WO2022101937A1

WO2022101937A1 - An integrated health data capture and analysis based device for evaluation, diagnosis and prognosis of heart failure

Info

Publication number: WO2022101937A1
Application number: PCT/IN2021/051068
Authority: WO
Inventors: Komal SHAH; Kamal Sharma
Original assignee: Shah Komal; Kamal Sharma
Priority date: 2020-11-12
Filing date: 2021-11-12
Publication date: 2022-05-19

Abstract

An integrated health data capture and analysis device for assessing heart failure comprises (a) Multipleconfounders adjusted algorithm (b) a device having embedded algorithm for collecting, processing and storing the data from multiple sources to derive and interpret corresponding values of diagnosis (b) connect with central processing unit for personalized care by patient centric -personalized disease management and adverse event prevention counselling system. The present invention relates to the field of diagnostics and more particularly, relates to a device for determining a patient's risk of suffering from heart failure based on the detection of BNP that is adjusted for effect of confounders and hence expected to be accurate. Also disclosed are methods for improving both the accuracy and speed of heart failure risk models by incorporating biomarker data from patient samples.

Description

Title AN INTEGRATED HEALTH DATA CAPTURE AND ANALYSIS BASED DEVICE FOR EVALUATION, DIAGNOSIS AND PROGNOSIS OF HEART FAILURE

FIELD OF INVENTION

The present invention relates to the field of diagnostics and prognostication and more particularly, relates to adevice for diagnosing patients suffering from heart failure beyond theassessment of serum BNP levels. Also disclosed are methods for improving both the accuracy and speed of heart failure risk assessment models by incorporating biomarker data from patient samples apart from demographics and biomarker confounders. According to the present invention, the detection devicecomprises ofa) an algorithm using clinical, demographic, clinical and biochemical variables for accurate prediction of heart failure b) an algorithm embedded IOT based device for processing data. This IOT will have compatibility with all the existing laboratory and point of care devices estimating BNP levels from human bloodthat can assemble and analyse data from a mobile and variety of analysers including point of care devices. This will act as a digital junction connecting all the potential sources of data(c) processing data to derive and interpret corresponding values of BNP from multiple testing sources(d) connected with central processing unit for personalized care of patient for disease management and to diagnose adverse event for prevention and /or prognostication counselling.

BACKGROUND OF INVENTION

An aim of modern medicine is to provide personalized or individualized treatment solutions. Thesemedical modalitiesusually take into account a patient's individual needs or risks. Personalized or individual treatment regimens shouldalso be taken into account for emergency medicaldisorders where it helps indeciding potential treatment regimens quickly. Heart diseases are leading cause of morbidity and mortality across the world.Heart diseasesare often asymptomatic for a long period of time before manifesting insidiously or acutely.This often leads to severe complications once an acute cardiovascular event occurs in hitherto asymptomatic undiagnosed chronic cardiac disease; E.g. A cardiac arrest maybe a sudden manifestation in a patient of chronic heart failure.

Heart failure is oftena manifestation arising fromany structural or functional cardiac disorder that impairs the ability of the heart to fill with or pump a sufficient amount of blood throughout the body. Even with the of the best therapies available, heart failure is associated with an annual mortality of around 10%with median survival of around 5 years. Heart failure is a chronic disease; it can, inter alia, occur either following an acute cardiovascular event (like myocardial infarction), or as a consequence of inflammatory or degenerative changes in myocardial tissue. Heart failure patients are classified according to the ACC classification based on risk factors of heart failure, symptoms and clinical functional status which is further stratified into classes I, II, III and IV based on functional capacity using NYHA classification. Stage A and B of ACC classification of heart failure are asymptomatic and will need to be screened in clinics and community at large for early detection, management and prognostication.

