107 associated with the user. In some embodiments, as discussed above, the mobile device 107 may include a user's notebook computer, smart phone, or the like. In another embodiment, computing device 700 is a server executing in a cloud environment.

In the illustrated embodiment, the storage device 715 includes the user profile 118. The memory 710 includes a decision support engine 114 that itself includes a DAM 116. The decision support engine 114 is executed by the computing device 700 to perform operations 402 through 416 of the method 400 in fig. 4.

As described above, the continuous analyte monitoring system 104 described with respect to fig. 1 may be a multi-analyte sensor system that includes multiple analyte sensors. Fig. 8A-12 depict an exemplary multi-analyte sensor for measuring multiple analytes.

As used herein, the phrases "analyte measurement device," "analyte monitoring device," "analyte sensing device," and/or "multi-analyte sensor device" are broad phrases and will give one of ordinary and customary meaning (and are not limited to special or customized meanings) to them, and refer to, but are not limited to, devices and/or systems responsible for detecting or transducing signals associated with a particular analyte or combination of analytes. For example, these phrases may refer, but are not limited to, an instrument responsible for detecting a particular analyte or combination of analytes. In one example, the instrument includes a sensor coupled to circuitry disposed within the housing and configured to process a signal associated with the analyte concentration into information. In one example, such devices and/or systems can use a biological recognition element in combination with a transduction (detection) element to provide specific quantitative, semi-quantitative, qualitative, and/or semi-qualitative analysis information.

As used herein, the terms "biosensor" and/or "sensor" are broad terms and will give one of ordinary and customary meaning to them (and are not limited to special or customized meanings) and refer to (but are not limited to) an analyte measurement device, an analyte monitoring device, an analyte sensing device, and/or a portion of a multi-analyte sensor device that is responsible for detecting or transducing a signal associated with a particular analyte or combination of analytes. In one example, a biosensor or sensor generally includes a body, a working electrode, a reference electrode, and/or a counter electrode coupled to the body and forming a surface configured to provide a signal during an electrochemical reaction. One or more membranes may be secured to the body and cover the electrochemical reaction surface. In one example, such biosensors and/or sensors can use a biological recognition element in combination with a transduction (detection) element to provide a specific quantitative, semi-quantitative, qualitative, semi-qualitative analysis signal.

As used herein, the phrases "sensing moiety," "sensing membrane," and/or "sensing mechanism" are broad phrases and will give one of ordinary and customary meaning (and are not limited to special or customized meanings) to them, and refer to (but are not limited to) a biosensor and/or a portion of a sensor that is responsible for detecting or transducing a signal associated with a particular analyte or combination of analytes. In one example, the sensing portion, sensing membrane, and/or sensing mechanism generally include an electrode configured to provide a signal during an electrochemical reaction with one or more membranes covering the electrochemical reaction surface. In one example, such sensing portions, sensing membranes, and/or sensing mechanisms can use a biological recognition element in combination with a transduction (detection) element to provide a particular quantitative, semi-quantitative, qualitative, semi-qualitative analytical signal.

The phrases "biological interface film" and "biological interface layer" as used interchangeably herein are broad phrases and will give one of ordinary skill in the art their ordinary and customary meaning (and are not limited to special or customized meanings) and refer to, but are not limited to, a permeable film (which may include multiple domains) or layer that acts as a biological protective interface between the recipient tissue and the implantable device. The terms "biological interface" and "bioprotective" are used interchangeably herein.

As used herein, the term "cofactor" is a broad term and will give the person of ordinary and customary meaning to those skilled in the art (and is not limited to a particular or customized meaning) and refers to, but is not limited to, one or more substances whose presence contributes to or is necessary for the analyte-related activity of an enzyme. Analyte-related activity may include, but is not limited to, any one of binding, electron transfer, and chemical conversion, or a combination thereof. Cofactors include coenzymes, non-protein compounds, metal ions and/or metal organic complexes. The coenzyme includes prosthetic groups and cosubstrates.

As used herein, the term "continuous" is a broad term and will give one of ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning) and refers to, but is not limited to, an uninterrupted or continuous portion, domain, coating or layer.

As used herein, the phrases "continuous analyte sensing" and "continuous multi-analyte sensing" are broad terms and will be given their ordinary and customary meaning (and are not limited to special or customized meanings) to one of ordinary skill in the art and refer to (but are not limited to) periods in which monitoring of analyte concentration is performed continuously, and/or intermittently (but periodically) (e.g., about every second or less to about once a week or more). In further examples, monitoring of the analyte concentration is performed about every 2 seconds, 3 seconds, 5 seconds, 7 seconds, 10 seconds, 15 seconds, 20 seconds, 25 seconds, 30 seconds, 35 seconds, 40 seconds, 45 seconds, 50 seconds, 55 seconds, or 60 seconds to about 1.25 minutes, 1.50 minutes, 1.75 minutes, 2.00 minutes, 2.25 minutes, 2.50 minutes, 2.75 minutes, 3.00 minutes, 3.25 minutes, 3.50 minutes, 3.75 minutes, 4.00 minutes, 4.25 minutes, 4.50 minutes, 4.75 minutes, 5.00 minutes, 5.25 minutes, 5.50 minutes, 5.75 minutes, 6.00 minutes, 6.25 minutes, 6.50 minutes, 6.75 minutes, 7.00 minutes, 7.25 minutes, 7.50 minutes, 7.75 minutes, 8.00 minutes, 8.25 minutes, 8.75 minutes, 9.00 minutes, 9.25 minutes, 9.50 minutes, or 9.75 minutes. In further examples, monitoring of the analyte concentration is performed about once every 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, or 8 hours for about 10 minutes, 20 minutes, 30 minutes, 40 minutes, or 50 minutes. In further examples, monitoring of the analyte concentration is performed about every 8 hours to about every 12 hours, 16 hours, 20 hours, or 24 hours. In further examples, monitoring of the analyte concentration is performed about once per day to about every 1.5, 2,3, 4, 5, 6, or 7 days. In further examples, monitoring of analyte concentration is performed about once every week to about every 1.5 weeks, 2 weeks, 3 weeks, or more.

As used herein, the term "coaxial" should be construed broadly to include sensor architectures having elements aligned along a shared axis about a core, which may be configured to have a circular, elliptical, triangular, polygonal, or other cross-section, such elements may include electrodes, insulating layers, or other elements that may be positioned circumferentially about the core layer, such as core electrodes or core polymer wires.

As used herein, the term "coupled" is a broad term and will give one of ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning) and refers to, but is not limited to, two or more system elements or components configured to be at least one of electronically attached, mechanically attached, thermally attached, operatively attached, chemically attached, or otherwise attached. For example, an element is "coupled" if it is covalently, communicatively, electrostatically, thermally, mechanically, magnetically linked, or ionically associated with another element or is physically trapped, adsorbed, or absorbed by the other element. Similarly, the phrases "operatively connected," "operatively linked," and "operatively coupled," as used herein, may refer to one or more components being coupled to another component in a manner that facilitates transmission of at least one signal between the components. In some examples, the components are part of the same structure and/or are integrated with each other, such as being covalently, electrostatically, mechanically, thermally, magnetically, ionically associated, or physically trapped or absorbed (i.e., "directly coupled", such as without intervening elements). In other examples, the components are connected via a remote device. For example, one or more electrodes may be used to detect an analyte in a sample and convert this information into a signal; the signal may then be transmitted to a circuit. In this example, the electrodes are "operably linked" to the electronic circuitry. The phrase "removably coupled" as used herein may refer to two or more system elements or components being configured or configured to have been electronically, mechanically, thermally, operatively, chemically, or otherwise attached and separated without damaging any of the coupled elements or components. As used herein, the phrase "permanently coupled" may refer to two or more system elements or components that are configured or have been electrically, mechanically, thermally, operatively, chemically, or otherwise attached, but cannot be decoupled without damaging at least one of the coupled elements or components.

As used herein, the term "discontinuous" is a broad term and will give one of ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning) and refers to, but is not limited to, discrete, intermittent or separate parts, layers, coatings or domains.

As used herein, the term "distal" is a broad term and will give one of ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning) and refers to, but is not limited to, a region that is relatively distant from a point of reference, such as a starting point or attachment point.

As used herein, the term "domain" is a broad term and will give the person of ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a particular or customized meaning) and refers to, but is not limited to, a region of a membrane system that may be a layer, a uniform or non-uniform gradient (e.g., anisotropic region of a membrane), or a portion of a membrane capable of sensing one, two, or more analytes. The domains discussed herein may be formed as a single layer, two or more layers, a bilayer pair, or a combination thereof.

As used herein, the term "electrochemically reactive surface" is a broad term and will give one of ordinary and customary meaning to those skilled in the art (and is not limited to a particular or customized meaning) and refers to, but is not limited to, the surface of an electrode that is electrochemically reactive. In one example, the reaction is a faraday reaction and results in a charge transfer between the surface and its environment. In one example, hydrogen peroxide generated by an enzymatic reaction of an analyte oxidized on a surface results in a measurable electron flow. For example, in the detection of glucose, glucose oxidase produces hydrogen peroxide (H ₂O₂) as a byproduct. H ₂O₂ reacts with the surface of the working electrode to produce two protons (2H ⁺), two electrons (2 e ^-) and one oxygen molecule (O ₂), thereby producing a detected electron flow. In the counter electrode, a reducible substance (e.g., O ₂) is reduced at the electrode surface to balance the current generated by the working electrode.

As used herein, the term "electrolysis" is a broad term and will give one of ordinary and customary meaning to those skilled in the art (and is not limited to a particular or customized meaning) and refers to, but is not limited to, the direct or indirect electrooxidation or electroreduction (collectively "redox") of a compound by one or more enzymes, cofactors, or mediators.

As used herein, the terms "indwelling," "built-in," "implanted," or "implantable" are broad terms and will give one of ordinary skill in the art their ordinary and customary meaning (and are not limited to special or customized meanings) and refer to, but are not limited to, an object that includes a sensor that is inserted or configured to be inserted in the following manner: subcutaneous insertion (i.e., in the fat layer between the skin and muscle), intradermal insertion (i.e., penetrating the stratum corneum and being positioned within the epidermis or dermis layer of the skin), or transdermal insertion (i.e., penetrating, entering, or passing through intact skin), which can result in the sensor having an in vivo portion and an in vitro portion. The term "indwelling" also encompasses objects configured for subcutaneous, intradermal, or percutaneous insertion, whether or not they have been so inserted.

As used herein, the terms "interferents" and "interfering substances" are broad terms and will give one of ordinary skill in the art their ordinary and customary meaning (and are not limited to special or customized meanings) and refer to, but are not limited to, effects and/or substances that interfere with the measurement of an analyte of interest in a sensor to produce a signal that is inaccurately indicative of the analyte measurement. In one example of an electrochemical sensor, the interfering substance is a compound that produces a non-analyte specific signal due to a reaction on an electrochemically active surface.

As used herein, the term "in vivo" is a broad term and will give one of ordinary and customary meaning to those skilled in the art (and is not limited to a particular or customized meaning) and includes, but is not limited to, portions of a device (e.g., a sensor) adapted to be inserted into and/or present within a subject's living body.

As used herein, the term "ex vivo" is a broad term and will give one of ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning) and includes, but is not limited to, portions of a device (e.g., a sensor) adapted to remain and/or reside outside of a subject's living body.

As used herein, the terms and phrases "mediator" and "redox mediator" are broad terms and phrases and will give one of ordinary and customary meaning to them (and are not limited to special or customized meanings) and refer to (but are not limited to) any compound or collection of compounds capable of direct or indirect electron transfer between an analyte, analyte precursor, analyte surrogate, analyte reducing or analyte oxidase or cofactor and an electrode surface maintained at a potential. In one example, the mediator accepts electrons from or transfers electrons to one or more enzymes or cofactors, and/or exchanges electrons with the sensor system electrode. In one example, the mediator is a transition metal coordinated organic molecule capable of reversible oxidation and reduction reactions. In other examples, the mediator may be an organic molecule or metal capable of reversible oxidation and reduction reactions.

As used herein, the term "film" is a broad term and will give one of ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning) and refers to, but is not limited to, a structure configured to perform the following functions, including, but not limited to: the method includes protecting the exposed electrode surface from biological environmental effects, diffusion resistance (limitation) of the analyte, acting as a matrix for a catalyst (e.g., one or more enzymes) that enables enzymatic reactions to proceed, limiting or blocking interfering substances, providing hydrophilicity at the electrochemically reactive surface of the sensor interface, acting as an interface between the recipient tissue and the implantable device, modulating the recipient tissue response via drug (or other substance) release, and combinations thereof. As used herein, the terms "membrane" and "matrix" are intended to be used interchangeably.

As used herein, the phrase "membrane system" is a broad phrase and will give the person of ordinary and customary meaning to those skilled in the art (and is not limited to special or customized meanings) and refers to (but is not limited to) a permeable or semi-permeable membrane that may be composed of two or more domains, two or more layers or two or more layers within a domain and is typically composed of a material of a thickness of a few microns or more, which is permeable to oxygen and optionally permeable to, for example, glucose or another analyte. In one example, the membrane system includes an enzyme that enables an analyte reaction to occur whereby the concentration of the analyte can be measured.

As used herein, the term "planar" will be interpreted broadly to describe a sensor architecture having a substrate including at least a first surface and an opposing second surface, and for example including a plurality of elements disposed on one or more surfaces or edges of the substrate. The plurality of elements may include conductive or insulating layers or elements configured to operate as a circuit. The plurality of elements may or may not be electrically or otherwise coupled. In one example, the plane includes one or more edges separating the opposing surfaces.

As used herein, the term "proximal" is a broad term and will give one of ordinary skill in the art its ordinary and customary meaning (and is not limited to a special or customized meaning) and refers to, but is not limited to, the spatial relationship between the various elements as compared to a specific reference point. For example, some examples of devices include a membrane system having a biological interface layer and an enzyme domain or layer. If the sensor is considered a reference point and the enzyme domain is positioned closer to the sensor than the biological interface layer, the enzyme domain is closer to the sensor than the biological interface layer.

