WO2021141950A1

WO2021141950A1 - Conditioning algorithms for biomarker sensor measurements

Info

Publication number: WO2021141950A1
Application number: PCT/US2021/012259
Authority: WO
Inventors: Justin J. Skaife
Original assignee: W. L. Gore & Associates, Inc.
Priority date: 2020-01-06
Filing date: 2021-01-06
Publication date: 2021-07-15

Abstract

Embodiments of the present disclosure relate monitoring a biomarker of a subject to determine the health of the subject. In at least one embodiment, a monitor comprises a processor and memory coupled to the processor. The memory comprises instructions that cause the processor to receive sensed signals from a sensor configured to sense signals associated with the biomarker, wherein the biomarker is correlated to health of the subject. The memory also comprises instructions that cause the processor to apply an exponential filter to the sensed signals to determine a concentration of the biomarker and compare the concentration of the biomarker to a baseline range for the biomarker. In addition, the memory comprises instructions that cause the processor to determine the health of the subject based on the comparison.

Description

CONDITIONING ALGORITHMS FOR BIOMARKER SENSOR MEASUREMENTS

CROSS-REFERENCE TO RELATED APPLICATION [0001] This application claims the benefit of Provisional Application No. 62/957,483, filed January 6, 2020, which is incorporated herein by reference in its entirety for all purposes.

FIELD

[0002] The present disclosure relates generally to algorithms for conditioning sensor measurements. More specifically, the present disclosure relates to algorithms for conditioning biomarker sensor measurements to determine a concentration of a biomarker.

BACKGROUND

[0003] Sensors have been developed to sense biomarkers of a subject. Biomarkers are indicative of phenomenon such as a disease, infection, or environmental exposure. As such, biomarkers can be useful in determining the health of a subject.

SUMMARY

[0004] The present disclosure relates to algorithms for conditioning biomarker sensor measurements. Exemplary embodiments include but are not limited to the following examples.

[0005] In an exemplary embodiment, a monitor configured to monitor a biomarker of a subject, comprises a processor; and memory coupled to the processor, the memory storing instructions that cause the processor to: receive sensed signals from a sensor configured to sense signals associated with the biomarker, the biomarker being correlated to health of the subject; apply an exponential filter to the sensed signals to determine a concentration of the biomarker; compare the concentration of the biomarker to a baseline range for the biomarker; and determine the health of the subject based on the comparison. As used herein, the exponential filter is a signal conditioning filter.

[0006] In an example thereof, the processor, the memory, and the sensor are incorporated into a single unit.

[0007] In another example thereof, the monitor is external to the subject.

[0008] In even another example thereof, the processor applies a smoothing factor that correlates to how many measured concentrations of the biomarker are obtained.

[0009] In yet another example thereof, the baseline range is specific to the subject.

[00010] In another example thereof, the memory stores instructions that cause the processor to determine the baseline range.

[00011] In even another example thereof, to apply the exponential filter to the sensed signals to determine the concentration of the biomarker the memory comprises instructions that cause the processor to apply more than one type of exponential filter to the sensed signals to determine the concentrations of the biomarker.

[00012] In yet another example thereof, the biomarker is BNP, NT-proBNP, or both.

[00013] In another example thereof, the biomarker is one or more biomarkers selected from the following group of biomarkers: vWF, D-dimer, fibrinogen, PAI-1, sCD40L, P-selectin, monocyte-platelet conjugates, CRP, IL-6, TNF-a, MPO, MCP-1, MMPs, cTNT, cTnl, IMA, FFAu, creatinine, and cystatin C.

[00014] In another exemplary embodiment, a computer-implemented method for monitoring a biomarker of a subject, comprises: receiving sensed signals associated with the biomarker from a sensor, the biomarker being correlated to health of the subject; applying an exponential filter to the sensed signals to determine a concentration of the biomarker; comparing the concentration of the biomarker to a baseline range for the biomarker; and determining the health of the subject based on the comparison.

[00015] In an example thereof, further comprising implanting the monitor within the subject.

[00016] In another example thereof, the monitor is external to the subject.

[00017] In even another example thereof, further comprising providing therapy based on the health of the subject.

[00018] In yet another example thereof, applying the exponential filter comprises applying a smoothing factor that correlates to how many measured concentrations of the biomarker are obtained.

[00019] In another example thereof, the baseline range is specific to the subject.

[00020] In even another example thereof, further comprising determining the baseline range.

[00021] In yet another example thereof, applying the exponential filter to the sensed signals to determine the concentration of the biomarker comprises applying more than one type of exponential filter to the sensed signals to determine the concentrations of the biomarker.

[00022] In another example thereof, the biomarker is BNP, NT-proBNP, or both.

[00023] In even another example thereof, the biomarker is one or more biomarkers selected from the following group of biomarkers: vWF, D-dimer, fibrinogen, PAI-1, sCD40L, P-selectin, monocyte-platelet conjugates, CRP, IL-6, TNF-a, MPO, MCP-1, MMPs, cTNT, cTnl, IMA, FFAu, creatinine, and cystatin C.

[00024] The foregoing Examples are just that and should not be read to limit or otherwise narrow the scope of any of the inventive concepts otherwise provided by the instant disclosure. While multiple examples are disclosed, still other embodiments will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative examples. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature rather than restrictive in nature.

BRIEF DESCRIPTION OF THE DRAWINGS

[00025] The accompanying drawings are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments, and together with the description serve to explain the principles of the disclosure.

[00026] Fig. 1 is a schematic illustration of an exemplary system including a biomarker sensor unit and a monitor device, in accordance with embodiments of the disclosure.

[00027] Fig. 2 is a block diagram of an exemplary biomarker sensor unit and a monitor device, in accordance with embodiments of the disclosure.

[00028] Fig. 3 is a graph illustrating simulated BNP blood level concentrations for a hypothetical patient suffering from heart disease, in accordance with embodiments of the disclosure.