Amongst various cardiovascular disorders, heart failure is projected to have the largest increase in prevalencein the next decade (Heidenreich, Circulation. 201 1, 123(8): 933-44). From thepoint of view of public health, it is of critical importance to identify patients who are at risk for heart failure (Stage A) and who have asymptomatic heart failure (Stage B). Rehabilitation in form of changes in diet, behaviour, lifestyle, medications and other factors reduce a patient's likelihood of experiencing worsening of heart failure, particularly if the risk is identified early. However, diagnosing patients at risk for heart failure early in community at large remains difficult, particularly due to the limitations of the currently available methods of heart failure evaluation.

In the past few decades, substantial advances have been made in understanding the underlying pathophysiology and hemodynamics, and in the development of novel pharmacological and interventional therapies. Nevertheless, short- and long-term heart failure -related re -hospitalization and mortality remain high, and demand substantial amounts of healthcare resources. The limited effectiveness of current treatment strategy at the late stage of heart failure necessitates novel interventions measures to overcome the maladaptive molecular processes at sub-clinical stage and to avoid the progression of heart failure to advanced stages.

A variety of biomarkers for heart failure have been identified. B-type natriuretic peptide (BNP) and the N-terminal fragment of the proprotein(NTproBNP) have emerged as clinically useful markers for diagnosis and prognosis of heart failure. A recent study showed that natriuretic peptides also provide a prognosis for individuals at moderate risk of cardiovascular disease without overt symptoms. Unfortunately, these biomarkers do not provide additional information on molecular targets for therapeutic interventions. Additionally, application of biomarkers may not be sufficient for evaluating patients with heart failure, and may requireto be normalised for confounders of these molecules through a combination of multiple data.

The screening strategy to identify asymptomatic patients with underlying cardiac dysfunction is an attractive strategy, since approximately 1% to 2% of the general population may have subclinical heart failure or asymptomatic left ventricular systolic and/or diastolic dysfunction. Current data from various Indian registries show that heart failure burden is increasing in India and the patients are younger by lOyears as compared to Caucasiansand majority of the burden lies below 65 years of age as compared to the patients from high-income countries. The preventive and management strategy for heart failure involves accurate and timely diagnosis of the diseases. Major challenges faced with this are 1) significant gaps in diagnostic criteria for discriminating between noncardiac versus cardiac causes of clinical symptoms 2) complex clinical criteria (like Framingham Score) for diagnosis of different forms of heart failure 3) lack of appropriatediagnostic cutoffs of biomarkers in the presence of confounders like age, gender or renal function. Current diagnostic guidelines consider echocardiography as the gold standard diagnostic test apart from biomarkers, though general practitioners have relied on symptoms and clinical findings followed by 12-lead electrocardiography in diagnosing the condition. However due to complexity of the disease identification of the cases with just clinical signs and symptoms and electrocardiography may be misleading. Therefore, recent guidelines from the National Institute for Health and Clinical Excellence, and the European Society for Cardiology (ESC) on the initial diagnosis of heart failure recommend the use of B -type natriuretic peptide (BNP) tests in combination with clinical assessment. (1,2) Hence development of single point-of-care diagnostics having potential to get integrated at primary health care and ambulatory care settings levels is needed.

Currently available point of care biomarker kitrelies on serology-based diagnosis and requires venepuncture. Majority of the available devices give absolute values and may be cumbersome to interpret for frontline healthcare workers and paramedics. Moreover, these values are confoundedby age, gender, renal function, drug interaction, hydration status and recent stroke or renal failure. These all can confound the values of biomarkers as assessed by these tests. None of the available device provides corrected threshold of biomarkers at present for these confounders. Currently available biomarker tests do not consider clinical scenario, renal function or drug history and give absolute values that may be too complex to comprehend for health care workers or common person.

Consequently, the technical problem underlying the present disclosure could be seen as the provision of improved means and methods for identifying individuals that have an elevated risk of heart failure with integrated primary health care data of the individual. The problem is solved by the embodiments of the present disclosure and described in the claims and in the specification below.