As used herein, the phrases "sensing moiety," "sensing membrane," and/or "sensing mechanism" are broad phrases and will give one of ordinary and customary meaning (and are not limited to special or customized meanings) to them, and refer to (but are not limited to) a biosensor and/or a portion of a sensor that is responsible for detecting or transducing a signal associated with a particular analyte or combination of analytes. In one example, the sensing portion, sensing membrane, and/or sensing mechanism generally include an electrode configured to provide a signal during an electrochemical reaction with one or more membranes covering the electrochemical reaction surface. In one example, such sensing moieties, sensing membranes, and/or sensing mechanisms can provide specific quantitative, semi-quantitative, qualitative, semi-qualitative analytical signals using a biological recognition element in combination with a transduction and/or detection element.

During general operation of an analyte measurement device, biosensor, sensor, sensing region, sensing portion, or sensing mechanism, a biological sample (e.g., blood or interstitial fluid) or component thereof is contacted with an enzyme (e.g., glucose oxidase), DNA, RNA, or protein or aptamer (e.g., one or more Periplasmic Binding Proteins (PBPs) or mutants or fusion proteins thereof having one or more analyte binding regions), either directly or after passing through one or more membranes, each region capable of specifically or reversibly binding to and/or reacting with at least one analyte. Interaction of the biological sample or a component thereof with the analyte measurement device, biosensor, sensor, sensing area, sensing portion, or sensing mechanism results in signal transduction that allows for qualitative, semi-qualitative, quantitative, or semi-quantitative determination of the analyte level in the biological sample, e.g., glucose, ketone, lactate, potassium, etc.

In one example, the sensing region or sensing portion may include at least a portion of a conductive substrate or at least a portion of a conductive surface (e.g., a wire (coaxial) or conductive trace or a substantially planar substrate including a substantially planar trace) and a membrane. In one example, the sensing region or sensing portion may include a non-conductive body; forming an electrochemically reactive surface at one location on the body and forming electronically connected working, reference and counter electrodes (optional) at another location on the body; and a sensing film attached to the body and covering the electrochemically reactive surface. In some examples, the sensing membrane further includes an enzyme domain (e.g., an enzyme domain) and an electrolyte phase (e.g., a free flowing liquid phase including an electrolyte-containing fluid, described further below). These terms are broad enough to include the entire device or only a sensing portion thereof (or something in between).

In another example, the sensing region may comprise one or more Periplasmic Binding Proteins (PBPs) (including mutants or fusion proteins thereof), or an aptamer having one or more analyte binding regions, each region capable of specifically and reversibly binding to at least one analyte. The change in aptamer or mutation in PBP may facilitate or alter one or more binding constants, long term protein stability (including thermostability), to bind the protein to a particular encapsulation matrix, membrane or polymer or to attach a detectable reporter group or "tag" to indicate a change in binding region or transduction of a signal corresponding to one or more analytes present in the biological fluid. Specific examples of binding region changes include, but are not limited to, hydrophobic/hydrophilic environmental changes, three-dimensional conformational changes, changes in amino acid/nucleic acid side chain orientation in the protein binding region, and redox state of the binding region. Such changes in the binding region provide for transduction of a detectable signal corresponding to one or more analytes present in the biological fluid.

In one example, the sensing region determines the selectivity between one or more analytes such that only the analyte that must be measured produces (transduces) a detectable signal. The selection may be based on any chemical or physical recognition of the analyte by the sensing region, wherein the chemical composition of the analyte is unchanged, or wherein the sensing region causes or catalyzes a reaction of the analyte that changes the chemical composition of the analyte.

As used herein, the term "sensitivity" is a broad term and will give one of ordinary and customary meaning to those skilled in the art (and is not limited to a particular or customized meaning) and refers to, but is not limited to, the amount of signal (e.g., in the form of current and/or voltage) generated by a predetermined amount (unit) of a measured analyte. For example, in one example, the sensor has a sensitivity (or slope) of about 1 picoamp to about 100 picoamps of current for every 1mg/dL of analyte.

The phrase "signal medium" or "transmission medium" should be taken to include any form of modulated data signal, carrier wave, or the like. The phrase "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

As used herein, the terms "transduction" or "transduction" and their grammatical equivalents are broad terms and will give one of ordinary skill in the art their ordinary and customary meaning (and are not limited to special or customized meanings) and refer to, but are not limited to, optical, electrical, electrochemical, acoustic/mechanical or colorimetric techniques and methods. Electrochemical properties include current and/or voltage, inductance, capacitance, impedance, transconductance, and potential. Optical properties include absorbance, fluorescence/phosphorescence decay rate, wavelength shift, dual wave phase modulation, bio/chemiluminescence, reflectivity, light scattering, and refractive index. For example, the sensing region transduces the identification of the analyte into a semi-quantitative or quantitative signal.

As used herein, the phrase "transduction element" is a broad phrase and will give one of ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning) and refers to, but is not limited to, an analyte recognition moiety capable of directly or indirectly facilitating detectable signal transduction corresponding to the presence and/or concentration of a recognized analyte. In one example, the transduction element is one or more enzymes, one or more aptamers, one or more ionophores, one or more capture antibodies, one or more proteins, one or more biological cells, one or more oligonucleotides, and/or one or more DNA or RNA moieties. The transdermal continuous multi-analyte sensor may be used in vivo for various lengths of time. The continuous multi-analyte sensor systems discussed herein may be percutaneous devices in that a portion of the device may be inserted through the recipient's skin and into underlying soft tissue while a portion of the device remains on the surface of the recipient's skin. In one aspect, to overcome the problems associated with noise or other sensor functions in a short period of time, one example employs a material that facilitates the formation of a fluid pocket around the sensor, such as a porous biological interface membrane or matrix that creates a space between the sensor and surrounding tissue. In some examples, the sensor is provided with a spacer adapted to provide a fluid pocket between the sensor and the recipient tissue. It is believed that the spacer (e.g., a biological interface material, matrix, structure, etc., as described in more detail elsewhere herein) transports oxygen and/or glucose to the sensor.

Membrane system

The membrane systems disclosed herein are suitable for use in implantable devices that are in contact with biological fluids. For example, the membrane system may be used with implantable devices, such as devices for monitoring and determining analyte levels in biological fluids, e.g., devices for monitoring glucose levels in individuals with diabetes. In some examples, the analyte measurement device is a continuous device. The analyte measurement device may employ any suitable sensing element to provide the primary signal, including, but not limited to, sensing elements involving enzymatic, chemical, physical, electrochemical, spectrophotometric, amperometric, potentiometric, polarimetric, thermo-assay, radiometric, immunochemical, and the like.

Suitable membrane systems for use in the aforementioned multi-analyte systems and devices may include those disclosed, for example, in U.S. patent 6,015,572, U.S. patent 5,964,745, and U.S. patent 6,083,523, the entire contents of which are incorporated herein by reference for their teachings of the membrane systems.

Typically, the membrane system comprises a plurality of domains, such as an electrode domain, an interference domain, an enzyme domain, a resistive domain, and a biological interface domain. The film system can be deposited on the exposed electroactive surface using known thin film techniques (e.g., vapor deposition, spray coating, electrodeposition, dipping, brush coating, film coating, drop coating, etc.). Additional steps may be applied after the deposition of the film material, such as drying, annealing, and curing (e.g., UV curing, thermal curing, moisture curing, radiation curing, etc.) to enhance certain properties, such as mechanical properties, signal stability, and selectivity. In a typical process, after deposition of the resistive domain film, a biological interface/drug release layer is formed having a "dry film" thickness of about 0.05 micrometers (μm) or less to about 1 μm, 2 μm, 3 μm, 4 μm,5 μm, 6 μm, 7 μm, 8 μm, 9 μm, 10 μm, 11 μm, 12 μm, 13 μm, 14 μm, 15 μm, or 16 μm. "Dry film" thickness refers to the thickness of the cured film cast from the coating formulation by standard coating techniques.

In certain examples, the bio-interface/drug release layer is formed from a bio-interface polymer, wherein the bio-interface polymer comprises one or more membrane domains and one or more zwitterionic repeat units, the membrane domains comprising polyurethane and/or polyurea segments. In some examples, the biological interface/drug release layer coating is formed from a polyurethaneurea having carboxybetaine groups incorporated in the polymer and nonionic hydrophilic polyethylene oxide segments, wherein the polyurethaneurea polymer is dissolved in an organic or non-organic solvent system according to a predetermined coating formulation, and crosslinked with an isocyanate crosslinker and cured at a moderate temperature of about 50 ℃. The solvent system may be a single solvent or a mixture of solvents to aid in the dissolution or dispersion of the polymer. The solvent may be selected as the polymerization medium or added after the polymerization is completed. The solvent is selected from solvents with a lower boiling point to facilitate drying and have lower toxicity for implant applications. Examples of such solvents include aliphatic ketones, esters, ethers, alcohols, hydrocarbons, and the like. Depending on the final thickness and solution viscosity (related to the percentage of polymer solids) of the bio-interface/drug release layer, the coating may be applied in a single step or multiple repeated steps (such as dipping) of the selected process to build the desired thickness. In yet other examples, the bioprotective polymer is formed from a polyurethaneurea having carboxylic acid groups and carboxybetaine groups incorporated in the polymer and nonionic hydrophilic polyethylene oxide segments, wherein the polyurethaneurea polymer is dissolved in an organic or non-organic solvent system in the coating formulation and crosslinked with a carbodiimide (e.g., 1-ethyl-3- (3-dimethylaminopropyl) carbodiimide (EDC)) and cured at a moderate temperature of about 50 ℃.

In other examples, the biological interface/drug release layer coating is formed from a polyurethaneurea having sulfobetaine groups incorporated in the polymer and nonionic hydrophilic polyethylene oxide segments, wherein the polyurethaneurea polymer is dissolved in an organic or non-organic solvent system according to a predetermined coating formulation, and crosslinked with an isocyanate crosslinker and cured at a moderate temperature of about 50 ℃. The solvent system may be a single solvent or a mixture of solvents to aid in the dissolution or dispersion of the polymer. The solvent may be selected as the polymerization medium or added after the polymerization is completed. The solvent is selected from solvents with a lower boiling point to facilitate drying and have lower toxicity for implant applications. Examples of such solvents include aliphatic ketones, esters, ethers, alcohols, hydrocarbons, and the like. Depending on the final thickness and solution viscosity (related to the percentage of polymer solids) of the bio-interface/drug release layer, the coating may be applied in a single step or multiple repeated steps (such as dipping) of the selected process to build the desired thickness. In yet other examples, the biointerfacial polymer is formed from a polyurethaneurea having unsaturated hydrocarbon groups and sulfobetaine groups incorporated in the polymer and nonionic hydrophilic polyethylene oxide segments, wherein the polyurethaneurea polymer is dissolved in an organic or non-organic solvent system in the coating formulation and crosslinked with heat or radiation (including UV, LED light, electron beam, etc.) in the presence of an initiator and cured at a moderate temperature of about 50 ℃. Examples of unsaturated hydrocarbons include allyl, vinyl, acrylate, methacrylate, alkene, alkyne, and the like.

In some examples, tethers are used. Tethers are polymers or chemical moieties that do not participate in the (electro) chemical reactions involved in sensing, but form chemical bonds with the (electro) chemically active components of the membrane. In some examples, the bonds are covalent bonds. In one example, the tether may be formed in solution prior to forming the one or more interlayers of the film, wherein the tether binds the two (electro) chemically active components directly to each other, or alternatively, the tether binds the (electro) chemically active components to the polymer backbone structure. In another example, the (electro) chemically active component is co-mixed with a cross-linking agent (and optionally a polymer) having an adjustable length, and the tethering reaction occurs as an in situ cross-link. The tethering may be used to maintain a predetermined number of degrees of freedom of NAD (P) H for efficient enzymatic catalysis, wherein "efficient" enzymatic catalysis results in the analyte sensor continuously monitoring one or more analytes for a period of about 5 days to about 15 days or more.

Film manufacture

The polymer may be processed by solution-based techniques such as spraying, dipping, casting, electrospinning, vapor deposition, spin coating, and the like. The water-based polymer emulsion may be manufactured into a film by methods similar to those used for solvent-based materials. In both cases, evaporation of the volatile liquid (e.g., organic solvent or water) leaves a polymer film. Crosslinking of the deposited film or layer can be performed by a number of methods using polyfunctional reactive ingredients. The liquid system may be cured by heat, moisture, high energy radiation, ultraviolet light or by completion of the reaction to produce the final polymer in the mold or on the substrate to be coated.

In some examples, the wetting characteristics of the membrane (and the extent of sensor drift exhibited by the extension sensor) may be modulated and/or controlled by creating covalent crosslinks between the surface-active group-containing polymer, the functional group-containing polymer, the polymer with zwitterionic groups (or precursors or derivatives thereof), and combinations thereof. Crosslinking can have a significant impact on the film structure, which in turn can affect the surface wetting characteristics of the film. Crosslinking can also affect the tensile strength, mechanical strength, water absorption rate, and other properties of the film.

The crosslinked polymers may have different crosslink densities. In some examples, a cross-linking agent is used to facilitate cross-linking between the layers. In other examples, heat is used to form the crosslinks instead of (or in addition to) the crosslinking techniques described above. For example, in some examples, imide and amide linkages may be formed between two polymers due to the high temperature. In some examples, photocrosslinking is performed to form covalent bonds between the polycationic layer and the polyanionic layer. One of the main advantages of photocrosslinking is that it provides the possibility of patterning. In certain examples, patterning is performed using photocrosslinking to modify the membrane structure and thus adjust the wetting properties of the membrane and membrane system, as discussed herein.

Polymers having domains or segments functionalized to allow crosslinking may be prepared by at least the methods as discussed herein. For example, polyurethaneurea polymers having aromatic or aliphatic segments containing electrophilic functional groups (e.g., carbonyl, aldehyde, anhydride, ester, amide, isocyano, epoxy, allyl, or halo groups) can be crosslinked with a crosslinking agent having multiple nucleophilic groups (e.g., hydroxyl, amine, urea, urethane, or thiol groups). In further examples, polyurethaneurea polymers having aromatic or aliphatic segments containing nucleophilic functional groups may be crosslinked with a crosslinking agent having multiple electrophilic groups. Still further, the polyurethaneurea polymer having a hydrophilic segment containing a nucleophilic functional group or an electrophilic functional group may be crosslinked with a crosslinking agent having a plurality of electrophilic functional groups or nucleophilic groups. Unsaturated functional groups on polyurethaneurea can also be used for crosslinking by reaction with polyvalent radical agents. Non-limiting examples of suitable cross-linking agents include isocyanate, carbodiimide, glutaraldehyde, aziridine, silane or other aldehydes, epoxy, acrylate, radical-based agents, ethylene Glycol Diglycidyl Ether (EGDE), poly (ethylene glycol) diglycidyl ether (PEGDE), or dicumyl peroxide (DCP). In one example, about 0.1 wt% to about 15 wt% of the crosslinking agent is added relative to the total dry weight of these ingredients added when blending the crosslinking agent and polymer. In another example, about 1 wt% to about 10 wt% of the crosslinking agent is added relative to the total dry weight of these components added when blending the crosslinking agent and polymer. In yet another example, about 5 wt% to about 15 wt% of the crosslinking agent is added relative to the total dry weight of these components added when blending the crosslinking agent and polymer. During the curing process, it is believed that substantially all of the crosslinker reacts leaving substantially no detectable unreacted crosslinker in the final film.