[00029] Fig. 4 is a graph illustrating simulated ranges of BNP concentrations for different New York Heart Association (NYHA) classes, in accordance with embodiments of the disclosure.

[00030] Fig. 5 is a graph illustrating simulated BNP blood level concentrations for different NYHA classes, in accordance with embodiments of the disclosure.

[00031] Fig. 6 is a graph illustrating simulated, filtered BNP blood level concentrations for different NYHA classes, in accordance with embodiments of the disclosure. [00032] Fig. 7 is a flow diagram illustrating an exemplary method for determining biomarker concentrations of a subject, in accordance with embodiments of the disclosure.

[00033] As the terms are used herein with respect to ranges of measurements “about” and “approximately” may be used, interchangeably, to refer to a measurement that includes the stated measurement and that also includes any measurements that are reasonably close to the stated measurement, but that may differ by a reasonably small amount such as will be understood, and readily ascertained, by individuals having ordinary skill in the relevant arts to be attributable to measurement error, differences in measurement and/or manufacturing equipment calibration, human error in reading and/or setting measurements, adjustments made to optimize performance and/or structural parameters in view of differences in measurements associated with other components, particular implementation scenarios, imprecise adjustment and/or manipulation of objects by a person or machine, and/or the like.

[00034] This disclosure is not meant to be read in a restrictive manner. For example, the terminology used in the application should be read broadly in the context of the meaning those in the field would attribute such terminology. In at least some embodiments described herein, the term “simulated” measurements or concentrations may be used to denote measurements or concentrations that have been statistically generated based on patient data statistics and do not represent actual measurements or concentrations from patients.

[00035] With respect terminology of inexactitude, the terms “about” and “approximately” may be used, interchangeably, to refer to a measurement that includes the stated measurement and that also includes any measurements that are reasonably close to the stated measurement. Measurements that are reasonably close to the stated measurement deviate from the stated measurement by a reasonably small amount as understood and readily ascertained by individuals having ordinary skill in the relevant arts. Such deviations may be attributable to measurement error or minor adjustments made to optimize performance, for example. In the event it is determined that individuals having ordinary skill in the relevant arts would not readily ascertain values for such reasonably small differences, the terms “about” and “approximately” can be understood to mean plus or minus 10% of the stated value.

[00036] Although the term “block” may be used herein to connote different elements illustratively employed, the term should not be interpreted as implying any requirement of, or particular order among or between, various steps disclosed herein unless and except when explicitly referring to the order of individual steps.

DETAILED DESCRIPTION

[00037] Persons skilled in the art will readily appreciate that various aspects of the present disclosure can be realized by any number of methods and apparatus configured to perform the intended functions. It should also be noted that the accompanying drawing figures referred to herein are not necessarily drawn to scale but may be exaggerated to illustrate various aspects of the present disclosure, and in that regard, the drawing figures should not be construed as limiting.

[00038] As stated above, biomarkers are indicative of phenomenon such as a disease, infection, or environmental exposure and, therefore, can be useful in determining the health of a subject. The concentrations of a subject’s biomarkers, however, can vary significantly over the course of a day and/or a biomarker sensor configured to sense the subject’s biomarker may have a sub-optimal signal to noise ratio at different times and/or different placements within a subject. As such, the concentration of a biomarker sensed at a first time during the day may indicate a severity of a malady that is quite different than if the biomarker is sensed at a second time during the day. Embodiments disclosed herein provide a solution to this problem.

[00039] While the embodiments disclosed herein primarily discuss sensing biomarkers, the embodiments disclosed herein can also be used to sense biological pathways. Similar to sensing biomarkers, the sensed biological pathways can be used to determine the health of a subject. In some embodiments, the sensed biological pathways can be used to determine concentrations of a subject’s biomarkers, which can then be used to determine the health of a subject. The sensed biomarkers can also be used to determine biological pathways, which can then be used to determine the health of a subject. As such, throughout this disclosure, sensed biomarkers may be used interchangeably with sensed biological pathways.

[00040] Fig. 1 is a schematic illustration of an exemplary system 100 including a biomarker sensor unit 102 and a monitor device (MD) 104, in accordance with embodiments of the disclosure. The biomarker sensor unit 102 is configured to be arranged within the body of a subject 106 to sense one or more biomarkers of the subject 106. For example, the biomarker sensor unit 102 may be arranged on the subject 106, e.g., on the surface of the subject’s 106 skin. Additionally, or alternatively, the biomarker sensor unit 102 may be arranged within a subject 106, e.g., within tissue of the subject 106 (e.g., adipose tissue, subcutaneous tissue, and/or the like), within fluid of the subject 106 (e.g., interstitial fluid of the subject 106, blood of the subject 106, pleural fluid of the subject 106, urine of the subject 106, and/or the like) to measure one or more concentrations of one or more biomarkers of the subject 106. In at least some embodiments, the biomarker sensor unit 102 may be formed from inorganic and/or organic materials, thin composite films, or engineered microstructures with controlled porosity such as polytetrafluoroethylene or expanded polytetrafluoroethylene (ePTFE), flexible elastomeric polymers, polyparaxylylene, carbon loaded film, solid or stranded wires, flexible circuit and/or micro-flat pipes, and/or coated in hydrophobic or hydrophilic coating for either enhanced insulation or contact.