SUMMARY OF INVENTION

Technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and science and to provide a population specific diagnostic solution for heart failure having compatibilitywith all the current technologies measuring BNP levels including point of care devices using test strips. This will facilitatethe patient centric - personalized disease diagnosis and management that can provide early detection and risk assessment of heart failure rapidly and accurately even by the frontline healthcare workers and patients themselves.

Most important feature of the invention is a population specific algorithm specifically derived for Indian patients for accurate diagnosis of heart failure. This algorithm is amalgamation of multiplevariables commonly known as confounders that influence BNP based diagnosis of heart failure. The confounders affecting BNP levels are highly population specific and this algorithm has been developed using the confounders affecting BNP cut-offs in Indians. A cohort of population from India with varying degree of comorbid conditions, clinical and demographic features was used to develop this algorithm. Algorithm requires data related to demographic, biochemical markers and clinical details of the individuals and use this information along with BNP levels for accurate diagnosis of heart failure. For this, clinical diagnosis provided by trained cardiologists was considered as gold standard and was used to assess diagnostic efficacy of the newly developed algorithm.

The most important novelty of the invention is that it provides Indian specific diagnostic threshold of the algorithm using widely accepted biomarker of heart failure - BNP that is adjusted for variety of confounders. This is extremely important when patients have multiple comorbid conditions which affects BNP levels and at times with normal levels of BNP there is a presence of heart failure. This requires extensive assessment of the condition using variety of tests including Echocardiography and other imaging technics. With this simple algorithm true reference of BNP can be utilized even in presence of various influencing factors. This makes innovation extremely useful in rural, tribal, difficult to reach regions and resource limited settings where need of sophisticated technologies and specialised clinicians for ruling out heart failure can be eliminated. This will make the technology assessable to larger area of community.

In this invention the algorithm with its appropriate threshold adjustment in embedded in the IOT based devices hardware and software. The IOT based device that act as a data processing junction and displayer of accurate diagnosis of heart failure. This device can collect and process data from multiple sources and can act as a connecting unit between mobile based data collector platforms and laboratory-based analysers including point of care devices using test strips. The IOT will have hardware and a software component which will facilitate complete processing of the clinical, biomarker and demographic data assessment and derive aresponse for assessment by clinicians/healthcare workers or by patient him/herself for self -monitoring.

One of the most important aspect of the innovation is to have a device that can accept data from mobile phones, autoanalyzer and point of care devices simultaneously. This device is connected with central processing unit and cloud platform for personalized care by patient centric -personalized disease management counselling system for an improved diagnosis, management and prognosis of heart failure in patients.

The third aspect of the innovation is that all it provides details regarding the presence and absence of heart failure in a color coded manner on IOT itself and hence provides an excellent platform for selfcare and monitoring even in difficult to reach and rural areas of the country.

In forth aspectdeveloped utilises the serum biomarker values alongwith a small subset of readily obtainable patient data to improve the diagnosis of asymptomatic/occultheart failureand future risk of developing heart failure in at risk subgroup.

The fifth aspect of the present invention may be carried out manually or may be automated. One or more steps of the invention methods may be automated, e.g., by suitable sensory equipment or a method for machine learning for determining the amount of natriuretic peptide (BNP) biomarkers in a patient sample, that is adjusted for an effect of confounders. So, patients/clinicians/healthcare workers can get the BNP levels assessed through various platforms and can manually add data to the algorithm using mobile device itself and get the diagnosis instantly done. The aspect of the invention is the population specific cut-off/threshold of the algorithm embedded in the IOT device identifies not only patients suffering from heart failure but can also detect patients at risk, irrespective of their comorbidity, biomarker or clinical confounders levels.