The polymers disclosed herein may be formulated as a mixture that may be stretched into a film or applied to a surface using methods such as spraying, self-assembled monolayer (SAM), painting, dip coating, vapor deposition, molding, 3-D printing, lithographic techniques (e.g., photolithography), micro-and nano-pipetting techniques, screen printing, and the like. The mixture may then be cured at an elevated temperature (e.g., about 30 ℃ to about 150 ℃). Other suitable curing methods may include, for example, ultraviolet radiation, electron beam, or gamma radiation.

In some cases, using a continuous multi-analyte monitoring system that includes sensors configured with bio-protective and/or drug release films, it is believed that the foreign body response is the primary event of prolonged implantation around the implant device and can be managed or manipulated to support, rather than block or block analyte transport. In another aspect, to extend the life of the sensor, one example employs a material that promotes vascularized tissue ingrowth, for example, within a porous biological interface membrane. For example, tissue ingrowth into the porous biological interface material surrounding the sensor may promote sensor function over an extended period of time (e.g., weeks, months, or years). It has been observed that tissue bed ingrowth and formation can take up to 3 weeks. Tissue ingrowth and tissue bed formation are considered to be part of the foreign body response. As will be discussed herein, the foreign body response may be manipulated through the use of porous bioprotective materials that surround the sensor and promote tissue and microvasculature ingrowth over time.

Thus, a sensor as discussed in examples herein may include a biological interface layer. Similar to the drug release layer, the biological interface layer may include, but is not limited to, for example, a porous biological interface material including a solid portion and interconnecting cavities, all of which are described in more detail elsewhere herein. The biological interface layer may be used to improve sensor function over a long period of time (e.g., after tissue ingrowth).

Thus, a sensor as discussed in examples herein may include a drug release film that at least partially acts as or in combination with a biological interface film. The drug release film may include, for example, a material comprising a hard-soft segment polymer having hydrophilic and optionally hydrophobic domains, all of which are described in more detail elsewhere herein, useful for chronically improving sensor function (e.g., after tissue ingrowth). In one example, a material comprising a hard-soft segment polymer having hydrophilic and optionally hydrophobic domains is configured to release a combination of dexamethasone or a derivative form of dexamethasone acetate with dexamethasone, thereby achieving one or more different release rates of the anti-inflammatory agent and extending the useful life of the sensor. Other suitable drug release films of the present disclosure may be selected from silicone polymers; polytetrafluoroethylene; expanded polytetrafluoroethylene; polyethylene-co-tetrafluoroethylene; a polyolefin; a polyester; a polycarbonate; biostable polytetrafluoroethylene; homopolymers, copolymers, terpolymers of polyurethane; polypropylene (PP); polyvinyl chloride (PVC); polyvinylidene fluoride (PVDF); polyvinyl alcohol (PVA); polyvinyl acetate; ethylene Vinyl Acetate (EVA); polybutylene terephthalate (PBT); polymethyl methacrylate (PMMA); polyetheretherketone (PEEK); a polyamide; polyurethanes and copolymers and blends thereof; polyurethaneurea polymers and copolymers and blends thereof; cellulosic polymers and copolymers and blends thereof; poly (ethylene oxide) and copolymers and blends thereof; poly (propylene oxide) and copolymers and blends thereof; polysulfones and block copolymers thereof, including, for example, diblock, triblock, alternating, random, and graft copolymers; cellulose; a hydrogel polymer; poly (2-hydroxyethyl methacrylate) (pHEMA) and copolymers and blends thereof; hydroxyethyl methacrylate (HEMA) and copolymers and blends thereof; polyacrylonitrile-polyvinylchloride (PAN-PVC) and copolymers and blends thereof; acrylic copolymers and blends thereof; nylon and copolymers and blends thereof; a polydifluoroethylene; polyanhydrides; poly (l-lysine); poly (L-lactic acid); hydroxyethyl methacrylate and copolymers and blends thereof; hydroxyapatite and copolymers and blends thereof.

Exemplary Multi-analyte sensor Membrane configuration

Continuous multi-analyte sensors are provided having various membrane configurations adapted to simultaneously, intermittently, and/or sequentially facilitate signal transduction corresponding to analyte concentrations. In one example, such sensors may be configured using a signal transducer that includes one or more transducing elements ("TL"). Such continuous multi-analyte sensors may employ various transduction means, such as amperometric, potentiometric, and impedance assays, among others.

In one example, the transduction element comprises one or more membranes, which may comprise one or more layers and/or domains, each of which may independently comprise one or more signal transducers, e.g., enzymes, RNAs, DNAs, aptamers, binding proteins, etc. As used herein, a transduction element includes an enzyme, ionophore, RNA, DNA, aptamer, binding protein, and are used interchangeably.

In one example, the transduction element is present in one or more films, layers, or domains formed on the sensing region. In one example, such sensors may be configured using one or more enzyme domains, such as a membrane domain including enzyme domains, also referred to as an EZ layer ("EZL"), each of which may include one or more enzymes. The reference to "enzyme layer" below is intended to include all or part of an enzyme domain, any of which may be all or part of a membrane system as discussed herein, e.g., as a single layer, as two or more layers, as a bilayer pair, or as a combination thereof.

In one example, a continuous multi-analyte sensor uses one or more of the following analyte-substrate/enzyme pairs: for example, a combination of sarcosine oxidase and creatinine amidohydrolase for sensing creatinine. Other examples of analyte/oxidase combinations that may be used in the sensing region include, for example, alcohol/alcohol oxidase, cholesterol/cholesterol oxidase, galactose/galactose oxidase, choline/choline oxidase, glutamate/glutamate oxidase, glycerol/glycerol-3 phosphate oxidase (or glycerol oxidase), bilirubin/bilirubin oxidase, ascorbic acid/ascorbate oxidase, uric acid/urate oxidase, pyruvic acid/pyruvate oxidase, hypoxanthine/xanthine oxidase, glucose/glucose oxidase, lactate/lactate oxidase, L-amino acid oxidase, and glycine/sarcosine oxidase. Other analyte-substrate/enzyme pairs may be used, including analyte-substrate/enzyme pairs that contain genetically altered enzymes, immobilized enzymes, mediator-linked enzymes, dimerizing and/or fusing enzymes.

NAD-based multi-analyte sensor platform

Nicotinamide adenine dinucleotide (NAD (P) ⁺/NAD (P) H) is a coenzyme, for example a dinucleotide consisting of two nucleotides linked by their phosphate groups. One nucleotide contains an adenine nucleobase and the other nucleotide contains nicotinamide. NAD exists in two forms, for example, an oxidized form (NAD (P) +) and a reduced form (NAD (P) H) (h=hydrogen). The reaction of NAD+ and NADH is reversible, so that the coenzyme can be continuously cycled between NAD (P) ⁺/and NAD (P) H forms without substantial consumption.

In one example, one or more enzyme domains of a sensing region of a continuous multi-analyte sensor apparatus disclosed herein includes an amount of nad+ or NADH for providing transduction of a detectable signal corresponding to the presence or concentration of one or more analytes. In one example, one or more enzyme domains of a sensing region of a continuous multi-analyte sensor apparatus disclosed herein includes excess nad+ or NADH for providing prolonged transduction of a detectable signal corresponding to the presence or concentration of one or more analytes.

In one example, NAD, NADH, NAD +, NAD (P) +, ATP, flavin Adenine Dinucleotide (FAD), magnesium (mg++), pyrroloquinoline quinone (PQQ), and functionalized derivatives thereof may be used in combination with one or more enzymes in a continuous multi-analyte sensor apparatus. In one example, NAD, NADH, NAD +, NAD (P) +, ATP, flavin Adenine Dinucleotide (FAD), magnesium (mg++), pyrroloquinoline quinone (PQQ), and functionalized derivatives are incorporated into the sensing region. In one example, NAD, NADH, NAD +, NAD (P) +, ATP, flavin Adenine Dinucleotide (FAD), magnesium (mg++), pyrroloquinoline quinone (PQQ), and functionalized derivatives are dispersed or distributed in one or more membranes or domains of the sensing region.

In one aspect of the disclosure, continuous sensing of one or more or two or more analytes using an nad+ dependent enzyme is provided in one or more membranes or domains of the sensing region. In one example, the membrane or domain provides for the retention and stable recirculation of nad+ and a mechanism for transducing NADH oxidation or nad+ reduction into an amperometric current that can be measured by amperometry. In one example, as described below, continuous sensing of multiple analytes that reversibly bind or at least one of which is oxidized or reduced by an nad+ dependent enzyme, such as ketone (β -hydroxybutyrate dehydrogenase), glycerol (glycerol dehydrogenase), cortisol (11β -hydroxysteroid dehydrogenase), glucose (glucose dehydrogenase), alcohol (alcohol dehydrogenase), aldehyde (aldehyde dehydrogenase), and lactate (lactate dehydrogenase), is provided. In other examples, as described below, a membrane is provided that enables continuous, in vivo sensing of multiple analytes using FAD-dependent dehydrogenases, such as fatty acid (acyl-coa dehydrogenase).

An exemplary configuration of one or more membranes or portions thereof is an arrangement for providing retention and recycling of nad+. Thus, the electrode surface or planar conductive surface of the lead (coaxial) is coated with at least one layer comprising at least one enzyme, as depicted in fig. 8A. Referring to fig. 8B, one or more optional layers may be positioned between the electrode surface and one or more enzyme domains. For example, one or more interfering domains (also referred to as "interferent-blocking layers") may be used to reduce or eliminate signal contributions from existing undesirable substances, or one or more electrodes (not shown) may be used to aid in wetting, system balancing, and/or priming. As shown in fig. 8A-8B, one or more of the membranes provide a nad+ reservoir domain, providing a reservoir for nad+. In one example, using one or more interferent blocking membranes and measuring H2O2 production or O2 consumption of an enzyme (such as or similar to NADH oxidase) with a potentiostat, nad+ reservoir and enzyme domain positions can be switched to promote better consumption of excess nad+ and slower unwanted out-diffusion. An exemplary sensor configuration can be found in U.S. provisional patent application No. 63/321340, "CONTINUOUS ANALYTE MONITORING SENSOR SYSTEMS AND METHODS OF USING THE SAME," filed on 18, 3, 2022, the entire contents of which are incorporated herein by reference.

In one example, one or more mediators optimal for NADH oxidation are bound to one or more electrode domains or enzyme domains. In one example, an organic mediator, such as phenanthroline dione or nitrosoaniline, is used. In another example, a metal-organic mediator such as ruthenium-phenanthroline-dione or osmium (bpy) ₂ Cl, a polymer containing covalently coupled organic mediators, or an organometallic coordination mediator polymer, such as polyvinylimidazole-Os (bpy) ₂ Cl, or polyvinylpyridine-organometallic coordination mediators (including ruthenium-phenanthroline-dione) are used. Other mediators may be used, as discussed further below.

In humans, serum levels of beta-hydroxybutyrate (BHB) are typically in the low micromolar range, but can be raised to about 6mM to 8mM. Serum levels of BHB can reach 1mM to 2mM after vigorous exercise, or stable levels above 2mM with a ketogenic diet with little carbohydrate. Other ketones such as acetoacetate and acetone are present in serum, however, most of the dynamic range of ketone levels is in the form of BHB. Thus, monitoring (e.g., continuous monitoring) of BHB is useful for providing health information to a user or medical provider.

Another example of a continuous ketone analyte detection configuration employing electrode-associated mediator-coupled diaphorase/nad+/dehydrogenase is depicted below:

In one example, diaphorase is electrically coupled to an electrode having an organometallic coordination mediator polymer. In another example, diaphorase is covalently coupled to an electrode having an organometallic coordination mediator polymer. Alternatively, multiple enzyme domains may be used in the enzyme layer, e.g., to separate electrode-associated diaphorase (closest to the electrode surface) from more distally adjacent nad+ or dehydrogenase enzymes to substantially separate NADH oxidation from analyte (ketone) oxidation. Alternatively, the nad+ may be closer to the electrode surface than the adjacent enzyme domain comprising the dehydrogenase. In one example, nad+ and/or HBDH are present in the same or different enzyme domains, and either of them can be immobilized, for example, using an amine-reactive cross-linker (e.g., glutaraldehyde, epoxide, NHS ester, imido ester). In one example, nad+ is coupled to the polymer and is present in the same or a different enzyme domain as HBDH. In one example, the molecular weight of nad+ is increased to prevent or eliminate migration from the sensing region, e.g., dimerization of nad+ using the C6 terminal amine of nad+ with any amine-reactive cross-linker. In one example, nad+ may be covalently coupled to an aspect of the enzyme domain that has a higher molecular weight than nad+, which may improve the stability profile of nad+ and thereby improve the ability to retain and/or immobilize nad+ in the enzyme domain. For example, dextran-NAD.

In one example, the sensing region includes one or more NADH-receptor oxidoreductases and one or more NAD-dependent dehydrogenases. In one example, the sensing region includes one or more NADH-receptor oxidoreductases and one or more NAD (P) -dependent dehydrogenases, wherein NAD (P) + or NAD (P) H is present in the sensing region as a cofactor. In one example, the sensing region includes an amount of diaphorase.