[00041] In exemplary embodiments, the biomarker sensing unit 102 senses biomarkers on a continuous, intermittent or near continuous basis. For example, the biomarker sensing unit 102 may be configured to sense biomarkers on a periodic basis, such as every 1/1000^th of a second, every 1/100^th of a second, every 1/10^th of a second, every second, every 5 seconds, every 10 seconds, every 30 seconds, every minute, every 10 minutes, every hour, every day, every week, etc. In embodiments, the biomarker sensing unit 102 may average and/or perform other types of processing on the sensed biomarkers, which can then be used to determine a biomarker concentration as discussed in more detail below. The biomarker sensing unit 102 may be an electrochemical and/or photonic sensor that uses enzymatic and/or optical properties to sense biomarker concentrations. Additionally, or alternatively, other types of electronic sensors may be used to sense biomarker concentrations, for example, a sensor that is measuring one or more electrical parameters such as conductance, resistance, capacitance, etc. that vary based on a recognition event of a biomarker in the biomarker sensing unit 102. Additionally, or alternatively to using enzymes, the biomarker sensing unit 102 may use molecular imprinted polymers and/or biological capture molecules such as aptamers, antibodies, fragments of antibodies, DNA strands, RNA strands, peptides, etc. that may be immobilized on the biomarker sensing unit 102 by having an affinity to the biomarker sensing unit 102. The one or more biomarkers can then be analyzed by the MD 104 to determine the health of the subject 106.

[00042] The biomarkers sensed by the biomarker sensing unit 102 may be correlated to a specific malady. For example, the biomarker sensing unit 102 may sense one or more biomarkers associated with hemostasis, platelet function, inflammation, necrosis and/or ischemia, hemodynamic, cardiac stress and/or fibrosis, and/or renal function. Regarding hemostasis, the biomarker sensing unit 102 may sense, for example, one or more of the following: von Willebrand factor (vWF), D-dimer, fibrinogen, plasminogen activator inhibitor-1 (PAI-1), and/or the like. Regarding platelet function, the biomarker sensing unit 102 may sense, for example, one or more of the following: soluble CD40 ligand (sCD40L), P-selectin, monocyte-platelet conjugates, and/or the like. Regarding inflammation, the biomarker sensing unit 102 may sense, for example, one or more of the following: C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-a), myeloperoxidase (MPO), monocyte chemoattractant protein-1 (MCP-1), matrix metalloproteinases (MMPs), and/or the like. Regarding necrosis and/or ischemia, the biomarker sensing unit 102 may sense, for example, one or more of the following: cardiac troponin T (cTnT), cardiac troponin I (cTnl), ischemia modified albumin (IMA), Free fatty acids unbound to albumin (FFAu), and/or the like. Regarding hemodynamic, cardiac stress and/or fibrosis, the biomarker sensing unit 102 may sense, for example, one or more of the following: brain natriuretic peptide (BNP), NT-proB-type natriuretic peptide (NT-proBNP), other natriuretic peptides, troponin, soluble suppression of tumorigenicity 2 (sST2), and/or the like. Regarding renal function, the biomarker sensing unit 102 may sense, for example, one or more of the following: creatinine, cystatin C, and/or the like.

[00043] In at least some embodiments, the biomarker sensing unit 102 may also be configured to sense one or more other analytes. Exemplary analytes include, but are not limited to, glucose, potassium, inorganic phosphorous, magnesium, lactate dehydrogenase (LD), lactate, oxygen, insulin, C-peptide, parathyroid hormone (PTH), osteocalcin, cortisone, C-telopeptide, adrenocorticotropic hormone (ACTFI), other types of hormones, pharmacologic agents, bio-pharmaceuticals, proteins and peptides, antibodies, therapeutic agents, electrolytes, vitamins, pathogenic components, antigens, molecular markers associated with different disease conditions in stages, viral loads, and/or the like.

[00044] In some embodiments, the sensed analytes may be used to determine the effectiveness of a treatment regimen for a subject. If the treatment regimen is not having its intended effect, a notification to provide a modified treatment regimen can be provided via a user interface. In some embodiments, the notification to provide a modified treatment regimen can include how the treatment regimen should be modified. For example, the notification may include a recommendation to increase or decrease a dosage of the treatment regimen. As another example, the notification may include a recommendation to alter a pharmaceutical that is being administered. Alternatively, if the treatment regimen is having its intended effect, then a notification can be provided that the treatment regimen is effective and should be continued and unaltered. [00045] As stated above, the biomarker sensor unit 102 is configured to be communicatively coupled to a MD 104 via a communication link 108. The MD 104 may be configured to receive, store, and/or process signals sensed by the biomarker sensor unit 102. As explained in more detail below, at least one of the processes performed by the MD 104 may be to filter the signals sensed by the biomarker sensor unit 102. Additionally, or alternatively, the MD 104 may administer therapy via, for example, chemical, hormonal, or electrical stimulations based on the biomarker signals sensed by the biomarker sensor unit 102 and/or the health that is determined based on the biomarker signals. In at least some embodiments, the MD 104 may also perform a power management function for the biomarker sensor unit 102. For example, the MD 104 may wake the biomarker sensor unit 102, sleep the biomarker sensor unit 102, and/or direct the biomarker sensor unit 102 to sense, store, process, and/or transmit signals. Additionally, or alternatively, the MD 104 may be configured to send notifications to another device (not shown) to alert the subject 106 and/or a third-party (e.g., a clinician) about any of the results obtained by the MD 104. Embodiments of the MD 104 may be any type of device having computing capabilities such as, for example, a smartphone, a tablet, a notebook, or other portable or non-portable computing device. While the MD 104 is illustrated as being a separate device, some or all of the functionality of the MD 104 may be performed by the biomarker sensor unit 102.

[00046] The communication link 108 may be, or include, a wired link (e.g., a link accomplished via a physical connection) or a non-wired link such as, for example, a short-range radio link, such as Bluetooth, IEEE 802.11, near-field communication (NFC), WiFi, a proprietary wireless protocol, and/or the like. The term "communication link" may refer to an ability to communicate some type of information in at least one direction between at least two devices and should not be understood to be limited to a direct, persistent, or otherwise limited communication channel. That is, according to embodiments, the communication link 108 may be a persistent communication link, an intermittent communication link, an ad-hoc communication link, and/or the like. The communication link 108 may refer to direct communications between the biomarker sensor unit 102 and the MD 104, and/or indirect communications that travel between the biomarker sensor unit 102 and the MD 104 via at least one other device (e.g., a repeater, router, hub, and/or the like). The communication link 108 may facilitate uni directional and/or bi-directional communication between the biomarker sensor unit 102 and the MD 104. Data and/or control signals may be transmitted between the biomarker sensor unit 102 and the MD 104 to coordinate the functions of the biomarker sensor unit 102 and/or the MD 104. In embodiments, subject data may be downloaded from one or more of the biomarker sensor unit 102 and the MD 104 periodically or on command. The clinician and/or the subject 106 may communicate with the biomarker sensor unit 102 and/or the MD 104, for example, to initiate, terminate and/or modify sensing, storing, processing, and/or transmitting signals.