Another aspect of the present invention is a detection device (IOT based) which can diagnose risk of heart failureand enable early detection comprises of following steps: a) Collection of variable specific data from mobile devices and biochemical markersanalysers including point of care device using test strips. b) IOT based device acting as a junction for collection and process of demographic, clinical, biomarker data and analysing it using the innovative algorithm for diagnosing heart failure. c) Processing data to derive and interpret corresponding values of biomarker (BNP) and a procedure that provides an accurate risk of heart failure normalised for various confounders for these biomarkers; d) Connecting with central processing unit for personalized care by patient centric - personalized disease management and adverse event prevention counselling system e) Obtaining a simplified model score based on the amount of BNP in the biological sample obtained from the subject and the subject's simplified model factors f) Obtaining the alignment value score compared and corrected by identifying clinical and patient-centric confounders; and g) Providing a diagnosis of heart failure in asymptomatic and at-riskpopulation at community level if the alignment value exceeds a threshold. h) Cloud based storage and record of the data facility embedded with IOT

According to certain aspect of the present invention, a detection device which can diagnosis risk of heart failure, comprising: a) Algorithm embedded in the IOT based device b) Indian population specific threshold of the algorithm for accurate diagnosis of heart failure c) IOT hardware and display presenting colour coded response for heart failure presence d) Device compatible with multiple devices and data collection sources which process data, run the algorithm and diagnose the condition based on appropriate threshold of the score e) Processing data to derive and interpret corresponding values of algorithm consist of: i. based on a static database component comprising static data related to the disease and/or the population; ii. a dynamic database component comprising dynamic data about the population and individual subjects; and iii. a computer modeling component that is configured to model the data in the static database component and the dynamic database component, thereby modeling the disease within the population.

The present invention relates to anIOT device that is for diagnosingheart failure rapidly for early warning in “at-risk” population and monitoring subclinical heart failure or asymptomatic left ventricular systolic and/or diastolic dysfunction. More particularly, the present invention relates to a device for determining a patient's risk of suffering from heart failure that is cost-effective, user-friendly, rapid and also process data to derive and interpret corresponding values of algorithmwhich is furtherconnected with central processing unit for personalized data of patient for personalized disease management and adverse event prevention and counselling system.

The above -described embodiments of the various aspects of the disclosure may be used alone or in any combination thereof without departing from the scope of the disclosure. Specific aspects will become evident from the following more detailed description and the claims.

BRIEF DESCRIPTION OF DRAWINGS

Figure 1: Architect of the device Figure 2: Algorithm based screening of population for heart failure

Figure 3: Schematic representation of mechanism involved in the detection.

DETAILED DESCRIPTION OF INVENTION

The embodiments disclosed herein are not intended to be exhaustive or limit the disclosure to the precise form disclosed in the following detailed description. Rather, the embodiments are chosen and described so that others skilled in the art may utilize their teachings.

It must be noted that as used herein, the singular forms "a", "an", and "the" include plural reference unless the context clearly dictates otherwise. As well, the terms "a" (or "an"), "one or more" and "at least one" can be used interchangeably herein. The term "comprising", "including", "characterized by" and "having" can be used interchangeably. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art/science to which this invention belongs. All publications and patents specifically mentioned herein are incorporated by reference for all purposes including describing and disclosing the chemicals, instruments, statistical analyses and methodologies which are reported in the publications which might be used in connection with the invention. All references cited in this specification are to be taken as indicative of the level of skill in the art. Nothing herein is to be construed as an admission that the invention is not entitled to antedate such disclosure by virtue of prior invention.

The term “healthy” refers to an individual not having heart failure or other related disorders or risk factors.

The term “subject” as used herein relates to animals, preferably mammals, and, more preferably, humans. Preferably, the subject referred to in accordance with the aforementioned method suffers from a myocardial dysfunction, in particular heart failure, and/or myocardial infarction or exhibits the symptoms or clinical parameters and may be asymptomatic or symptomatic and as such may have increased BNP level accompanied therewith, i.e., being at least suspect to be at risk or be suffering from anasymptomatic or symptomatic heart failure.