In one example, a ketone sensing configuration suitable for combination with another analyte sensing configuration is provided. Thus, EZL layers about 1 μm to 20 μm thick were prepared by providing a EZL solution composition of 10mM HEPES in water with about 20 μL 500mg/mL HBDH, about 20 μL [500mg/mL NAD (P) H, 200mg/mL polyethylene glycol-diethylene glycol ether (PEG-DGE) ] of about 400MW, about 20 μL 500mg/mL diaphorase, about 40 μL 250mg/mL polyvinylimidazole-osmium bis (2, 2' -bipyridine) chloride (PVI-Os (bpy) 2 Cl), to a substrate such as a working electrode to provide, after drying, about 15% to 40% by weight HBDH, about 5% to 30% diaphorase, about 5% to 30% NAD (P) H, about 10% to 50% PVI-Os (bpy) 2Cl, and about 1% to 12% PEG-E (400 MW). The substrates discussed herein, which may include working electrodes, may be formed of gold, platinum, palladium, rhodium, iridium, titanium, tantalum, chromium, and/or alloys or combinations thereof or carbon (e.g., graphite, glassy carbon, carbon nanotubes, graphene, or doped diamond, and combinations thereof).

A resistive domain (also referred to as a resistive layer ("RL")) is contacted with the enzyme domain. In one example, the RL includes about 55% to 100% PVP and about 0.1% to 45% PEG-DGE. In another example, the RL includes about 75% to 100% PVP and about 0.3% to 25% PEG-DGE. In yet another example, the RL includes about 85% to 100% PVP and about 0.5% to 15% PEG-DGE. In yet another example, the RL includes substantially 100% PVP.

The exemplary continuous ketone sensor comprising an NAD (P) H reservoir domain as depicted in fig. 8A-8B is configured such that NAD (P) H is not rate-limiting in any of the enzyme domains of the sensing region. In one example, the loading of NAD (P) H in the NAD (P) H reservoir domain is greater than about 20 wt%, 30 wt%, 40 wt%, or 50 wt%. One or more of the membranes or portions of one or more membrane domains (hereinafter also referred to as "membranes") may also contain a polymer or protein binder, such as a zwitterionic polyurethane and/or albumin. Alternatively, the membrane may contain one or more analyte-specific enzymes (e.g., HBDH, glycerol dehydrogenase, etc.) in addition to NAD (P) H, such that optionally the NAD (P) H reservoir membrane also provides a catalytic function. In one example, NAD (P) H is dispersed or distributed in or with a polymer (or protein) and can be crosslinked to a degree that still allows for adequate enzyme/cofactor functionality and/or reduces intra-domain NAD (P) H flux.

In one example, NADH oxidase alone or in combination with superoxide dismutase (SOD) is used in one or more membranes of the sensing region. In one example, superoxide dismutase (SOD) is used in an amount capable of scavenging some or most of the one or more radicals generated by the NADH oxidase. In one example, NADH oxidase is used alone or in combination with superoxide dismutase (SOD) in combination with NAD (P) H and/or functionalized polymers, wherein NAD (P) H is immobilized onto the polymer from a C6 terminal amine in one or more membranes of the sensing region.

In one example, NAD (P) H is immobilized to a degree that maintains NAD (P) H catalytic functionality. In one example, dimerizing NAD (P) H serves to entrap NAD (P) H within one or more membranes by crosslinking their respective C6 terminal amines with a suitable amine reactive crosslinking agent, such as glutaraldehyde or PEG-DGE.

The foregoing continuous ketone sensor configuration may be adapted for use with other analytes or in combination with other sensor configurations. For example, analyte-dehydrogenase combinations may be used in any of the membranes of the sensing region, including: glycerol (glycerol dehydrogenase); cortisol (11 beta-hydroxysteroid dehydrogenase); glucose (glucose dehydrogenase); alcohols (alcohol dehydrogenases); aldehyde (aldehyde dehydrogenase); and lactic acid (lactate dehydrogenase).

In one example, a semi-permeable membrane is used in or adjacent to the sensing region or one or more membranes of the sensing region in order to attenuate the flux of at least one analyte or chemical. In one example, the semipermeable membrane attenuates the flux of at least one analyte or chemical species so as to provide a linear response from the transduction signal. In another example, the semi-permeable membrane prevents or eliminates NAD (P) H from flowing out of the sensing region or any membrane or domain. In one example, the semi-permeable membrane may be an ion-selective membrane that is selective for an ion analyte of interest (such as ammonium ions).

In another example, a continuous multi-analyte sensor configuration is prepared that includes one or more enzymes and/or at least one cofactor. FIG. 1C depicts such an exemplary configuration of an Enzyme domain 850 comprising an Enzyme (Enzyme) having an amount of cofactor (Cofactor) positioned proximate at least a portion of a working electrode ("WE") surface, wherein WE comprises an electrochemically reactive surface. In one example, the second membrane 851, which includes an amount of cofactor, is positioned adjacent to the first enzyme domain. The amount of cofactor in the second membrane may provide an excess to the enzyme, e.g., to extend sensor lifetime. One or more resistive domains 852 ("RL") are positioned adjacent to (or may be positioned between) the second film. RL may be configured to block the diffusion of cofactors from the second film. Electronic transduction from cofactor to WE corresponds directly or indirectly to the signal of the analyte concentration.

Fig. 8D depicts an alternative enzyme domain configuration that includes a first membrane 851 with an amount of cofactor that is positioned closer to at least a portion of the WE surface. An enzyme domain 850 comprising an amount of enzyme is positioned adjacent to the first membrane.

In the membrane structures depicted in fig. 8C-8D, the generation of electrochemically active species in the enzyme domain diffuses to the WE surface and transduces a signal that directly or indirectly corresponds to the analyte concentration. In some examples, the electrochemically active material comprises hydrogen peroxide. For a sensor configuration including cofactors, the cofactors from the first layer may diffuse to the enzyme domain to extend sensor life, for example, by regenerating the cofactors. For other sensor configurations, cofactors may optionally be included to improve performance attributes, such as stability. For example, the continuous ketone sensor may include NAD (P) H and a divalent metal cation, such as Mg ⁺². One or more resistive domains RL may be located adjacent to the second film (or may be located between layers). The RL may be configured to block diffusion of cofactors from the second film and/or interferents from reaching the WE surface. Other configurations may be used in the foregoing configurations, such as electrodes, resistors, biological interfaces, and drug release films, layers, or domains. In other examples, the continuous analyte sensor includes one or more cofactors that contribute to sensor performance.

FIG. 8E depicts another continuous multi-analyte membrane configuration in which { beta } -hydroxybutyrate dehydrogenase BHBDH in a first enzyme domain 853 is positioned near the working electrode WE and a second enzyme domain 1854, e.g., comprising Alcohol Dehydrogenase (ADH) and NADH, is positioned adjacent to the first enzyme domain. One or more resistive domains RL 852 can be disposed adjacent to the second enzyme domain 854. In this configuration, the presence of a combination of alcohol and ketone in the serum co-acts to provide a transduction signal corresponding to at least one of the analyte concentrations (e.g., ketone). Thus, when NADH present in the second more distal enzyme domain consumes alcohol present in the serum environment, NADH is oxidized to NAD (P) H, which diffuses into the first membrane layer to provide BHBDH-catalyzed electron transfer of acetoacetate and transduction of a detectable signal corresponding to ketone concentration. In one example, the enzyme may be configured for reverse catalysis and may produce a substrate for catalyzing another enzyme present in the same or a different layer or domain. Other configurations may be used in the foregoing configurations, such as electrodes, resistors, biological interfaces, and drug release films, layers, or domains. Thus, a first enzyme domain that is farther away from the WE than a second enzyme domain may be configured to generate cofactors or other elements to act as reactants (and/or reactant substrates) for the second enzyme domain to detect one or more target analytes.

Alcohol sensor configuration

In one example, a continuous alcohol (e.g., ethanol) sensor device configuration is provided. In one example, one or more enzyme domains comprising Alcohol Oxidase (AOX) are provided and the presence and/or amount of alcohol is transduced by the production of hydrogen peroxide alone or in combination with oxygen consumption or with another substrate-oxidase system (e.g., glucose-glucose oxidase), wherein hydrogen peroxide and/or oxygen and/or glucose can be detected and/or measured qualitatively or quantitatively using amperometric methods.

In one example, the sensing region for the aforementioned enzyme substrate-oxidase configuration has one or more enzyme domains, including one or more electrodes. In one example, the sensing region for the foregoing enzyme substrate-oxidase configuration has one or more enzyme domains with or without one or more electrodes, and one or more interference blocking membranes (e.g., permselective membranes, charge-exclusion membranes) to attenuate diffusion of one or more interferents through the membrane to the working electrode. In one example, the sensing region for the foregoing substrate-oxidase configuration has one or more enzyme domains with or without one or more electrodes, and further includes one or more resistive domains with or without one or more interference blocker films to attenuate one or more analytes or enzyme substrates. In one example, the sensing region for the foregoing substrate-oxidase configuration has one or more enzyme domains with or without one or more electrodes, and the one or more resistive domains with or without one or more interference blocking films further independently include one or more biological interface films and/or drug release films to attenuate one or more analytes or enzyme substrates and attenuate the immune response of the inserted recipient.

In one example, one or more interference blocker films are deposited adjacent to the working electrode and/or electrode surface. In one example, one or more interference blocker films are deposited directly adjacent to the working electrode and/or electrode surface. In one example, one or more interference blocker films are deposited between another layer or film or domain adjacent to the working electrode or electrode surface to attenuate diffusion of one or all analytes other than oxygen through the sensing region. Such membranes may be used to attenuate the alcohol itself as well as attenuate other electrochemically active species or other analytes that, if diffused to the working electrode, may otherwise interfere by generating a signal.

In one example, the working electrode used comprises platinum and the applied potential is about 0.5 volts.

In one example, electrochemical sensing of oxygen level changes may be performed, for example, by coating the electrode with one or more films of one or more polymers (such as NAFION ^TM), for example, in a Clark-type electrode arrangement or in a different configuration. Based on the change in potential, a change in oxygen concentration can be recorded, which is directly or indirectly related to the concentration of alcohol. When properly designed to take stoichiometric actions, the presence of a particular concentration of alcohol should cause a commensurate decrease in local oxygen in direct (linear) relation to the alcohol concentration. Thus, a multi-analyte sensor for both alcohol and oxygen may be provided.

In another example, the above-mentioned alcohol sensing configuration may include one or more secondary enzymes that react with an alcohol/alcohol oxidase catalyzed reaction product (e.g., hydrogen peroxide), and provide an oxidized form of the secondary enzyme that transduces an alcohol-dependent signal to WE/RE at a lower potential than without the secondary enzyme. Thus, in one example, an alcohol/alcohol oxidase is used with a reduced form of peroxidase (e.g., horseradish peroxidase). The alcohol/alcohol oxidase may be in the same or different layer as the peroxidase, or they may be spatially separated away from the electrode surface, e.g., the alcohol/alcohol oxidase is farther from the electrode surface and the peroxidase is closer to the electrode surface, or alternatively, the alcohol/alcohol oxidase is closer to the electrode surface and the peroxidase is farther from the electrode surface. In one example, the alcohol/alcohol oxidase is further from the electrode surface and the peroxidase further includes any combination of electrodes, interference, resistance, and biological interface membrane to optimize signal, durability, reduce drift, or extend lifetime.

In another example, the above-mentioned alcohol sensing configuration may include one or more mediators. In one example, one or more mediators are present in, on, or around one or more electrodes or electrode surfaces, and/or deposited on or otherwise associated with the surface of a Working Electrode (WE) or Reference Electrode (RE). In one example, one or more mediators eliminate or reduce direct oxidation of interfering species that may reach the WE or RE. In one example, the one or more mediators provide a reduction in the operating potential of the WE/RE on the platinum electrode, e.g., from about 0.6V to about 0.3V or less, which can reduce or eliminate oxidation of endogenous interfering substances. Examples of one or more mediators are provided below. Other electrodes, such as counter electrodes, may be employed.

In one example, other enzymes or additional components may be added to the polymer mixture that make up any portion of the sensing region to increase the stability of the foregoing sensor and/or reduce or eliminate byproducts of the alcohol/alcohol oxidase reaction. Increased stability includes shelf or shelf life and/or operational stability (e.g., retention of enzyme activity during use). For example, byproducts of the enzymatic reaction may be undesirable for increasing shelf life and/or operational stability, and thus may be desirable for reduction or removal. In one example, xanthine oxidase may be used to remove one or more byproducts of an enzymatic reaction.

In another example, a dehydrogenase is used with an oxidase to detect alcohol alone or in combination with oxygen. Thus, in one example, alcohol is oxidized to aldehyde using an alcohol dehydrogenase in the presence of reduced nicotinamide adenine dinucleotide (NAD (P) H) or reduced nicotinamide adenine dinucleotide phosphate (NAD (P) +). In order to provide a continuous source of NAD (P) H or NAD (P) +, NADH oxidase or NADPH oxidase is used to oxidize NAD (P) H or NAD (P) +, while consuming oxygen. In another example, diaphorase may be used in place of or in combination with NADH oxidase or NADPH oxidase. Alternatively, excess NAD (P) H may be incorporated into one or more enzyme domains and/or one or more electrodes in an amount that is tailored to the expected duration of the projected lifetime of the sensor.

In the foregoing dual enzyme configuration, the signal may be sensed by any of the following means: (1) Electrically coupled (e.g., "wired") Alcohol Dehydrogenases (ADHs), for example, using electroactive hydrogel polymers comprising one or more mediators; or (2) oxygen electrochemical sensing to measure the oxygen consumption of NADH oxidase. In alternative examples, the cofactor NAD (P) H or NAD (P) + may be coupled with a polymer, such as dextran, that is immobilized in the enzyme domain along with the ADH. This provides retention of cofactors for the ADH active site and its availability. In the above examples, any combination of electrodes, interference, resistance, and biological interface film may be used to optimize signals, durability, reduce drift, or extend lifetime. In one example, electrical coupling is provided, directly or indirectly, with at least a portion of a transduction element (such as an aptamer, enzyme, or cofactor) and at least a portion of an electrode surface, e.g., via a covalent bond or an ionic bond. Chemical moieties capable of assisting in the transfer of electrons from the enzyme or cofactor to the electrode surface may be used, including one or more mediators as described below.

In one example, any of the foregoing continuous alcohol sensor configurations are combined with any of the foregoing continuous ketone monitoring configurations to provide a continuous multi-analyte sensor apparatus as described further below. In one example, the continuous glucose monitoring configuration is combined with any of the foregoing continuous alcohol sensor configurations and any of the foregoing continuous ketone monitoring configurations to provide a continuous multi-analyte sensor apparatus as described further below.