[00047] The diagram shown in Fig. 1 is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the present disclosure. The diagram also should not be interpreted as having any dependency or requirement related to any single component or combination of components illustrated therein. Additionally, various components depicted in Fig. 1 may be, in embodiments, integrated with various ones of the other components depicted therein (and/or components not illustrated), all of which are within the ambit of the present disclosure.

[00048] Fig. 2 is a block diagram of an exemplary biomarker sensor unit 102 and a MD 104, in accordance with embodiments of the disclosure. As stated above, while the MD 104 is illustrated as being a separate device as the biomarker sensor unit 102, some or all of the functionality of the MD 104 may be performed by the biomarker sensor unit 102.

[00049] According to at least some embodiments, the biomarker sensor unit 102 includes a sensor 110, a processor 112, memory 114 including sensed data 116, an analog-to-digital (ADC) component 118, a communication component 120, and/or a power source 122. Flowever, these components are only meant to be an example and other components that facilitate sensing biomarker concentrations may be used, depending on the type of sensing (e.g., electrical sensing, electrochemical sensing, photonic sensing, enzyme sensing, etc.) incorporated into the biomarker sensor unit 102.

[00050] The sensor 110 may be configured to sense one or more biomarkers of the subject 106. Exemplary biomarkers include, but are not limited, to: vWF, D-dimer, fibrinogen, PAI-1, sCD40L, P-selectin, monocyte-platelet conjugates, CRP, IL-6, TNR-a, MPO, MCP-1 , MMPs, cTnT, cTnl, IMA, FFAu, BNP, NT-proBNP, other Natriuretic Peptides, troponin, sST2, creatinine, cystatin C, cortisone, and/or the like. Signals associated with the sensed biomarkers may be stored in sensed data 116 in memory 114 and/or transmitted to the MD 104 for further processing, as described in more detail below.

[00051] The processor 112 may be any arrangement of electronic circuits, electronic components, processors, program components and/or the like configured to store and/or execute programming instructions, to direct the operation of the other functional components of the biomarker sensor unit 102. For example, the processor 112 may instruct the sensor 110 to sense one or more biomarkers of a subject 106, to instruct the ADC component to convert any biomarker signals sensed by the sensor 110 from analog signals to digital signals, to store any sensed data 116, to instruct the communication component 120 to transmit any data corresponding to biomarkers sensed by the sensor 110 and/or the like, and may be implemented, for example, in the form of any combination of hardware, software, and/or firmware.

[00052] In embodiments, the processor 112 may be, include, or be included in one or more Field Programmable Gate Arrays (FPGAs), one or more Programmable Logic Devices (PLDs), one or more Complex PLDs (CPLDs), one or more custom Application Specific Integrated Circuits (ASICs), one or more dedicated processors (e.g., microprocessors), one or more central processing units (CPUs), software, hardware, firmware, or any combination of these and/or other components. Although the processor 112 is referred to herein in the singular, the processor 112 may be implemented in multiple instances, distributed across multiple computing devices, instantiated within multiple virtual machines, and/or the like.

[00053] According to embodiments, the processor 112 may include a processing unit configured to communicate with memory 114 to execute computer-executable instructions stored in the memory 114. Additionally, or alternatively, the processor 112 may be configured to store information (e.g., sensed data 116) in the memory 114 and/or access information (e.g., sensed data 116) from the memory 114.

[00054] In embodiments, the memory 114 includes computer-readable media in the form of volatile and/or nonvolatile memory and may be removable, nonremovable, or a combination thereof. Media examples include Random Access Memory (RAM); Read Only Memory (ROM); Electronically Erasable Programmable Read Only Memory (EEPROM); flash memory; optical or holographic media; magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices; data transmissions; and/or any other medium that can be used to store information and can be accessed by a computing device such as, for example, quantum state memory, and/or the like. In embodiments, the memory 114 stores computer-executable instructions for causing the processor to implement aspects of embodiments of system components discussed herein and/or to perform aspects of embodiments of methods and procedures discussed herein.

[00055] The computer-executable instructions may include, for example, computer code, digital signal processing, machine-useable instructions, and the like such as, for example, program components capable of being executed by one or more processors associated with the computing device. Program components may be programmed using any number of different programming environments, including various languages, development kits, frameworks, and/or the like. Some or all of the functionality contemplated herein may also, or alternatively, be implemented in hardware and/or firmware.

[00056] The communication component 120 may be configured to communicate (i.e. , send and/or receive signals) with the MD 104 and/or any other device. For example, the sensed data 116 may be transmitted to the MD 104 for processing and/or storage. In embodiments, the communication component 120 may include, for example, circuits, program components, antennas, and one or more transmitters and/or receivers for communicating wirelessly with one or more other devices such as, for example, the MD 104. According to various embodiments, the communication component 120 may include one or more transmitters, receivers, transceivers, transducers, and/or the like, and may be configured to facilitate any number of different types of wireless communication such as, for example, radio-frequency (RF) communication, microwave communication, infrared or visual spectrum communication, acoustic communication, inductive communication, conductive communication, and/or the like. The communication component 120 may include any combination of hardware, software, and/or firmware configured to facilitate establishing, maintaining, and using any number of communication links.