The term “metabolism” refers to the set of chemical reaction to maintain life in a living organism. Metabolism is divided into two categories: catabolism and anabolism. Anabolism is a set of chemical reactions that use energy to construct components of cells (e.g., protein and nucleic acid synthesis). Catabolism is a set of chemical reactions that breaks down organic matter (e.g., to harvest energy in cellular respiration).

As used herein, the term “biomarker” refers to a molecular species which serves as a biologically derived indicator (such as a biochemical metabolite in the body) of a process, event, or condition (such as aging, disease, or exposure to a toxic substance).

The term “metabolite” is an intermediate or product of metabolism. The term metabolite is generally restricted to small molecules. A “primary metabolite” is a metabolite directly involved in normal growth, development, and reproduction (e.g., alcohol). A “secondary metabolite” is a metabolite not directly involved in those processes, but that usually has an important ecological function (e.g., antibiotics, pigments). Some antibiotics use primary metabolites as precursors, such as actinomycin which is created from the primary metabolite, tryptophan. Rather, for the purposes of the present invention, the term metabolite refers to the small molecules (<1000 Dalton) intermediates and products involved in metabolic pathways such as glycolysis, the citric acid (TCA) cycle, amino acid synthesis and fatty acid metabolism, amongst others.

The term “heart failure” refers to a condition in which the function of the heart is impaired, such that the heart is unable to pump blood at an adequate rate or in adequate volume. Heart failure can be systolic, such that a significantly reduced ejection fraction of blood from the heart and, thus, a reduced blood flow. Therefore, systolic heart failure is characterized by a significantly reduced left ventricular ejection fraction (LVEF), preferably, an ejection fraction of less than 50%. Alternatively, heart failure can be diastolic (HFpEF), i.e. a failure of the ventricle to properly relax that is usually accompanied by a stiffer ventricular wall. The diastolic heart failure causes inadequate filling of the ventricle, and thus affects the blood flow. Thus, diastolic dysfunction also results in elevated end-diastolic pressures. Heart failure may, thus, affect the right heart (pulmonary circulation), the left heart (body circulation) or both. Techniques for measuring heart failure are well known in the art and include echocardiography, electrophysiology, angiography, and the determination of peptide biomarkers, such as the B-type Natriuretic Peptide (BNP) or the N-terminal fragment of its propeptide (NTproBNP), in the blood. It is understood that heart failure can occur permanently or only under certain stress or exercise conditions. Typical symptoms of heart failure include dyspnea, chest pain, dizziness, confusion, pulmonary and/or peripheral edema. Heart failure can be classified as stages A, B, C and D. Stage A: patients at high risk for developing heart failure in the future but no functional or structural heart disorder. Stage B: a structural heart disorder but no symptoms at any stage. Stage C: previous or current symptoms of heart failure in the context of an underlying structural heart problem, but managed with medical treatment. Stage D: patients with refractory heart failure requiring advanced intervention. Heart failure comprises of HFpEF(Heart failure with preserved ejection fraction) with LVEF>50%, HFmrEF (Heart failure with midrange ejection fraction) with LVEF- 40-50% and HRrEF (Heart failure with reduced ejection fraction) with LVEF<40%. The term diastolic heart failure and HFpEF/HFmrEF may be used interchangeably.

The term “diagnosing” as used herein means assessing as to whether a subject having an elevated level of BNP suffers from a pre-existing heart failure, or not. As will be understood by those skilled in the art, such an assessment is usually not intended to be correct for all (i.e., 100%) of the subjects to be identified. The term, however, requires that a statistically significant portion of subjects can be identified (e.g., a cohort in a cohort study). Whether a portion is statistically significant can be determined without further ado by the person skilled in the art using various well-known statistic evaluation tools, e.g., determination of confidence intervals, p-value determination, Student's t-test, Mann-Whitney test etc.