Uric acid sensor configuration

In another example, a continuous uric acid sensor device configuration is provided. Thus, in one example, urate Oxidase (UOX) may be included in one or more enzyme domains and positioned adjacent to the working electrode surface. Catalytic production of uric acid using UOX produces hydrogen peroxide, which can be detected using amperometric, potentiometric, and impedance measurement techniques, among others. In one example, to reduce or eliminate interference from direct oxidation of uric acid on an electrode surface, one or more electrodes, interference, and/or resistive domains may be deposited on at least a portion of the working electrode surface. Such membranes may be used to attenuate diffusion of uric acid and other analytes to the working electrode, which analytes may interfere with signal transduction.

In an alternative example, the uric acid continuous sensing device configuration includes sensing oxygen level changes around the WE surface, e.g., as in a Clark-type electrode arrangement, or one or more electrodes may independently include one or more different polymers, such as NAFION ^TM, a polyamphonic polymer, or a polymer mediator adjacent to at least a portion of the electrode surface. In one example, an electrode surface having one or more electrode domains provides for measuring oxygen at different or lower voltages. Oxygen levels and their changes may be sensed, recorded and correlated to uric acid concentration based on, for example, using conventional calibration methods.

In one example, one or more coatings may be deposited on the WE surface, alone or in combination with any of the foregoing configurations, uric acid sensor configurations, in order to reduce the potential at the WE for uric acid signal transduction. One or more coatings can be deposited or formed on the WE surface and/or other coatings formed thereon using a variety of techniques including, but not limited to, dipping, electrodeposition, vapor deposition, spraying, and the like. In one example, the coated WE surface can provide a redox reaction such as hydrogen peroxide at a lower potential (compared to 0.6V on a platinum electrode surface without such a coating). Examples of materials that may be coated or annealed onto the WE surface include, but are not limited to, prussian blue, medola blue, methylene green, methyl viologen, ferrocyanide, ferrocene, cobalt ions, cobalt phthalocyanine, and the like.

In one example, one or more secondary enzymes, cofactors, and/or mediators (electrically coupled or polymeric mediators) can be added to the enzyme domain with UOX to facilitate direct or indirect electron transfer to the WE. For example, in such a configuration, regeneration of the initially oxidized form of the secondary enzyme is reduced by the WE for signal transduction. In one example, the secondary enzyme is horseradish peroxidase (HRP).

Choline sensor configuration

In one example, a continuous choline sensor apparatus may be provided, for example, using choline oxidase that generates hydrogen peroxide as choline oxidizes. Thus, in one example, the at least one enzyme domain includes Choline Oxidase (COX) adjacent to the at least one WE surface, optionally with one or more electrodes and/or interfering membranes positioned between the WE surface and the at least one enzyme domain. Catalysis of choline with COX results in the production of hydrogen peroxide, which can be detected using amperometric, potentiometric, and impedance measurement techniques.

In one example, the foregoing continuous choline sensor configuration is combined with any one of the foregoing continuous alcohol sensor configuration and continuous uric acid sensor configuration to provide a continuous multi-analyte sensor apparatus as further described below. The continuous multi-analyte sensor apparatus may also include continuous glucose monitoring capabilities. Other membranes may be used in the aforementioned continuous choline sensor configurations, such as electrodes, resistors, biological interfaces, and drug release membranes.

Cholesterol sensor configuration

In one example, a continuous cholesterol sensor configuration may be fabricated using cholesterol oxidase (CHOX) in a manner similar to the previously described sensor. Thus, one or more enzyme domains including CHOX can be located adjacent to at least one WE surface. Catalysis of free cholesterol with CHOX results in the production of hydrogen peroxide, which can be detected using amperometric, potentiometric and impedance measurement techniques, among others.

An exemplary cholesterol sensor configuration using platinum WE was prepared in which at least one interfering membrane was positioned adjacent to at least one WE surface, upon which at least one enzyme domain comprising CHOX was present, upon which at least one resistive domain was positioned to control diffusion characteristics.

The above method and the cholesterol sensor may measure free cholesterol, however, by modification, the configuration may measure more types of cholesterol as well as total cholesterol concentration. Measuring different types of cholesterol and total cholesterol is important because significant amounts of cholesterol are in unmodified and esterified form due to the low solubility of cholesterol in water. Thus, in one example, a total cholesterol sample is provided in which a secondary enzyme is introduced into at least one enzyme domain, e.g., to provide a combination of cholesterol esterase and CHOX cholesterol ester, which essentially represents total cholesterol that can be measured indirectly from the signal of cholesterol transduction present and formed by the esterase.

In one example, the foregoing continuous (total) cholesterol sensor configuration is combined with any of the foregoing continuous alcohol sensor configurations and/or continuous uric acid sensor configurations to provide a continuous multi-analyte sensor system as further described below. The continuous multi-analyte sensor apparatus may also include continuous glucose monitoring capabilities. Other membrane configurations may be used in the aforementioned continuous cholesterol sensor configurations, such as one or more electrode domains, resistive domains, biological interface domains, and drug release membranes.

Bilirubin sensor and ascorbic acid sensor configuration

In one example, a continuous bilirubin and ascorbic acid sensor is provided. These sensors may employ bilirubin oxidase and ascorbate oxidase, respectively. However, unlike some oxidoreductases, the end product of the catalysis of the analytes for bilirubin oxidase and ascorbate oxidase is water rather than hydrogen peroxide. Thus, redox detection of hydrogen peroxide is not possible in association with bilirubin or ascorbic acid. However, these oxidases still consume oxygen for catalysis, and the level of oxygen consumption correlates with the level of target analyte present. Thus, bilirubin and ascorbic acid levels may be measured indirectly by electrochemical sensing of oxygen level changes, such as in a Clark-type electrode arrangement.

Alternatively, different configurations for sensing bilirubin and ascorbic acid may be employed. For example, an electrode field comprising one or more electrode fields comprising an electron transfer agent such as NAFION ^TM, a polyamphomer, or a polymer mediator may be coated on the electrode. The measured oxygen levels transduced from such enzyme domain configurations may be correlated with bilirubin levels and ascorbic acid levels. In one example, an electrode domain comprising one or more mediators electrically coupled to a working electrode may be employed, and the electrode domain may be associated with bilirubin levels and ascorbic acid levels.

In one example, the foregoing continuous bilirubin and ascorbic acid sensor configurations may be combined with any of the foregoing continuous alcohol sensor configurations, continuous uric acid sensor configurations, continuous cholesterol sensor configurations to provide a continuous multi-analyte sensor apparatus as described further below. The continuous multi-analyte sensor apparatus may also include continuous glucose monitoring capabilities. Other membranes may be used in the aforementioned continuous bilirubin and ascorbic acid sensor configurations, such as electrodes, resistors, biological interfaces, and drug release membranes.

Single working electrode configuration for dual analyte detection

In one example, at least a dual enzyme domain configuration is provided, wherein each layer contains one or more specific enzymes and optionally one or more cofactors. In a broad sense, one example of a continuous multi-analyte sensor configuration is depicted in fig. 9A, wherein a first membrane 855 (EZL 1) comprising at least one enzyme (enzyme 1) of at least two enzyme domain configurations is proximate to at least one surface of WE. One or more analyte-substrate enzyme pairs with enzyme 1 transduce at least one detectable signal to the WE surface by direct electron transfer or by mediator transfer that corresponds directly or indirectly to the analyte concentration. A second membrane 856 (EZL 2) having at least one second enzyme (enzyme 2) is positioned adjacent 855ELZ1 and is generally further away from WE than EZL 1. One or more resistive domains (RL) 852 may be provided adjacent EZL to 2 856 and/or between EZL1 855 and EZL2 856. Different enzymes catalyze the conversion of the same analyte, but at least one enzyme in EZL2 856 provides hydrogen peroxide and another enzyme in EZL1 855 does not. Thus, each measurable species (e.g., hydrogen peroxide and other measurable species that are not hydrogen peroxide) generates a signal that is related to its concentration.

For example, in the configuration shown in FIG. 9A, the first analyte diffuses through RL 852 and into EZL 2.2.856, generating peroxide via interaction with enzyme 2. The peroxide diffuses at least through EZL1 855 to WE and transduces a signal directly or indirectly corresponding to the concentration of the first analyte. A second analyte, different from the first analyte, diffuses through RL 852 and EZL2 856 and interacts with enzyme 1, which causes electrons to transfer to WE and transduce a signal that corresponds directly or indirectly to the concentration of the second analyte.

As shown in fig. 9B, the above configuration is adapted for wire electrode configurations in which at least two different enzyme-containing layers are configured on the same WE with a single active surface. In one example, the single WE is a wire, with the active surface positioned about the longitudinal axis of the wire. In another example, a single WE is a conductive trace on a substrate with an active surface positioned about a longitudinal axis of the trace. In one example, the active surface is substantially continuous about a longitudinal axis or radius.

In the above configuration, at least two different enzymes may be used and they catalyze the conversion of different analytes, wherein at least one enzyme in EZL2 856 provides hydrogen peroxide and at least another enzyme in EZL1 855 does not provide hydrogen peroxide, e.g., provides electron transfer to the WE surface directly or indirectly corresponding to the analyte concentration.

In one example, the inner layer of at least two enzyme domains EZL, EZL, 856 includes at least one immobilized enzyme in combination with at least one mediator that facilitates lower bias operation of the WE compared to the absence of the mediator. In one example, for such direct electronic transduction, potential P1 is used. In one example, at least a portion of the inner layer EZL is closer to the WE surface and may have one or more intervening electrode domains and/or cover interference and/or biological interfaces and/or drug release films, provided that at least one mediator may promote low bias operation of the WE surface. In another example, at least a portion of the inner layer EZL1 855 is directly adjacent to WE.

The second layer (outer layer EZL2 856) of the at least dual enzyme domain of fig. 9B contains at least one enzyme that results in one or more catalytic reactions that ultimately produce an amount of hydrogen peroxide that electrochemically transduces a signal corresponding to the analyte concentration. In one example, the generated hydrogen peroxide diffuses through layer EZL2 856 and through inner layer EZL1 855 to the WE surface and undergoes redox at the potential of P2, where p2+.p1. In this way, by controlling the potentials P1, P2 applied at the same WE surface, electron transfer and electrolysis (redox) can be selectively controlled. Any applied potential duration may be used for P1, P2, such as equal/periodic duration, staggered duration, random duration, and various potentiometric sequences, cyclic voltammetry, and the like. In some examples, impedance measurement sensing may be used. In one example, a phase shift (e.g., time delay) may be generated due to detecting two signals from two different working electrodes, each signal generated by a different EZL (EZL 1 855, EZL2 856) associated with each electrode. The two (or more) signals may be decomposed into components to detect a single signal and signal artifact generated by each of EZL1 855 and EZL2 856 in response to detection of two analytes. In some examples, each EZL detects a different analyte. In other examples, two EZL detect the same analyte.

In another alternative exemplary configuration, as shown in fig. 9C-9D, a multi-enzyme domain configuration as described above is provided for a continuous multi-analyte sensor apparatus using a single WE having two or more active surfaces. In one example, the multi-enzyme domain configuration discussed herein is formed on a planar substrate. In another example, a single WE is coaxial, e.g., configured as a wire, with two or more active surfaces positioned about a longitudinal axis of the wire. The additional wire may be used, for example, as a reference electrode and/or a counter electrode. In another example, a single WE is a conductive trace on a substrate, where two or more active surfaces are positioned about the longitudinal axis of the trace. At least a portion of the two or more active surfaces are discontinuous, providing at least two physically separated WE surfaces (e.g., WE1, WE 2) on the same WE wire or trace. In one example, the first analyte detected by WE1 is glucose and the second analyte detected by WE2 is lactate. In another example, the first analyte detected by WE1 is glucose and the second analyte detected by WE2 is a ketone.

Thus, fig. 9C-9D depict exemplary configurations of a continuous multi-analyte sensor configuration in which EZL1 855, EZL2 856, and RL 852 (resistive domains) as described above are arranged, for example, by sequential dip coating techniques, on a single coaxial wire comprising spatially separated electrode surfaces WE1, WE2. One or more parameters of the enzyme domains, the resistor domains, etc., such as thickness, length along the axis from the distal end of the wire, etc., may be independently controlled along the longitudinal axis of the WE. In one example, at least a portion of the spatially separated electrode surfaces have the same composition. In another example, at least a portion of the spatially separated electrode surfaces have different compositions. In fig. 9C to 9D, WE1 represents a first working electrode surface configured to operate, for example, under P1 and is electrically insulated from a second working electrode surface WE2 configured to operate under P2, and RE represents a reference electrode RE electrically isolated from both WE1, WE2. In the configuration of fig. 9C, one resistive domain is provided that covers the reference electrode and WE1, WE2. In the configuration of fig. 9D, an additional resistive domain is provided that substantially covers only WE2 and extends over it. Additional electrodes, such as counter electrodes, may be used. Such a configuration (whether a single wire or a two wire configuration) may also be used to measure the same analyte using two different techniques. Using different signal generation sequences and different RLs, the data collected from the two different modes of measurement provides increased fidelity, improved performance and device lifetime. Non-limiting examples are glucose oxidase (producing H2O 2) and glucose dehydrogenase (galvanic coupling) configurations. Measuring glucose from two different electrodes at two potentials provides more data points and accuracy. Such methods may not be necessary for glucose sensing, but may be applied to biomarker sensing spectra of other analytes, such as ketone sensing, ketone/lactate sensing, and ketone/glucose sensing, alone or in combination with glucose sensing.

In an alternative configuration depicted in fig. 9C-9D, two or more wire electrodes are presented that may be collinear, wrapped, or otherwise juxtaposed, with WE1 separated from WE2, e.g., from other elongate shape electrodes. The insulating layer electrically isolates WE1 from WE2. In this configuration, independent electrode potentials may be applied to the corresponding electrode surfaces, wherein independent electrode potentials may be provided to WE1, WE2 simultaneously, sequentially or randomly. In one example, the electrode potentials presented to the corresponding electrode surfaces WES1, WES2 are different. One or more additional electrodes may be present, such as a reference electrode and/or a counter electrode. In one example, WES2 is positioned longitudinally distal to WES1 in an elongate arrangement. WES1 and WES2 are coated with enzyme domains EZL1 and WES2 is coated with a different enzyme domain EZL2 using, for example, dip coating. Based on the impregnation parameters or enzyme domains of different thickness, multiple layers of enzyme domains may be employed, each layer independently comprising different loadings and/or compositions of enzyme and/or cofactor, mediator. Likewise, one or more resistive domains (RL) may be applied, each of which may have a different thickness along the longitudinal axis of the electrode, and on different electrodes and enzyme domains, for example by controlling the immersion length and other parameters. Referring to FIG. 9D, such an arrangement of RL 'is depicted, with the additional RL 852' adjacent to WES2, but not substantially in WES 1.