[00057] The power source 122 provides electrical power to the other operative components (e.g., the sensor 110, the processor 112, the memory 114, the ADC component 118, and the communication component 120), and may be any type of power source suitable for providing the desired performance and/or longevity requirements of the biomarker sensor unit 102. In various embodiments, the power source 122 may include one or more batteries, which may be rechargeable (e.g., using an external energy source). The power source 122 may include one or more capacitors, energy conversion mechanisms, and/or the like. Additionally, or alternatively, the power source 122 may harvest energy from a subject (e.g., the subject 106) (e.g. motion, heat, biochemical) and/or from the environment (e.g. electromagnetic).

[00058] As shown in FIG. 2, the MD 104 is communicatively coupled to the electronics unit via the communication link 108 and includes a processor 124, memory 126, an I/O component 128, a communication component 130, and a power source 132. In at least some embodiments, the memory 126 may have stored thereon: sensed data 116, a filter component 134, a classification component 136, and a baseline component 138.

[00059] Similar to the processor 112, the processor 124 may be any arrangement of electronic circuits, electronic components, processors, program components and/or the like configured to store and/or execute programming instructions, to direct the operation of the other functional components of the MD 104, to store data received by the MD 104 from the biomarker sensor unit 102, and/or the like, and may be implemented, for example, in the form of any combination of hardware, software, and/or firmware.

[00060] In embodiments, the processor 124 may be, include, or be included in one or more Field Programmable Gate Arrays (FPGAs), one or more Programmable Logic Devices (PLDs), one or more Complex PLDs (CPLDs), one or more custom Application Specific Integrated Circuits (ASICs), one or more dedicated processors (e.g., microprocessors), one or more central processing units (CPUs), software, hardware, firmware, or any combination of these and/or other components. According to embodiments, the processor 124 may include a processing unit configured to communicate with memory 126 to execute computer-executable instructions stored in the memory. Although the processor 124 is referred to herein in the singular, the processor 124 may be implemented in multiple instances, distributed across multiple computing devices, instantiated within multiple virtual machines, and/or the like.

[00061] The processor 124 may also be configured to store information (e.g., sensed data 116) in the memory 126 and/or access information (e.g., sensed data 116) from the memory 126. The processor 124 may execute instructions and perform desired tasks as specified by computer-executable instructions stored in the memory 126. In embodiments, for example, the processor 124 may be configured to instantiate, by executing instructions stored in the memory 126.

[00062] In embodiments, the memory 126 includes computer-readable media in the form of volatile and/or nonvolatile memory and may be removable, nonremovable, or a combination thereof. Media examples include Random Access Memory (RAM); Read Only Memory (ROM); Electronically Erasable Programmable Read Only Memory (EEPROM); flash memory; optical or holographic media; magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices; data transmissions; and/or any other medium that can be used to store information and can be accessed by a computing device such as, for example, quantum state memory, and/or the like. In embodiments, the memory stores computer-executable instructions for causing the processor to implement aspects of embodiments of system components discussed herein and/or to perform aspects of embodiments of methods and procedures discussed herein.

[00063] The computer-executable instructions may include, for example, computer code, machine-useable instructions, and the like such as, for example, program components capable of being executed by one or more processors associated with the computing device. Program components may be programmed using any number of different programming environments, including various languages, development kits, frameworks, and/or the like. Some or all of the functionality contemplated herein may also, or alternatively, be implemented in hardware and/or firmware.

[00064] The I/O component 128 may include and/or be coupled to a user interface configured to present information to a user or receive indication from a user. For example, the I/O component 128 may include and/or be coupled to a display device, a speaker, a printing device, and/or the like, and/or an input component such as, for example, a microphone, a joystick, a satellite dish, a scanner, a printer, a wireless device, a keyboard, a pen, a voice input device, a touch input device, a touch-screen device, an interactive display device, a mouse, and/or the like. As stated above, the I/O component 128 may be used to present and/or provide an indication of any of the sensed data 116 and/or any of the other results determined by the components 134,

136, 138, which are discussed in more detail below. According to embodiments, for example, the I/O component 128 may include one or more visual indicators (e.g., single color LED lights, multi-color LED lights, a flexible digital display device, and/or the like) configured to provide information to a user (e.g., by illuminating, flashing, displaying data, etc.).

[00065] The communication component 130 may be configured to communicate (i.e. , send and/or receive signals) with the biomarker sensing unit 102 and/or any other device. For example, the communication component 130 may be configured to receive the sensed data 116 from the biomarker sensing unit 102. Additionally, or alternatively, the communication component 130 may be configured to send commands to the biomarker sensing unit 102, send the sensed data 116 and/or any other results determined by the components 134, 136, 138 to another device (not shown) for processing and/or storage.

[00066] According to various embodiments, the communication component 130 may include one or more transmitters, receivers, transceivers, transducers, and/or the like, and may be configured to facilitate any number of different types of wireless communication such as, for example, radio-frequency (RF) communication, microwave communication, infrared or visual spectrum communication, acoustic communication, inductive communication, conductive communication, and/or the like. The communication component 130 may include any combination of hardware, software, and/or firmware configured to facilitate establishing, maintaining, and using any number of communication links.

[00067] The power source 132 provides electrical power to the other operative components (e.g., the processor 124, the memory 126, the I/O component 128, and/or the communication component 130), and may be any type of power source suitable for providing the desired performance and/or longevity requirements of the MD 104. In various embodiments, the power source 132 may include one or more batteries, which may be rechargeable (e.g., using an external energy source). The power source 132 may include one or more capacitors, energy conversion mechanisms, and/or the like. In embodiments, the power source 132 may transfer power to the power source 122 using a wireless or non-wireless connection (e.g., via conduction, induction, radio-frequency, etc.). Because the biomarker sensing unit 102 may be a small device, the power source 122 may not be capable of storing a lot of power and, therefore, the longevity of the biomarker sensing unit 102 may be increased via power transfer from the MD 104 to the biomarker sensing unit 102.