The term "predicting the risk" as used herein refers to assessing the probability that a subject will suffer from heart failure within a certain time window, i.e., the predictive window. However, as will be understood by those skilled in the art, such an assessment is usually not intended to be binding for each and every subject being investigated. The term, however, requires that a prediction can be made for a statistically significant portion of subjects in a proper and correct manner. Whether a portion is statistically significant can be determined by those skilled in the art using various well-known statistic evaluation tools, e.g., determination of confidence intervals, p-value determination, Student's t-test, and Mann-Whitney test etc. as and when needed using available softwares as and when needed with correction for outliers. Details regarding suitable statistic evaluation tools can be found in Dowdy and Wearden, Statistics for Research (John Wiley & Sons, New York 1983). Suitable confidence intervals are at least 90%, at least 95%, at least 97%, at least 98%, or at least 99%. Suitable p-values are 0.1, 0.05, 0.01, 0.005, or 0.0001.

The term "Confounders" as used herein refers to the factors that modify the influence of both dependent and independent variables of the outcomes and can alter sensitivity and specificity. The confounders are broadly categories demographic and clinical. Both of these confounders are patient-centric, however demographic variables are non-modifiable in nature, whereas clinical confounders consisted of modifiable variables.

In another embodiment present invention, a detection kit comprises:

I. Test strip

II. A device for detecting test strip with a sensor for sensing and scanning the test strip and a processing unit to process data from detector of device and data of confounders for processing.

In further embodiment present invention Test strip consist of a. a membrane embedded with biomarkers anti NTproBNP and/or anti BNP in test and control areas; b. a sample loading well; and c. an interpretation window. Sample for the present invention is selected from whole blood, blood serum, heparinized plasma.

An interpretation window stratifying or indicating the subject in 3 categories viz. Low risk/ no heart failure with green color code; At risk/intermediate with orange color code; High risk / definite heart failure with red color code.

In another embodiment present invention a device comprises of: a. a Sensor; b. The detector; and c. Central processing unit with back end data processing (machine learning based diagnostic algorithm).

A device is (compatible handheld instrument) coupled with back end data processing (machine learning based diagnostic algorithm). The said device is IOT based. The said device is accompanied with a sensor to derive and interpret corresponding values of biomarkers such as NTproBNP and/or BNP. Scanning of sample loaded test strip will be done in this area.

The detectors will contain fluorescent conjugates, stabilizers and preservatives. The front - end data (confounders: patient specific clinical and demographic details) will be collected using smart phone display or computer. The data collected from biomarkers and values thereof; and front end data collected will be collectively processed using back end data processing.

An IOT device will read the test strip in around 10 minutes and provide signals to test strip for categorizing patients in colour coded risk categories

The study have combination of eight confounders which plays major role in interpretation of NTproBNP and/or BNP levels in diagnosis of Heart failure. The discrepancy in accurate diagnosis of heart failure in patients can be more accurate if these confounders are adjusted for appropriate cut-off values are established after accounting for these on the measured values of biomarkers.

The confounders are the major contributor of fluctuation in interpretation of BNP levels in diagnosis of Heart failure. The confounders are adjusted with appropriate cut-off values by establishing accounting for these on the measured values of biomarkers. These confounders with appropriate cut-off values reduces discrepancy in patients and helps in accurate diagnosis of heart failure in patients.

Diagnosing according to the present invention includes diagnosing, monitoring, confirmation, sub-classification and prediction of the relevant disease, symptoms or risks thereof. Monitoring relates to keeping track of an already diagnosed disease. Confirmation relates to the strengthening or substantiating a diagnosis already performed using other indicators or markers. Sub-classification relates to further defining a diagnosis according to different subclasses of the diagnosed disease, e.g., defining according to mild and severe forms of the disease.