In one example of measuring two different analytes, the above configuration includes an enzyme domain EZL1 855 that includes one or more enzymes and one or more mediators for at least one enzyme of EZL1 to provide direct electron transfer to WES1 and determine the concentration of at least a first analyte. In addition, the enzyme domain EZL, 2856 may include at least one enzyme that provides peroxide (e.g., hydrogen peroxide) or consumes oxygen during catalysis with its substrate. The peroxide or oxygen generated in EZL2856 migrates to WES2 and provides a detectable signal that corresponds directly or indirectly to the second analyte. For example, WES2 may be carbon, wired to glucose dehydrogenase to measure glucose, while WES1 may be platinum, which measures the peroxide produced by lactate oxidase/lactate in EZL2 856. The combination of electrode materials and enzymes as disclosed herein is exemplary and not limiting.

In one example, the potentials of P1 and P2 can be separated by an amount of potential such that the two signals (from direct electron transfer from EZL1 855 and from hydrogen peroxide redox at WE) can be activated and measured separately. In one example, the electronic module of the sensor may continuously switch between two sensed potentials in a continuous or semi-continuous periodic manner, e.g., a period (t 1) at potential P1 and a period (t 2) at potential P2, optionally with a rest time when no potential is applied. The extracted signals may then be analyzed to measure the concentration of two different analytes. In another example, the electronics module of the sensor may undergo cyclic voltammetry, providing a current change when sweeping the potential of P1 and P2 may be related to the transduction signal from direct electron transfer or hydrogen peroxide electrolysis, respectively. In one example, the sensed pattern is non-limiting and may include different amperometric techniques, such as cyclic voltammetry. In one example, an alternative configuration is provided, but hydrogen peroxide production in EZL is replaced by another suitable electrolytic compound (such as oxygen) that maintains a p2+.p1 relationship and at least one enzyme-substrate combination that provides another electrolytic compound.

For example, a continuous multi-analyte sensor configuration for choline and glucose was prepared in which enzyme domains EZ1 855, EZ2 856 were associated with different WE (e.g., platinum WE2 and gold WE 1). In this exemplary case EZL1 855 contains glucose oxidase and a mediator coupled to WE1 to promote direct transfer of electrons upon glucose catalysis, and EZL2 856 contains choline oxidase, which will catalyze choline and generate hydrogen peroxide for electrolysis at WE 2. EZL' are coated with a resistive domain; after solidification and readiness, they undergo cyclic voltammetry in the presence of glucose and choline. Glucose oxidase wired to the gold electrode was able to transduce signals at 0.2 volts, and thus, by analyzing the current change at 0.2 volts, glucose concentration could be determined. The data also show that if the CV trace is analyzed at voltage P2, choline concentration can also be detected inferentially at WE2 platinum electrode.

In one example, the electrode WE1 or WE2 can be, for example, a composite material, such as a gold electrode, with platinum ink, a carbon/platinum mixture deposited on top, and/or trace carbon on top of platinum, or a porous carbon coating on the platinum surface. In one example, the electrode surface contains two different materials, e.g., carbon for the ligase and electron transfer, while platinum may be used for hydrogen peroxide redox and detection. As shown in fig. 9E, an example of such a composite electrode surface is shown, where an extended platinum-covered wire 857 is half coated with carbon 858 to facilitate multi-sensing on two different surfaces of the same electrode. In one example, WE2 can be grown on or extend from a portion of the surface or distal end of WE1, such as by vapor deposition, sputtering, or electrolytic deposition, or the like.

Additional examples include composite electrode materials that can be used to form one or both of WE1 and WE 2. In one example, a platinum-carbon electrode WE1 comprising EZL a with glucose dehydrogenase is wired to the carbon surface, and the outer EZL a lactate oxidase that generates hydrogen peroxide that is detectable by the platinum surface of the same WE1 electrode. Other examples of this configuration may include ketone sensing (β -hydroxybutyrate dehydrogenase electrocoupling enzyme in EZL 1855) and glucose sensing (glucose oxidase in EZL2 856). Other membranes may be used in the foregoing configurations, such as electrodes, resistors, biological interfaces, and drug release membranes. In other examples, one or both of the working electrodes (WE 1, WE 2) may be gold-carbon (Au-C), palladium-carbon (Pd-C), iridium-carbon (Ir-C), rhodium-carbon (Rh-C), or ruthenium-carbon (Ru-C). In some examples, the carbon in the working electrode discussed herein may alternatively or additionally include graphene, graphene oxide, or other materials suitable for forming the working electrode, such as commercially available carbon inks.

Glycerol sensor configuration

As shown in fig. 10A, an exemplary continuous glycerol sensor configuration is depicted in which a first enzyme domain EZL, comprising galactose oxidase, is located proximate to at least a portion of the WE surface. The second enzyme domain EZL2 861, comprising glucose oxidase and catalase, is located further away from WE. As shown in fig. 10A, one or more resistive domains (RL) 852 are positioned between EZL a1 860 and EZL a2 861. Additional RL may be employed, for example adjacent EZL2 861. Modifications to one or more RL membranes are contemplated to attenuate the flux of either analyte and increase the sensitivity ratio of glycerol to galactose. The glycerol sensing configurations described above provide a glycerol sensor that can be combined with one or more additional sensor configurations as disclosed herein.

Glycerol may be catalyzed by galactose oxidase (GalOx), however, galOx has an activity ratio of 1% to 5% on glycerol. In one example, galOx's activity on the secondary analyte glycerol may be utilized. The relative concentration of glycerol in the body is much higher than galactose (galactose is about 2umol/l and glycerol is about 100 umol/l), which complements the above configuration.

If GalOx present in the EZL1 860 membrane has no additional functional limitations, galOx will catalyze the passage of most, if not all, of the glycerol of one or more RL. The signal contribution from the glycerol present will be higher than the signal contribution from galactose. In one example, one or more RL's are chemically configured to provide a higher glycerol inflow or a lower galactose inflow.

In another example, a plurality of working electrodes WE are used to provide a glycol sensor configuration that utilizes signals transduced from both WE. The use of signals transduced from both WEs can provide increased selectivity. In one example, EZL1 860 and EZL2 861 include the same oxidase (e.g., galactose oxidase) with different enzyme loading ratios, and/or different immobilized polymers and/or different numbers and layers of RL' on WE. Such a configuration provides for measurement of the same target analyte with different sensitivities, resulting in a dual measurement. Using a mathematical algorithm to correct noise and interference from the first signal and inputting the first signal from one sensing electrode having a first analyte sensitivity ratio into the mathematical algorithm allows for separation of the second signal corresponding to the desired analyte contribution. Modification of the sensitivity ratio of the one or more EZL 'can be provided by adjusting one or more of the enzyme source, the enzyme loading in EZL', the chemical/diffusion characteristics of EZL ', the chemical/diffusion characteristics of the at least one RL', and combinations thereof to distinguish signals from interfering species and analytes of interest.

As discussed herein, the secondary enzyme domains can be used to catalyze non-target analytes, reduce their concentration and limit diffusion through adjacent membranes containing the primary enzyme and necessary additives toward the sensing electrode. In this example, the distal-most enzyme domain EZL2 861 is configured to catalyze non-target analytes that would otherwise react with EZL1, providing a potentially less accurate reading of target analyte (glycerol) concentration. The secondary enzyme domains may themselves act as "selective diffusion barrier" or may be placed above or below the Resistive Layer (RL) 852 in some other configuration. In this example, the target analyte is glycerol and GalOX is used to catalyze the glycerol to form a measurable species (e.g., hydrogen peroxide).

In one example, a continuous glycerol sensor configuration is provided using at least glycerol oxidase, which provides hydrogen peroxide upon glycerol reaction and catalysis. Thus, in one example, an enzyme domain comprising glycerol oxidase can be located adjacent to at least a portion of the WE surface and amperometric detection of hydrogen peroxide is used. In another example, an enzyme domain comprising glycerol oxidase is used to sense oxygen level changes, such as in a Clark-type electrode arrangement. Alternatively, at least a portion of the surface of the WE may be coated with one or more layers of an electrically coupled polymer, such as a mediator system discussed below, to provide a coated WE capable of transferring electrons from the enzyme at a lower potential. The coated WE can then be operated at a different and lower voltage to measure oxygen and its correlation with glycerol concentration.

In another example, a glycerol sensor configuration is provided using a glycerol-3-phosphate oxidase in the enzyme domain. In one example, ATP is used as a cofactor. Thus, as shown in fig. 10B and 10C, an exemplary sensor configuration is depicted in which, in one example (fig. 10B), one or more cofactors (e.g., ATP) 862 are proximate to at least a portion of the WE surface. One or more enzyme domains 863 comprising glycerol-3-phosphate oxidase (G3 PD), lipase and/or Glycerol Kinase (GK) and one or more regenerases contained in the enzyme domains capable of continuously regenerating cofactors are adjacent to the cofactors or further from the WE surface than the cofactor layer 862. Examples of regenerants that may be used to provide ATP regeneration include, but are not limited to, ATP synthase, pyruvate kinase, acetate kinase, and creatine kinase. The one or more regenerating enzymes may be included in one or more enzyme domains, or in separate layers.

An alternative configuration is shown in fig. 10C, wherein one or more enzyme domains 863 comprising G3PD, at least one cofactor, and at least one regenerating enzyme are located proximate to at least a portion of the WE surface, wherein one or more cofactor reservoirs 862 are located adjacent to the enzyme domains comprising G3PD and further away from the WE surface, and one or more RL'852 are located adjacent to the cofactor reservoirs. In any of these configurations, an additional enzyme domain (which includes a lipase) may be included to indirectly measure triglycerides, as the lipase will produce glycerol for detection by the aforementioned glycerol sensor configuration.

In another example, a dehydrogenase is used with cofactors and a regenerating enzyme to provide a glycerol sensor configuration. In one example, cofactors that may be bound into one or more enzyme domains include one or more of NAD (P) H, NADP + and ATP. In one example, for the use of NAD (P) H, the regenerating enzyme may be NADH oxidase or diaphorase to convert NADH (a product catalyzed by dehydrogenase) back to NAD (P) H. Similar methods can be used to produce other glycerol sensors, e.g., glycerol dehydrogenase, in combination with NADH oxidase or diaphorase, which can be configured to measure glycerol or oxygen.

In one example, mathematical modeling may be used to identify and remove interfering signals, measure very low analyte concentrations, signal errors, and noise reduction, in order to improve and increase the useful life of the multi-analyte sensor. For example, for a dual WE electrode configuration, where WE1 is coated with a first EZL and WE2 is coated with two or more different EZL, optionally with one or more resistive domains (RL), such disturbances can be mathematically corrected to provide increased accuracy of the measurement.

Variations in enzyme loading, immobilized polymer, and resistive domain characteristics on each analyte sensing region can result in different sensitivity ratios between two or more target analytes and interfering substances. If mathematical modeling is used to collect and analyze the signals, a more accurate concentration of the target analyte can be calculated.

One example that may be helpful using mathematical modeling is glycerol sensing, where galactose oxidase is sensitive to both galactose and glycerol. The sensitivity ratio of galactose oxidase to glycerol is about 1% to 5% of its sensitivity to galactose. In such cases, the sensitivity ratio to the two analytes may be modified by adjusting one or more parameters, such as enzyme source, enzyme loading, enzyme domain (EZL) diffusion characteristics, RL diffusion characteristics, and combinations thereof. If two WEs are operating in the sensor system, correcting and analyzing the signals from the two WEs using mathematical modeling provides a high degree of fidelity and target analyte concentration measurement.

In the above-described configuration, the proximity of one or more of the enzyme immobilization layers discussed herein to the WE may be different or opposite, for example, if the enzyme immobilization layer closest to the WE enzyme domain provides hydrogen peroxide, this configuration may be used.

In some examples, one or more cooperating enzymes may be used to measure the target analyte. In one example, ATP may be immobilized in one or more EZL membranes, or may be added to an adjacent layer alone or in combination with a second cofactor, or may be regenerated/recycled for use in the same EZL or adjacent third EZL. The configuration may also include a cofactor regeneration enzyme, such as an alcohol dehydrogenase or NADH oxidase, to regenerate NAD (P) H. Other examples of cofactor regenerating enzymes that can be used for ATP regeneration are ATP synthase, pyruvate kinase, acetate kinase, creatine kinase, etc.

In one example, the foregoing continuous glycerol sensor configuration may be combined with any of the foregoing continuous alcohol sensor configuration, continuous uric acid sensor configuration, continuous cholesterol sensor configuration, continuous bilirubin/ascorbic acid sensor configuration, ketone sensor configuration, choline sensor configuration to provide a continuous multi-analyte sensor apparatus as described further below. The continuous multi-analyte sensor apparatus may also include continuous glucose monitoring capabilities. Other configurations may be used in the aforementioned continuous glycerol sensor configurations, such as electrodes, resistors, biological interfaces, and drug release films.

Creatinine sensor configuration

In one example, a continuous creatinine sensor configuration is provided, such configuration containing one or more enzymes and/or cofactors. Creatinine sensor configurations are examples of continuous analyte sensing systems that generate intermediates, interfering products, where these intermediates/interferents are also present in the sampled biological fluid. The present disclosure provides solutions to these technical problems and provides accurate, stable and continuous creatinine monitoring alone or in combination with other continuous multi-analyte sensor configurations.

Creatinine sensors undergo a number of physiologically-present changes in intermediates/interfering products (e.g., sarcosine and creatine) that can affect the correlation of the transduction signal with creatinine concentration when used. For example, the physiological concentration range of creatine is an order of magnitude lower than creatine or creatine, so the signal contribution from circulating creatine is typically minimal. However, changes in local physiological creatine concentration can affect the creatinine sensor signal. In one example, such signal contributions are eliminated or reduced.