[00068] As mentioned above, the concentrations of a subject’s biomarkers can vary significantly over the course of a day. One such biomarker that varies significantly over the course of a day is BNP. While the description provided below is in relation to BNP, the embodiments can be applied to any of the other biomarkers set forth above.

[00069] As mentioned above, BNP may help evaluate a level of cardiac stress.

Fig. 3 is a graph 300 illustrating simulated BNP blood level concentrations for a hypothetical patient suffering from heart disease, in accordance with embodiments of the disclosure. Generally, heart failure can be classified into four different classifications. An NYFIA Class I patient is a subject 106 with cardiac disease that won’t result in limitation of physical activity. That is, ordinary physical activity does not cause undue fatigue, palpitation (rapid or pounding heart beat), dyspnea (shortness of breath), or anginal pain (chest pain). An NYFIA Class II patient is a subject 106 with cardiac disease that results in a slight limitation of physical activity. That is, the subject 106 is comfortable at rest, but ordinary physical activity results in fatigue, palpitation, dyspnea, or anginal pain. An NYHA Class III patient is a subject 106 with cardiac disease that results in marked limitation of physical activity. That is, the subject 106 is comfortable at rest, but less than ordinary activity causes fatigue, palpitation, dyspnea, or anginal pain. An NYHA Class IV patient is a subject 106 with cardiac disease that results in the inability to carry on any physical activity without discomfort. Symptoms of heart failure or the anginal syndrome may be present even at rest; and, if for any physical activity discomfort is increased. The NYHA classification is one classification of heart disease. The American Heart Association and other organizations have similar classifications, and the discussion included herein applies to the other classifications as well.

[00070] As illustrated in Fig. 3, the concentrations of BNP illustrated in Fig. 3 for the hypothetical patient may vary significantly and be idiosyncratic to the patient. For example, the concentration of BNP for the subject 106 varies significantly from less than 200pg/ml_ to greater than 1200pg/ml_ throughout the course of approximately 65 samples being taken from the subject 106. Adding to the complexity of trying to evaluate a level of heart failure for a subject 106 and determining whether any heart disease of a subject 106 is getting worse or better, the BNP concentrations for different classes of heart failure significantly overlap, as illustrated in Fig. 4, which illustrates simulated ranges of BNP concentrations for different NYHA classes. As such, it may be difficult to use BNP concentrations in isolation to not only determine what classification of heart failure should be assigned to a patient but also whether any heart disease of a patient is getting worse or better. The filter component 134, the classification component 136 and/or the baseline component 138 may be used as described above and below to alleviate some of these problems.

[00071] Fig. 5 is a graph 500 illustrating simulated BNP blood level concentrations for different NYHA classes, in accordance with embodiments of the disclosure. In embodiments, the graph 500 may be understood to be the BNP concentrations for a subject 106 in the event the subject 106 has different classes of heart failure. As shown, the BNP concentration for the subject 106 in the event the subject 106 has Class I heart failure may be greater at different times than the BNP concentration for the subject 106 in the event the subject 106 has Class II, Class III, or Class IV. For example, as illustrated in Fig. 5, the BNP concentration for the subject 106 in the event the subject 106 has Class IV heart failure may be low as 200pg/ml_ and the BNP concentration for the subject 106 in the event the subject has Class I heart failure, may be as high as 600pg/ml_. Similarly, the BNP concentration for the subject 106 in the event the subject 106 has Class II heart failure, may be greater than the BNP concentration for the subject 106 in the event the subject 106 has Class III or Class IV heart failure, during the course of different samples being taken.

[00072] To solve these problems, the filter component 134 is configured to apply a filter to sensed data 116, which, in this specific example, is BNP concentrations. In at least some embodiments, the filter component 134 may apply an exponential filter to the BNP sensor measurements. Additionally, or alternatively, the filter component 134 may apply more than one type of filter (e.g., more than one type of exponential filter).

[00073] In at least some embodiments, the filter component 134 may apply a smoothing factor. The smoothing factor may correlate to how many sensor measurements are taken before the sensor measurements are sufficiently filtered and a BNP concentration can be determined. For example, a sample set of X measurements (i.e. , a window size) may be taken before a BNP concentration is determined; and, once X + 1 sensor measurements are taken, then the first sensor measurement in the sample set X may discarded and the remaining sample sensor measurements in the sample set may be used to determine the BNP concentration. That is, the smoothing factor may determine the window size of the sensor measurements that are taken before the sensor measurements are sufficiently filtered and a BNP concentration can be determined.

[00074] In at least some embodiments, the smoothing factor may depend on how noisy the sensor measurements may be and/or the standard deviation of the sensor measurements. For example, if the sensor measurements inherently include noisy data and/or have a large standard deviation, then the smoothing factor can require more sensor measurements to be taken before the signal is sufficiently filtered by the filter component 134. Conversely, if the sensor measurements do not inherently include noisy data and/or the standard deviation of the sensor measurements is small, then the smoothing factor can require fewer sensor measurements to be taken before the signal is sufficiently filtered by the filter component 134. As such, the smoothing factor may limit the influence of any one sample or any group of samples on the determined concentration.

[00075] Fig. 6 is a graph 600 illustrating simulated, filtered BNP blood level concentrations for different NYFIA classes, in accordance with embodiments of the disclosure. As mentioned above, BNP concentrations for different NYFIA classes may be idiosyncratic to a subject 106. That is, a class IV subject 106 may have BNP levels below a Class III subject 106, a class III subject 106 may have BNP levels below a Class II subject 106, etc. As such, the graph 600 may be understood to be the BNP concentrations for a subject 106 assuming the subject 106 has different classes of heart failure, as stated above.