DIAGNOSTIC ALGORITHM AND IOT DEVICE

1) List of confounders

The study will have combination of eight confounders as mentioned belowinTable 1:

■■ ■ DList of biomarkers: B-type natriuretic peptide (BNP) The proposed innovation aims to provide accurate prediction of Heart failure using BNP levels. This will involve estimation of three more biochemical parameters - blood glucose, hemoglobin and hsCRP. However, these three biochemical tests will be used only to improve the accuracy of BNP based diagnosis of Heart failure. ■■ ■ Algorithm of computer modelling

Based on a diagnostic efficacy study, following algorithm is developed, however the algorithm will be further updated with machine learning and big data analysis. This involves combination of following demographic, clinical and biochemical variables with appropriate weightage based on their confounding effect on BNP levels. This algorithm was tested for its diagnostic efficacy against clinical confirmation of Heart failure. Here absolute values of variables (such as age in years, blood glucose, and hemoglobin) are added, whereas for sex in case of males, 0 and 1 in case of female is used.

(-0.08) + 1.175*(Age) + 0.874*(BMI) + 0.794*(MBP) + 0.917*(Sex) + 0.676*(Blood glucose) + (-0.002)*(Hb) + (-1.28)*(eGFR) + 1.2634*(BNP) + 0.489*(hsCRP)

Abbreviations:

BMI - Body Mass Index

MBP - Mean Blood Pressure

Hb - Hemoglobin

EGFR - Estimated Glomerular Filtration Rate

BNP - B-type natriuretic peptide hsCRP - High Sensitivity C-Reactive Protein

This algorithm is integrated in the IOT based device that has compatibility with various point-of care devices, autoanalyzer and other laboratory-based devices for capturing data of laboratory parameters including BNP. ■■ ■ The appropriate cut-off values of the confounders and with example calculation of the same

Algorithm

(-0.08)+1.175*(Age)+0.874*(BMI)+0.794*(MBP)+0.917*(Sex)+0.676*(Blood glucose)+(-0.002)*(Hb)+(-1.28)*(eGFR)+1.2634*(BNP)+0.489*(hsCRP)

Cut off/threshold for diagnosis - 260

Example calculation a) Algorithm in clinically confirmed positive patient

(-0.08)+1.175*(62)+0.874*(27.3)+0.794*(107)+0.917*(l)+0.676*(279)+(- 0.002)*(12.1)+(-1.28)*(80.67)+1.2634*(45.51)+0.489*(2) = 326.6 (above the proposed threshold of the algorithm)

Above calculation showed the algorithm calculation in clinically confirmed case of heart failure. This female patient has normal levels of BNP (below lOOpg/mL), however with use of the algorithm the patient was identified as heart failure positive. This was in accordance with clinical diagnosis. b) Algorithm in clinically confirmed negative patient

(-0.08)+1.175*(62)+0.874*(28)+0.794*(75)+0.917*(0)+0.676*(119)+(-0.002)*(11.3)+(- 1.28)*( 120.37)+ 1.2634*(104)+0.489*( 1.86) = 215.7 (below the proposed threshold of the algorithm) Above calculation showed the algorithm calculation in clinically confirmed negative case of heart failure. The algorithm improved diagnostic accuracy of BNP and confirm negative case, similar to clinical diagnosis. ■■ ■ Algorithm to derive Sensitivity and Specificity and its explanation.

The algorithm is developed and tested on a heterogenous cohort having healthy, diseased, stable, at-risk and comorbid patients. All the patients underwent clinical diagnosis of heart failure using various laboratory, imagining and clinical tests. The clinical diagnosis was made by trained cardiologists and they were blinded with other forms of statistical analysis. This diagnosis was considered as gold standard irrespective of BNP level of the patients. Through series of statistical analysis, group of variables were identified which affected levels of BNP in Indian subjects. It was observed that in 25% of the patients with clinically confirmed heart failure, the BNP levels were normal. However, with this algorithm these patients were identified. Algorithm based score was generated for each individual patient and through receiver operating curve analysis diagnostic threshold of the algorithm was developed. Initially three cutoffs were identified, of which cutoff with most superior diagnostic accuracy as compared to gold standard was selected for further analysis. The details of algorithm-based diagnosis are as follows:

This indicated accurate diagnosis of heart failure even in presence of confounders using the novel algorithm that is specific for Indians. ■■ ■ Comparative study of Sensitivity and Specificity

Currently there is no specific algorithm is available for Indian patients with respect to BNP’s sensitivity and specificity. However recently an Indian company VIDAS® NT- proBNP2, has prepared a diagnostic kit for heart failure using NT -proBNP (different protein than BNP) as biomarker. This kit has adjusted the values of NT -proBNP for only one confounder - Age and have claimed sensitivity and specificity as follows in Table 2:

*Adapted from: https://www.biomerieuxindia.in/product/vidas-nt-probnp2 Similarly, research from Indian subject is still lacking in terms of accuracy of BNP for heart failure diagnosis, across the globe it is reported as follows in Table 3:

Indian consensus document was published in 2014 regarding BNP guided diagnosis for heart failure, however the studies used to derive the consensus were majorly from outside India having non-Indian population. The claimed sensitivity and specificity of the trials for BNP are as follows in Table 4:

*Satyamurthy I, Dalal JJ, Sawhney JP, Mohan JC, Chogle SA, Desai N, Sathe SP, Maisel AS. The Indian consensus document on cardiac biomarker. Indian heart journal. 2014 Jan l;66(l):73-82.

Claims

CLAIMS We Claim,

1. A detection kit for evaluation, diagnosis and prognosis of heart failure comprises:

(a) test strip with a membrane embedded with biomarkers having a sample loading well for collection of sample and an interpretation window;

(b) a device with detecting sensor for sensing and scanning the test strip;

(c) the detector with fluorescent conjugates, stabilizers and preservatives for reading and deriving values of biomarkers; and

(d) a processing unit with data from detector and data of confounders for processing.

2. The detection kit as claimed in claim 1 , wherein biomarkers are selected from anti B-type natriuretic peptide (BNP) and anti N-terminal fragment of its pro-peptide (NTproBNP).

3. The detection kit as claimed in claim 1, wherein sample is selected from whole blood, blood serum, heparinized plasma.

4. The detection kit as claimed in claim 1 , wherein an interpretation window consist of color codes stratifying or indicating the subject sample in three categories as low risk for heart failure with green color code; as intermediate risk with orange color code; as a high risk for heart failure with red color code.

5. The detection kit as claimed in claim 1 , wherein the confounders are patient specific clinical and demographic details.

6. The detection kit as claimed in claim 5, wherein confounders are Age, Sex, BMI, Mean Blood pressure, e-GFR, haemoglobin, blood sugar, hs-CRP. The detection kit as claimed in claim 1, wherein the device processes the data collected from biomarkers and data of confounders are collectively processed. A detection kit as claimed in claim 7 wherein, confounders are adjusted for appropriate cut-off values which are established after the measured values of biomarkers. The detection kit as claimed in claim 7, wherein the back end data processing is machine learning based diagnostic algorithm. The detection kit as claimed in claim 1, wherein a device having compatibility with mobile and laboratory-based data collection platforms. The detection kit as claimed in claim 1, wherein process for working of the said detection kit comprises steps of : a) collection of data from devices of biochemical markers analysers including point of care device using test strips; b) a processing unit for collection and process of demographic, clinical, biomarker data and analysing it using the innovative algorithm for diagnosing heart failure; c) processing data to derive and interpret corresponding values of biomarker (BNP) and a procedure that provides an accurate risk of heart failure normalised for various confounders for these biomarkers; d) connecting with central processing unit for personalized care by patient centric- personalized disease management and adverse event prevention counselling system e) obtaining a simplified model score based on the amount of BNP in the biological sample obtained from the subject and the subject's simplified model factors; f) obtaining the alignment value score compared and corrected by identifying clinical and patient-centric confounders; and g) providing a diagnosis of heart failure in asymptomatic and at-risk population at community level if the alignment value exceeds a threshold and storage of recorded data.