Thus, in one example, eliminating or reducing the creatine signal contribution of the creatinine sensor includes using at least one enzyme that will consume a non-target interfering analyte (in this case creatine). For example, two enzyme domains located adjacent to each other are used. At least a portion of the first enzyme domain is positioned proximate at least a portion of the WE surface, the first enzyme domain comprising one or more enzymes selected from creatinine amidohydrolase (CNH), creatinine amidohydrolase (CRH), and Sarcosine Oxidase (SOX). The second enzyme domain, adjacent to the first enzyme domain and further from the surface of the WE, includes one or more enzymes that use creatine as their substrate in order to eliminate or reduce the diffusion of creatine towards the WE. In one example, the combination of enzymes includes CRH, SOX, creatine kinase, and catalase, where the enzyme ratio is adjusted to provide a sufficient number of units such that circulating creatine will be at least partially consumed by CRH, providing sarcosine and urea, and the produced sarcosine will be at least partially consumed by SOX, providing glycine (e.g., glycine aldehyde) in an oxidized form that will be at least partially consumed by catalase. In an alternative configuration described above, urea produced by CRH catalysis may be at least partially consumed by urease to provide ammonia, with the aqueous form (nh4+) detected via an ion selective electrode (e.g., a non-actin ionophore). Such alternative potentiometric sensing configurations may provide alternatives to amperometric peroxide detection (e.g., improved sensitivity, detection limits, and lossless, alternative paths/mechanisms for the reference electrode). The dual analyte sensing example may include a creatinine-potassium sensor with potentiometric sensing at two different working electrodes. In this example, the interfering signal may be identified and corrected. In an alternative example, the foregoing configuration may include a multi-mode sensing architecture that uses a combination of amperometric and potentiometric methods to detect the concentration of peroxide and ammonium ions, which are measured using amperometric and potentiometric methods, respectively, and associated with measuring creatinine concentration. In one example, the foregoing configuration may further include one or more configurations (e.g., enzyme-free) that separate two enzyme domains to provide complementary or auxiliary diffusion separation and barrier.

In yet another example, a method of separating the signal and measuring substantially only creatinine is to use a second WE that measures interfering substances (e.g., creatine) and then correct the signal using mathematical modeling. Thus, for example, a signal from WE interacting with creatine is used as a reference signal. The signal from another WE that interacted with creatinine was corrected based on the signal from WE that interacted with creatine to selectively determine creatinine concentration.

In yet another example, sensing creatinine is by electrochemical measurement of oxygen level changes, such as in a Clark-type electrode set-up, or using one or more electrodes coated with layers of different polymers (such as NAFION ^TM) and correlating potential changes based on oxygen changes, which would be indirectly related to creatinine concentration.

In yet another example, sensing creatinine is through the use of a sarcosine oxidase that is wired to at least one WE using one or more electrically coupled mediators. In this method, the creatinine concentration will be indirectly related to the signal generated by electron transfer collected from the WE.

For the foregoing creatinine sensor configuration based on hydrogen peroxide and/or oxygen measurements, the one or more enzymes may be in a single enzyme domain, or the one or more enzymes may be independently in one or more enzyme domains, or any other combination thereof, wherein at least one enzyme is present in each layer. For the foregoing creatinine sensor configuration based on the use of an electrically coupled sarcosine oxidase-containing layer that is positioned adjacent to the electrode and electrically coupled to at least a portion of the electrode surface using a mediator.

In another example, the foregoing creatinine sensor configuration may be sensed using potentiometry using Urease (UR) that produces ammonium from urea, which is produced from creatine through CRH, which is formed by the interaction of creatinine with CNH. Thus, ammonium can be measured by the above configuration and correlated to creatinine concentration. Alternatively, creatinine amidohydrolase (CI) or creatinine deiminase may be used to generate ammonia gas that will provide ammonium ions for signal transduction under physiological conditions of the transdermal sensor.

In yet another example, sensing creatinine is through the use of one or more enzymes and one or more cofactors. Some non-limiting examples of such configurations include Creatinine Deaminase (CD) that provides ammonium from creatinine, glutamate dehydrogenase (GLDH) that provides peroxide from ammonium, wherein hydrogen peroxide is associated with current levels of creatinine. The above configuration may further comprise a third enzyme: glutamate oxidase (GLOD) to further break down glutamate formed by GDLH and produce additional hydrogen peroxide. The combination of such enzymes may be independently in one or more enzyme domains or any other combination thereof, wherein at least one enzyme is present in each domain or layer.

In yet another example, sensing creatinine is by a combination of creatinine amidohydrolase (CNH), creatine Kinase (CK), and Pyruvate Kinase (PK), wherein pyruvate produced by the PK is detectable by one or more of Lactate Dehydrogenase (LDH) or Pyruvate Oxidase (POX) configured independently, wherein one or more of the foregoing enzymes is present in one layer, or wherein each of the plurality of layers comprises at least one enzyme; any other combination thereof.

In such sensor configurations using one or more cofactors and/or regenerases for the cofactors, one or more of excess NADH, NAD (P) H, and ATP may be provided in any one of the one or more configurations, and one or more diffusion resistance domains may be introduced to limit or prevent the cofactors from flowing out of their respective membranes. Other configurations may be used in the foregoing configurations, such as electrodes, resistors, biological interfaces, and drug release films.

In yet another example, creatinine detection is provided by using creatinine deiminase in one or more enzyme domains and providing ammonium to the enzyme domains via catalysis of creatinine. Ammonium ions can then be detected by potentiometry or by using a composite electrode that undergoes redox when exposed to ammonium ions (e.g., NAFION ^TM/polyaniline composite electrode), wherein polyaniline undergoes redox when ammonium is present at the electrode at potential. The ammonium concentration can then be correlated to the creatinine concentration.

Fig. 11 depicts an exemplary continuous sensor configuration for creatinine. In the example of fig. 11, the sensor includes a first enzyme domain 864 that includes CNH, CRH, and SOX adjacent to a working electrode WE (e.g., platinum). The second enzyme domain 865 is located adjacent to the first enzyme domain and further away from the WE. One or more resistive domains (RL) 852 can be positioned adjacent to or between the first layer and the second layer. Creatinine can diffuse through RL and the second enzyme domain to the first enzyme domain where it is converted to peroxide and converted to a signal corresponding to its concentration. Creatine can diffuse through RL and be converted to sarcosine and urea in the second enzyme domain, with the sarcosine being consumed by sarcosine oxidase and the peroxide produced being consumed by catalase, preventing the transduction of the creatine signal.

For example, variations of the above configuration are possible for continuous monitoring of creatinine alone or in combination with one or more other analytes. Thus, an alternative method of sensing creatinine may be to electrochemically sense oxygen level changes, such as in a Clark-type electrode arrangement. In one example, WE can be coated with layers of different polymers (such as NAFION ^TM), and creatinine concentration can be correlated based on changes in the possible oxygen changes. In yet another example, one or more mediators may be used to "wire" one or more enzymes closest to WE (i.e., sarcosine oxidase) to the electrode. Each of the different enzymes in the above configuration may be distributed within a polymer matrix or domain to provide one enzyme domain. In another example, one or more of the different enzymes discussed herein may be formed as an enzyme domain and may be formed layer by layer, with at least one enzyme present in each layer. In the example of a "wired" enzyme configuration with a multilayer membrane, the wired enzyme domain would be closest to the electrode. One or more interferent layers may be deposited in a multi-layer enzyme configuration to prevent non-target analytes from reaching the electrodes.

In one example, the foregoing continuous creatinine sensor configuration may be combined with any of the foregoing continuous alcohol sensor configuration, continuous uric acid sensor configuration, continuous cholesterol sensor configuration, continuous bilirubin/ascorbic acid sensor configuration, ketone sensor configuration, choline sensor configuration, glycerol sensor configuration to provide a continuous multi-analyte sensor apparatus as described further below. The continuous multi-analyte sensor apparatus may also include continuous glucose monitoring capabilities.

Lactose sensor arrangement

In one example, a continuous lactose sensor configuration is provided, alone or in combination with another analyte sensing configuration that includes one or more enzymes and/or cofactors. In a general sense, lactose sensing configurations using at least one enzyme domain comprising lactase are used to produce glucose and galactose from lactose. The glucose or galactose produced is then enzymatically converted to peroxide for signal transduction at the electrodes. Thus, in one example, at least one enzyme domain EZL, including lactase, is located proximate to at least a portion of the WE surface capable of electrolyzing hydrogen peroxide. In one example, glucose Oxidase (GOX) is included in EZL, with one or more cofactors or electrically coupled mediators. In another example, galactose oxidase (GalOx) is included in EZL, optionally with one or more cofactors or mediators. In one example, both glucose oxidase and galactose oxidase are included in EZL. In one example, both glucose oxidase and galactose oxidase are included in EZL a1, optionally with one or more cofactors or electrically coupled mediators.

One or more additional EZL' (e.g., EZL 2) may be positioned adjacent to EZL1, wherein at least a portion of EZL is farther from at least a portion of WE than EZL 1. In one example, one or more layers may be positioned between EZL and EZL, such layers may include, or be substantially free of, one or more of an enzyme, cofactor, or mediator. In one example, one or more layers positioned between EZL and EZL are substantially free of enzymes, e.g., no intentionally added enzymes. In one example, one or more layers may be positioned adjacent EZL2 farther from at least a portion of EZL than EZL and include one or more of the enzymes present in EZL or EZL 2.

In one example of the foregoing lactose sensor configurations, the peroxide-producing enzyme may be electrically coupled to the electrode using a coupling mediator. The transduced peroxide signal from the aforementioned lactose sensor configuration may be correlated with the level of lactose present.

Fig. 12A-12D depict alternative continuous lactose sensor configurations. Thus, in the enzyme domain EZL1 864 closest to WE (G1) comprising GalOx and lactase, a lactose sensor is provided that is sensitive to galactose and lactose concentration changes and does not substantially transduce glucose concentration. As shown in fig. 12B-12D, additional layers are used, including an enzyme-free layer 859 and a lactase-containing layer 865, and optionally electrodes, resistors, biological interfaces, and drug release films (not shown). Since the change in physiological galactose concentration is small, the transduction signal is substantially derived from physiological lactose fluctuations.

In one example, the foregoing continuous lactose sensor configuration may be combined with any of the foregoing continuous alcohol sensor configuration, continuous uric acid sensor configuration, continuous cholesterol sensor configuration, continuous bilirubin/ascorbic acid sensor configuration, ketone sensor configuration, choline sensor configuration, glycerol sensor configuration, creatinine sensor configuration to provide a continuous multi-analyte sensor apparatus as further described below. The continuous multi-analyte sensor apparatus may also include continuous glucose monitoring capabilities. Other membranes may be used in the foregoing sensor configurations, such as electrodes, resistors, biological interfaces, and drug release membranes.

Urea sensor configuration

Similar methods as described above may also be used to produce a continuous urea sensor. For example, urease (UR), which can break down urea and provide ammonium, can be used in an enzyme domain configuration. Ammonium can be detected potentiometrically or by using a composite electrode (e.g., an electrode that undergoes redox when exposed to ammonium). Exemplary electrodes for ammonium signal transduction include, but are not limited to, NAFION ^TM/polyaniline composite electrodes, wherein polyaniline undergoes redox in the presence of ammonium at an applied potential, wherein the signal is substantially directly related to the level of ammonium present in the surroundings. The method may also be used to measure other analytes, such as glutamate, using Glutaminase (GLUS).

In one example, the foregoing continuous uric acid sensor configuration may be combined with any of the foregoing continuous alcohol sensor configuration and/or continuous uric acid sensor configuration and/or continuous cholesterol sensor configuration and/or continuous bilirubin/ascorbic acid sensor configuration and/or continuous ketone sensor configuration and/or continuous choline sensor configuration and/or continuous glycerol sensor configuration and/or continuous creatinine sensor configuration and/or continuous lactose sensor configuration to provide a continuous multi-analyte sensor apparatus as further described below. The continuous multi-analyte sensor apparatus may also include continuous glucose monitoring capabilities. Other membranes may be used in the aforementioned uric acid sensor configurations, such as electrodes, resistors, biological interfaces, and drug release membranes.

In certain embodiments, the continuous analyte monitoring system 104 may be a lactate sensor, as discussed with reference to fig. 1. Fig. 13A-14C depict exemplary lactate sensor systems for measuring lactate in accordance with certain embodiments of the present disclosure.

Fig. 13A shows an exemplary embodiment of the physical structure of lactate sensor 1338. In this embodiment, radial windows 1303 are formed through insulating layer 1305 to expose the electroactive working electrode of conductor material 1304. Although fig. 13A shows a coaxial design, any form factor or shape, such as a flat plate, may alternatively be used. Rathee et al describe various lactate sensor designs. "biosensor based on electrochemical lactate detection: comprehensive review (Biosensors based on electrochemical lactate detection: A comprehensive review) "," report on biochemistry and biophysics (Biochemistry and Biophysics Reports), "pages 35-54, 5 (2016), and Rasaei et al," lactate biosensor: current state and prospect (Lactate Biosensors: current status and outlook) "," analytical and biological analytical chemistry (ANALYTICAL AND Bioanalytical Chemistry) ", month 9 of 2013, both of which are incorporated herein by reference in their entirety.

FIG. 13B is a cross-sectional view of the electroactive section of the exemplary sensor of FIG. 13A, showing the exposed electroactive surface of the working electrode surrounded by the sensing film in one embodiment. Such sensing membranes are found in a variety of lactate sensor designs. As shown in fig. 13B, a sensing film can be deposited on at least a portion of the electroactive surface of the sensor (working electrode and optional reference electrode) and protect the exposed electrode surface from biological environments, diffusion resistance of analytes, catalysts capable of enzymatic reactions, restriction or blocking of interferents, and/or hydrophilicity on the electrochemically reactive surface of the sensor interface.

Thus, the sensing membrane may include multiple domains, e.g., electrode domain 1307, interference domain 1308, enzyme domain 1309 (e.g., including lactate oxidase), and resistive domain 1300, and may include a high oxygen solubility domain and/or a bio-protective domain (not shown). The film system may be deposited on the exposed electroactive surface using known film techniques (e.g., spraying, electrodeposition, dipping, etc.). In one embodiment, one or more domains are deposited by immersing the sensor in a solution and pulling the sensor out at a rate that provides the appropriate domain thickness. However, the sensing film may be disposed over (or deposited on) the electroactive surface using any known method as will be appreciated by those skilled in the art.

The sensing membrane typically includes an enzyme domain 1309 that is disposed farther from the electroactive surface than either the interference domain 1308 or the electrode domain 1307. In some embodiments, the enzyme domains are deposited directly on the electroactive surface. In a preferred embodiment, enzyme domain 1309 provides an enzyme, such as lactose oxidase, to catalyze the reaction of an analyte with its co-reactant.