[00076] In this exemplary embodiment, the filter component 134 may apply a smoothing factor to the sensed data to the sensed data depicted in Fig. 5. In the illustrated example, the smoothing factor applied is eight so eight samples may be collected prior to determining a BNP concentration. When a ninth sample is collected, the filter component 134 assigns the first eight samples in the sample set a weight of 7/8 and the ninth sample is assigned a weight of 1/8. Further, when a tenth sample is collected, the filter component 134 assigns the first nine samples in the sample set a weight of 7/8 and the tenth sample is assigned a weight of 1/8 and so on. In embodiments, the BNP concentration may be determined prior to the collection of eight samples if the smoothing factor is eight, but the BNP concentration may be less accurate than when a BNP concentration is determined after eight samples are collected.

[00077] As another example, if the smoothing factor is five, then five samples may be collected prior to determining a BNP concentration. Then, when a sixth sample is collected, the filter component 134 assigns the first five samples in the sample set a weight of 4/5 and the sixth sample is assigned a weight of 1/5. Further, when a seventh sample is collected, the filter component 134 assigns the first six samples in the sample set a weight of 4/5 and the seventh sample is assigned a weight of 1/5 and so on. Similar to above, the BNP concentration may be determined prior to the collection of five samples if the smoothing factor is five, but the BNP concentration may be less accurate than when a BNP concentration is determined after five samples are collected.

[00078] As shown, the BNP measurements are smoothed and, importantly, separate. That is, a BNP concentration for the subject 106 in the event the subject 106 has Class IV heart failure does not overlap a BNP concentration of a sample taken from the subject 106 in the event the subject 106 has Class I, II, or III heart failure. Similarly, a BNP concentration of a sample taken from the subject 106 in the event the subject 106 has Class III heart failure does not overlap a BNP concentration of a sample taken from the subject 106 in the event the subject 106 has Class I or II heart failure. And, a BNP concentration of a sample taken from the subject 106 in the event the subject 106 has Class II heart failure does not overlap with a BNP concentration of a sample taken from the subject 106 in the event the subject 106 has Class I heart failure. As such, the BNP concentration of a subject 106 may be correlated to a specific heart failure class based on the filtered results depicted in Fig. 6. For example, the classification component 136 may compare the BNP concentrations of a subject 106 to respective baseline ranges for different classes of heart failure. That is, the classification component 136 may compare the BNP concentrations for a subject 106 against BNP baseline concentration ranges that are indicative of Class I, II, III, or IV heart failure for the subject 106. And, depending on which BNP baseline concentration range the BNP concentrations of the subject 106 are included within, the classification component 136 may determine the health of the subject 106 by classifying the subject 106 as having the respective class of heart failure that corresponds to that BNP baseline concentration range.

[00079] In at least some embodiments, the heart failure classification for a subject 106 may be determined via other methods known in the art. In addition, the BNP concentrations for the subject 106 may be measured and correlated to the determined heart failure classification for the subject 106. And, once a heart failure classification and BNP concentrations for a subject 106 are determined, the BNP concentrations for the subject 106 may be monitored by the classification component 136 to inform a trend of the heart failure and determine whether the heart failure classification is likely getting better or worse.

[00080] In at least some embodiments, the classification component 136 may be used to inform a trend based on the sensed measurements and, therefore, determine whether the health of the health of the subject 106 is trending better or worse. For example, if the BNP concentration of a subject 106 is increasing, then the classification component 136 may indicate it is likely the heart failure of the subject 106 is getting worse. Conversely, if the BNP concentration of a subject 106 is decreasing, then the classification component 136 may indicate it is likely the heart failure of the subject 106 is getting better.

[00081] While general baselines ranges may be used in some circumstances, in other circumstances the baseline ranges may be specifically adapted for the subject 106. For example, a subject 106 may have aberrant BNP concentrations that are not consistent with BNP baseline concentration ranges in general. That is, a subject 106 may have a BNP concentration that would otherwise classify the subject 106 as having Class III heart failure when in fact the subject 106 has Class I, II, or IV heart failure. In these circumstances, the baseline component 138 may adjust and/or update the BNP baseline concentration ranges based on specific characteristics of the subject 106. For example, if the subject 106 has Class III heart failure but has higher than normal BNP concentrations for Class III heart failure, then the baseline component 138 may increase the BNP baseline classification ranges for the subject 106. Conversely, if the subject 106 has Class III heart failure but has lower than normal BNP concentrations for Class III heart failure, then the baseline component 138 may decrease the BNP baseline classification ranges for the subject 106.

[00082] In at least some embodiments, the baseline component 138 may determine whether a subject 106 can be classified using general biomarker ranges for a specific malady and/or whether a subject’s 106 health is getting worse or better based on general biomarker ranges. For example, assume a subject 106 has been determined to have Class III heart failure and had BNP baseline concentrations that range from Xi to Yi when it was determined the subject 106 had Class III heart failure. If the subject 106 continues to have BNP concentrations between Xi and Yi, then it may be likely the subject 106 continues to have Class III heart failure. Conversely, if the subject 106 has BNP concentrations that are outside the range of Xi to Yi, then it may be likely the subject’s 106 heart failure is getting worse (if the BNP concentrations are increasing) or the subject’s 106 heart failure is getting better (if the BNP concentrations are decreasing). Additionally, or alternatively, assume the subject’s 106 heart failure has been getting better and it is determined the subject 106 has Class II heart failure and BNP baseline concentrations ranging from X2 to Y2. Then, if the subject 106 continues to have BNP concentrations between X2 and Y2, it may be likely the subject 106 continues to have Class II heart failure. Conversely, if the subject 106 has BNP concentrations that are outside the range of X2 to Y2, then it may be likely the subject’s 106 heart failure is getting worse (if the BNP concentrations are increasing) or the subject’s 106 heart failure is getting better (if the BNP concentrations are decreasing). Additionally, or alternatively, the baseline component 138 may determine baseline ranges for a subject 106 using machine learning.

[00083] As stated above, while the description provided above is in relation to BNP, the embodiments can be applied to any of the other biomarkers set forth above.

[00084] Fig. 7 is a flow diagram illustrating an exemplary method 700 for determining biomarker concentrations of a subject, in accordance with embodiments of the disclosure. The method 700 may include implanting a biomarker sensor unit within a subject (block 202). A biomarker sensor unit (e.g., the biomarker sensor unit 102) may be used to send the biomarker concentrations. In at least some embodiments, the biomarker sensor unit 102 may be implanted within a subject 106. Alternatively, the biomarker sensor unit 102 may be arranged on the subject’s 106 skin.

[00085] In at least some embodiments, the method 700 may include implanting a monitor within a subject (block 204). A monitor device (e.g., the monitor device 104) may be used to monitor biomarker concentrations. Alternatively, the MD 104 may be external to the subject 102. However, as described above, some or all of the functionality of the MD 104 may be incorporated into and/or performed by the biomarker sensor unit 102.

[00086] In at least some embodiments, the method 700 may also include sensing signals by the biomarker sensor unit (block 206) and receiving the sensed biomarker signals by the MD 104 (block 208).

[00087] In embodiments, the method 700 may further include filtering the sensed signals to determine a biomarker concentration (block 210). The filter component 134 may filter the sensed biomarker signals according to any of the embodiments described above to determine the biomarker concentration. For example, the filter component 134 may apply an exponential filter to the sensed signals to determine the biomarker concentration. Additionally, or alternatively, the filter component 134 may apply more than one exponential filter to the sensed signals to determine the biomarker concentration. Additionally, or alternatively, the filter component 134 may apply a smoothing factor to the sensed signals.

[00088] The method 700 may further include determining a baseline range for the sensed measurements (block 212) and comparing the biomarker concentration to the baseline range (block 214). The baseline component 138 may determine the baseline range according to any of the embodiments discussed above. And, the classification component 136 may compare the biomarker concentration to the baseline range.

[00089] In at least some embodiments, the method 700 includes determining the health of a subject based on the comparison (block 216). In some embodiments, the classification component 136 determines the health of a subject 106 based on which biomarker concentration range the subject’s 106 biomarker concentrations are included within.

[00090] The diagram shown in Fig. 7 is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the present disclosure. The diagram also should not be interpreted as having any dependency or requirement related to any single block or combination of blocks illustrated therein.

[00091] The embodiments disclosed herein have been described above both generically and regarding specific embodiments. It will be apparent to those skilled in the art that various modifications and variations can be made in the embodiments without departing from the scope of the disclosure. Thus, it is intended that the embodiments cover the modifications and variations of this disclosure provided they come within the scope of the appended claims and their equivalents.

Claims

CLAIMS What is claimed is:

1. A monitor configured to monitor a biomarker of a subject, the monitor comprising: a processor; and memory coupled to the processor, the memory storing instructions that cause the processor to: receive sensed signals from a sensor configured to sense signals associated with the biomarker, the biomarker being correlated to health of the subject; apply an exponential filter to the sensed signals to determine a concentration of the biomarker; compare the concentration of the biomarker to a baseline range for the biomarker; and determine the health of the subject based on the comparison.

2. The monitor of claim 1 , further comprising the sensor, wherein the processor, the memory, and the sensor are incorporated into a single unit.

3. The monitor of any one of claims 1 -2, wherein the monitor is external to the subject.

4. The monitor of any one of claims 1 -3, wherein the processor applies a smoothing factor that correlates to how many measured concentrations of the biomarker are obtained.

5. The monitor of any one of claims 1 -4, wherein the baseline range is specific to the subject.

6. The monitor of any one of claims 1 -5, the memory storing instructions that cause the processor to determine the baseline range.

7. The monitor of any one of claims 1 -6, wherein to apply the exponential filter to the sensed signals to determine the concentration of the biomarker the memory comprises instructions that cause the processor to apply more than one type of exponential filter to the sensed signals to determine the concentrations of the biomarker.

8. The monitor of any one of claims 1-7, wherein the biomarker is BNP, NT- proBNP, or both.

9. The monitor of any one of claims 1 -8, wherein the biomarker is one or more biomarkers selected from the following group of biomarkers: vWF, D-dimer, fibrinogen, PAI-1, sCD40L, P-selectin, monocyte-platelet conjugates, CRP, IL-6, TNF-a, MPO, MCP-1, MMPs, cTNT, cTnl, IMA, FFAu, creatinine, and cystatin C.

10. A computer-implemented method for monitoring a biomarker of a subject, the method comprising: receiving sensed signals associated with the biomarker from a sensor, the biomarker being correlated to health of the subject; applying an exponential filter to the sensed signals to determine a concentration of the biomarker; comparing the concentration of the biomarker to a baseline range for the biomarker; and determining the health of the subject based on the comparison.

11. The method of claim 10, further comprising implanting the monitor within the subject.

12. The method of claim 10, wherein the monitor is external to the subject.

13. The method of claim 10, further comprising providing therapy based on the health of the subject.

14. The method of any one of claims 10-13, wherein applying the exponential filter comprises applying a smoothing factor that correlates to how many measured concentrations of the biomarker are obtained.

15. The method of any one of claims 10-14, wherein the baseline range is specific to the subject.

16. The method of any one of claims 10-15, further comprising determining the baseline range.

17. The method of any one of claims 10-16, wherein applying the exponential filter to the sensed signals to determine the concentration of the biomarker comprises applying more than one type of exponential filter to the sensed signals to determine the concentrations of the biomarker.

18. The method of any one of claims 10-17, wherein the biomarker is BNP, NT- proBNP, or both.

19. The method of any one of claims 10-18, wherein the biomarker is one or more biomarkers selected from the following group of biomarkers: vWF, D-dimer, fibrinogen, PAI-1, sCD40L, P-selectin, monocyte-platelet conjugates, CRP, IL-6, TNF-a, MPO, MCP-1, MMPs, cTNT, cTnl, IMA, FFAu, creatinine, and cystatin C.