The sensing membrane may also include a resistive domain 1300 that is disposed further from the electroactive surface than the enzyme domain 1309 because there is a molar excess of lactate relative to the amount of oxygen in the blood. However, it is preferable to supply a non-rate limiting excess of oxygen to an enzyme-based sensor that uses oxygen as a co-reactant so that the sensor responds accurately to changes in analyte concentration, rather than the reaction failing to utilize the analyte present due to the lack of oxygen co-reactant. This has been found to be a problem for glucose concentration monitors and is also responsible for the inclusion of resistive domains. In particular, when the glucose monitoring reaction is an oxygen limited reaction, linearity cannot be achieved above the minimum concentration of glucose. Without a semipermeable membrane over the enzyme domain to control the flow of glucose and oxygen, a linear response to glucose levels can only be obtained at glucose concentrations up to about 2 or 3 mM. However, in a clinical setting, a linear response to glucose levels up to at least about 20mM is desirable. To accurately determine higher glucose levels, the resistive domain in the glucose monitoring environment may be 200 times more permeable to oxygen than glucose. This allows a sufficiently high oxygen concentration to make the glucose concentration a determinant of the detected electrochemical reaction rate.

In some embodiments, the resistive domains may be thinner and the analyte to oxygen permeability difference is smaller for lactate sensors described herein, e.g., oxygen to lactate permeability is 50:1 or 10:1. In some embodiments, this makes the lactate sensor more sensitive to low lactate levels, e.g., 0.5mM or as low as 3 or 4 mM. The resistive domain may be configured such that lactate is the rate-limiting reactant at 3mM or less lactate, thus allowing accurate threshold detection at about 2 mM. The resistive domain may also be configured to allow oxygen to be the rate limiting reactant at lactate concentrations greater than 10 mM. In some embodiments, these ranges may be further narrowed, for example, the resistive domains may be configured such that lactate is the rate-limiting reactant at lactate concentrations of 4mM or less, and such that oxygen is the rate-limiting reactant at lactate concentrations of greater than 6 mM. In this way, the sensor itself may be optimized for early sepsis detection. It is also understood that other analyte sensors besides lactate may be combined with the lactate sensors described herein, such as sensors suitable for ketones, ethanol, glycerol, glucose, hormones, viruses, or any other biological component of interest.

Fig. 14A-14C illustrate an exemplary embodiment of a sensor system 104 implemented as a wearable device, such as an on-skin sensor assembly 1400. As shown in fig. 14A-14B, the on-skin sensor assembly includes a housing 1428. Adhesive patch 1426 may couple housing 1428 to the skin of the recipient. The adhesive 1426 may be a pressure sensitive adhesive (e.g., acrylic, rubber-based, or other suitable type) that adheres to a carrier substrate (e.g., a hydroentangled polyester, polyurethane film, or other suitable type) for skin attachment. The housing 1428 can include a through-hole 1480 that mates with a sensor inserter device (not shown) for implanting the sensor 1338 under the skin of a subject.

The wearable sensor assembly 1400 includes sensor electronics 1435 operable to measure and/or analyze lactate concentration indicators sensed by the lactate sensor 1438. As shown in fig. 14C, in this embodiment, the sensor 1338 extends from its distal end up into the through hole 1480 and is routed to sensor electronics 1435, which are typically mounted on a printed circuit board 1435 within the housing 1428. The sensor electrodes are connected to sensor electronics 1435. These types of analyte monitors are currently used in commercially available blood glucose monitoring systems for diabetics, and the design principles used herein may also be used in lactate monitors.

The housing 1428 of the sensor assembly 1400 may include a user interface for delivering a message regarding the sepsis status to the patient. Because in some instances lactate sensors described herein may not be monitors that the patient will wear regularly as glucose monitors, in such instances they may not need to include many of the features present in other monitor types, such as periodic wireless transmission of analyte concentration data. Thus, a simple user interface may be implemented that only delivers alerts. In some embodiments, the user interface may be a single Light Emitting Diode (LED) that emits light when the sensor electronics determine that there is a risk of sepsis. The two LEDs or bi-colored LEDs may be green when the monitor is running and low risk is detected, and red when the risk of sepsis is detected and a warning is issued. If the measured value returns to a value appropriate for the output, the monitor may be configured to return to a green or low risk state. To provide additional flexibility in delivering information to the patient (e.g., error information, time remaining to wear the device, etc.), a simple dot-matrix character display (e.g., less than 200 pixels per side or a configurable 20-character LCD) may be used, which is still inexpensive and power-efficient.

Other considerations

The methods disclosed herein comprise one or more steps or actions for achieving these methods. The method steps and/or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specified, the order and/or use of specific steps and/or actions may be modified without departing from the scope of the claims.

As used herein, a phrase referring to "at least one" of a list of items refers to any combination of those items, including individual members. For example, "at least one of a, b, or c" is intended to encompass a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with a plurality of the same elements (e.g., a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b-b, b-b-c, c-c, and c-c, or any other ordering of a, b, and c).

The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language of the claims, wherein reference to an element in the singular is not intended to mean "one and only one" unless specifically so stated, but rather "one or more". The term "some" means one or more unless specifically stated otherwise. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Furthermore, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element should be construed in accordance with the provision of 35u.s.c. ≡112 (f) unless explicitly stated using the phrase "means for … …" or, in the case of method claims, using the phrase "step for … …".

While various examples of the present invention have been described above, it should be understood that they have been presented by way of example only, and not limitation. Likewise, the figures may depict exemplary architectures or other configurations of the present disclosure to aid in understanding the features and functionality that may be included in the present disclosure. The disclosure is not limited to the exemplary architecture or configuration shown, but may be implemented using a variety of alternative architectures and configurations. Additionally, while the disclosure has been described above in terms of various exemplary examples and aspects, it should be understood that the various features and functions described in one or more of the various examples are not limited in their applicability to the particular examples described. They may be applied singly or in some combination to one or more other examples of the present disclosure, whether or not such examples are described, and whether or not such features are presented as part of the described examples. Accordingly, the breadth and scope of the present disclosure should not be limited by any of the above-described exemplary examples.

All references cited herein are incorporated by reference in their entirety. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and/or take precedence over any such contradictory material.

Unless otherwise defined, all terms (including technical and scientific terms) will be given their ordinary and customary meaning to those skilled in the art and are not limited to the specific or customized meaning unless clearly defined herein.

Unless explicitly stated otherwise, the terms and phrases used in the present application and their variants, particularly those used in the appended claims, should be construed as open ended, not limiting. As an example of the foregoing, the term "comprising" should be understood to mean "including but not limited to", etc.; as used herein, the term "comprising" is synonymous with "including," "containing," or "characterized by," and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps; the term "having" should be interpreted as "having at least"; the term "comprising" should be interpreted as "including but not limited to"; the term "example" is used to provide an illustrative example of the item in question, not an exhaustive or limiting list thereof; adjectives such as "known," "normal," "standard," and terms of similar meaning should not be construed as limiting the item being described to a given time period or to an item being available at a given time, but rather should be construed as encompassing known, normal, or standard technologies that are available or known at any time now or in the future; and the use of terms such as "preferred" or "expected" and words of similar import should not be taken to imply that certain features are critical, essential, or even important to the structure or function of this application, but rather to emphasize alternative or additional features that may or may not be utilized in a particular example of the application. Likewise, a group of items linked with the conjunction "and" should not be construed as requiring that each of these items be present in the grouping, but rather should be construed as "and/or" unless expressly stated otherwise. Similarly, unless explicitly stated otherwise, a group of items linked with the conjunction "or" should not be construed as requiring mutual exclusivity among that group, but rather as "and/or".

As used herein, the term "comprising" is synonymous with "including," "containing," or "characterized by," and is inclusive or open-ended, and does not exclude additional, unrecited elements or method steps.

All numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification are to be understood as being modified in all instances by the term "about". Accordingly, unless indicated to the contrary, the numerical parameters set forth herein are approximations that may vary depending upon the desired properties sought to be obtained. In any application where priority is claimed, each numerical parameter should be construed in light of the number of significant digits and ordinary rounding techniques and is not intended to limit the application of the doctrine of equivalents to the scope of any claim.

Furthermore, although the foregoing has been described in some detail by way of illustration and example for purposes of clarity and understanding, it will be apparent to those skilled in the art that certain changes and modifications may be practiced. Therefore, the specification and examples should not be construed as limiting the scope of the invention to the particular embodiments and examples described herein, but rather as covering all modifications and alternatives that may fall within the true scope and spirit of the invention.

Claims

1. A monitoring system, the monitoring system comprising:

a continuous analyte sensor configured to generate an analyte measurement associated with an analyte level of a patient; and

A sensor electronics module coupled to the continuous analyte sensor and configured to receive and process the analyte measurements.

2. The monitoring system of claim 1, wherein the continuous analyte sensor comprises:

an electroactive working electrode of a conductor material configured to be inserted into the skin of the patient, wherein the electroactive working electrode is surrounded by a sensing membrane for sensing the analyte level.

3. The monitoring system of claim 1, wherein:

The continuous analyte sensor is a continuous lactate sensor, and

The analyte measurement includes a lactate measurement.

4. The monitoring system of claim 3, further comprising:

A memory, the memory comprising executable instructions;

One or more processors in data communication with the sensor electronics module and configured by the executable instructions to:

Receiving analyte data from the sensor electronics module, the analyte data comprising the lactate measurement associated with at least a first time period;

Processing the analyte data from the at least the first time period to determine an indicator of at least one lactate source; and

Generating a disease prediction using the at least one lactate derived indicator.

5. The monitoring system of claim 4, wherein the processor is further configured to generate one or more therapy recommendations based at least in part on the disease prediction.

6. The monitoring system of claim 5, wherein the one or more treatment recommendations comprise at least one of:

lifestyle modification recommendations;

Drug prescription recommendation;

surgical recommendations; or alternatively

Medical device recommendations for use by the patient.

7. The monitoring system of claim 4, wherein the disease prediction comprises at least one of:

An indication that the patient is suffering from liver disease;

an indication of the severity of the liver disease in the patient;

A score associated with the liver disease of the patient;

an indication of the level of risk of the patient being diagnosed with the liver disease;

an indication of an improvement level or a worsening level of the liver disease in the patient;

In response to a study drug and/or device intervention, an indication of an improvement level or a worsening level of the liver disease in the patient, wherein the device intervention comprises an invention by a gastric bypass device, a muscle electrical stimulation device, or a TENS device;

A risk of mortality for the patient; or alternatively

Identification of one or more diseases associated with the liver disease of the patient and associated risk that the patient is diagnosed with the one or more diseases.

8. The monitoring system of claim 7, wherein the indication of the improvement level or the worsening level of the liver disease of the patient is based at least in part on at least one of:

Surgery previously performed on the patient;

A medication previously ingested by the patient.

9. The monitoring system of claim 4, wherein the at least one lactate source indicator comprises at least one of a lactate clearance rate, an area under a lactate curve, a lactate baseline, a lactate rate of change, or a postprandial lactate level.

10. The monitoring system of claim 4, further comprising:

One or more non-analyte sensors configured to generate non-analyte sensor data during the first time period.

11. The monitoring system of claim 10, wherein:

the at least one lactate source indicator includes at least a first lactate removal rate; and

The processor being configured to process the analyte data from the at least the first time period to determine the at least one lactate removal rate comprises the processor being configured to:

Identifying at least one lactate increase period by the patient during the at least the first period of time;

Calculating a first lactate clearance rate for the patient after the at least one lactate increase period; and

The first lactate clearance rate of the patient is corrected based at least in part on the non-analyte sensor data to isolate lactate clearance by the liver of the patient.

12. The monitoring system of claim 11, wherein the at least one lactate increase period is due to at least one of:

Physical activity of the patient; or alternatively

The patient ingests lactate.

13. The monitoring system of claim 11, wherein the processor being configured to calculate the first lactate removal rate for the patient after the at least one lactate increase period comprises the processor being configured to:

Determining a maximum lactate level of the patient during the at least one lactate increase period;

Determining an amount of time it takes for the patient to decrease the maximum lactate level to a certain percentage of a baseline lactate level or a certain percentage of the maximum lactate level after the at least one lactate increase period; and

The first lactate removal rate of the patient is calculated using the determined maximum lactate level of the patient, the baseline lactate level of the patient, and the determined amount of time it takes for the maximum lactate level of the patient to decrease to the percentage of the baseline lactate level.

14. The monitoring system of claim 11, wherein the processor being configured to correct the first lactate clearance rate of the patient comprises the processor being configured to:

Identifying, using the non-analyte sensor data, that the at least one lactate increase period is due to physical activity of the patient;

Comparing the non-analyte sensor data to other non-analyte sensor data for one or more other lactate increase periods that are due to physical activity and have a predetermined lactate clearance rate decomposition, wherein the predetermined lactate clearance rate decomposition is indicative of decomposition of lactate clearance by at least one of the liver, kidney, muscle, and heart of the patient; and

Determining a second lactate clearance rate indicative of lactate clearance by the liver of the patient alone based at least in part on the comparison, and

Wherein at least the analyte data for one or more analytes and the second lactate removal rate are used to generate the disease prediction.

15. The monitoring system of claim 11, wherein the processor being configured to correct the first lactate clearance rate of the patient comprises the processor being configured to:

Identifying, using the non-analyte sensor data, that the at least one lactate increase period is not due to physical activity of the patient;

Comparing the data generated from the non-analyte sensor data to other non-analyte sensor data for one or more other lactate increase periods that are not due to physical activity and have a predetermined lactate clearance rate decomposition, wherein the predetermined lactate clearance rate decomposition is indicative of decomposition of lactate clearance by at least one of the liver, kidney, muscle, and heart of the patient;

determining a second lactate clearance rate indicative of lactate clearance by only the liver of the patient based at least in part on the comparison; and

16. The monitoring system of claim 4, wherein the disease prediction is generated using a model trained with training data, wherein the training data comprises records of historical patients suffering from liver disease at different stages.

17. The monitoring system of claim 4, wherein the processor is further configured to:

Obtaining at least one of demographic information, food intake information, activity level information, or medication information related to the patient, and

Wherein the disease prediction is also generated using at least one of the demographic information, the food intake information, the activity level information, or the pharmaceutical information.

18. The monitoring system of claim 4, wherein the one or more analytes further comprise at least one of glucose or a ketone.

19. The monitoring system of claim 4, wherein the one or more analytes of the patient are monitored continuously, semi-continuously, or periodically.