Use of cardiac troponin and galectin-3 for differentiating type I and type II cardiac infarction

Abstract

The present invention provides methods for determining whether a subject suspected of having myocardial infarction is experiencing type I myocardial infarction or type II myocardial infarction. In particular, systems and methods are provided for processing the gender, age, and cardiac troponin concentration of a subject, as well as the galectin-3 (Gal-3) concentration of a subject, employing probability scoring based on a decision tree algorithm.

Description

[0001] Related application information

[0002] This application claims priority to U.S. application No. 63 / 401,335, filed on August 26, 2022, and U.S. application No. 63 / 464,412, filed on May 5, 2023, the contents of each of which are incorporated herein by reference. Technical Field

[0003] Methods are provided for determining whether a subject suspected of having a myocardial infarction is experiencing a type I or type II myocardial infarction. Specifically, systems and methods are provided that utilize a probability score based on a decision tree algorithm to process the subject's sex, age, and cardiac troponin concentration, as well as the subject's galectin-3 (Gal-3) concentration. Background Art

[0004] In the United States, more than six million patients are evaluated for suspected acute coronary syndrome (ACS) in hospitals each year. The most serious diagnosis associated with ACS (which typically presents as chest pain and related symptoms) is myocardial infarction (MI). There are many types of MI. Type I is the classic type associated with plaque rupture or erosion. Type I MI typically causes platelet activation, thrombosis, and eventual coronary artery obstruction, thereby preventing blood from flowing to the muscle (myocardium) supplied by the arteries. Typically, patients diagnosed with Type I MI are immediately taken to a catheterization laboratory for coronary angiography, with or without percutaneous coronary intervention (PCI; balloon and stent placement), or less frequently, coronary artery bypass grafting (CABG) surgery when necessary.

[0005] Type II MI is most often attributed to an imbalance in myocardial oxygen supply / demand, with or without atherosclerosis and endothelial dysfunction, in which the myocardial demand for oxygen increases but cannot be met by supply. The increased demand is caused by problems such as sepsis, severe anemia, and / or arrhythmias. Treatment of a type II MI generally involves addressing the underlying pathology. PCI or CABG alone is unlikely to be effective because the problem is not primarily due to arterial blockage.

[0006] Because of differences in etiology and treatment, differentiating between MI types is crucial for providing optimal care for patients. Currently, this distinction is typically based on clinical and electrocardiographic (EKG) criteria, which are not always accurate. Summary of the Invention

[0007] Provided herein are methods for determining whether a subject suspected of having a myocardial infarction is experiencing a Type I myocardial infarction or a Type II myocardial infarction.

[0008] In some embodiments, the method comprises: a) obtaining subject values for the subject, wherein the subject values comprise: i) a subject sex value; ii) a subject age value; iii) a subject initial cardiac troponin concentration of an initial sample from the subject; and iv) a subject galectin-3 (Gal-3) concentration of an initial sample from the subject; b) processing the subject's sex, age, and cardiac troponin value using a processing system such that an algorithm index score for the subject is determined, wherein the processing system comprises: i) a computer processor, and ii) a non-transitory computer memory comprising one or more computer programs and a database, wherein the one or more computer programs comprise an additive tree algorithm, wherein the database comprises at least M decision trees, wherein each individual decision tree comprises at least two predetermined splitting variables and at least three predetermined terminal node values, wherein the at least two predetermined The splitting variables are initial cardiac troponin concentration thresholds, gender values and / or age values, wherein the one or more computer programs in combination with the computer processor are configured to: i) apply the subject's initial cardiac troponin concentration, the subject's gender value and / or the age value to the database to determine a terminal node value for each of the at least M decision trees, and ii) apply the additive tree algorithm to: (a) determine a combined value from the M terminal node values, and (b) process the combined value to determine the algorithm index score that the subject is experiencing myocardial infarction, wherein M is an integer of at least 2; c) report the algorithm index score for the subject determined by the processing system; d) generate a probability score based on: i) the subject's Gal-3 concentration and ii) the algorithm index score; and e) determine whether the subject has type I myocardial infarction or type II myocardial infarction based on the probability score.

[0009] In some embodiments, the subject value also includes a first subsequent cardiac troponin concentration, a second subsequent cardiac troponin concentration, or a first subsequent cardiac troponin concentration and a second subsequent cardiac troponin concentration for a corresponding first subsequent sample and / or second subsequent sample from the subject. In some embodiments, at least two predetermined split variables are: a cardiac troponin rate of change threshold, an initial cardiac troponin concentration threshold, or a combination thereof; and a gender value and / or an age value. In some embodiments, the combination of the one or more computer programs and the computer processor is configured to apply the rate of change algorithm to determine a subject cardiac troponin rate of change value from at least two of the following: the subject's initial cardiac troponin concentration, the first subsequent cardiac troponin concentration, and the second subsequent cardiac troponin concentration.

[0010] In some embodiments, M is an integer from 2 to 1000. In other embodiments, M is an integer from 2 to 100000. The integer selected for M will be determined based on the optimal number of trees for the boosting algorithm, which can be determined using conventional techniques known in the art.

[0011] In some embodiments, the method comprises: a) obtaining subject values for the subject, wherein the subject values comprise: i) a subject sex value; ii) a subject age value; iii) a subject initial cardiac troponin concentration for an initial sample from the subject; iv) a subject galectin-3 (Gal-3) concentration for an initial sample from the subject; and v) a first subsequent cardiac troponin concentration, a second subsequent cardiac troponin concentration, or a first subsequent cardiac troponin concentration and a second subsequent cardiac troponin concentration for a corresponding first subsequent sample and / or second subsequent sample from the subject; b) processing the subject's sex, age, and cardiac troponin values using a processing system such that an algorithm index score for the subject is determined, wherein the processing system comprises: i) a computer processor, and ii) non-transitory computer memory comprising one or more computer programs and a database, wherein the one or more computer programs comprise: a rate of change algorithm and an additive tree algorithm, and wherein the database comprises at least M decision trees, wherein each individual decision tree comprises at least two predetermined splitting variables and at least three predetermined terminal node values, wherein the at least two predetermined splitting variables are: a cardiac troponin rate of change threshold; value, an initial cardiac troponin concentration threshold value, or a combination thereof; and a gender value and / or an age value, wherein the one or more computer programs in combination with the computer processor are configured to: i) apply the rate of change algorithm to determine a subject cardiac troponin rate of change value from at least two of: the subject's initial cardiac troponin concentration, the first subsequent cardiac troponin concentration, and the second subsequent cardiac troponin concentration, ii) apply the subject cardiac troponin rate of change value, the subject's initial cardiac troponin concentration, the subject's gender value, and / or the age value to the database to determine the at least terminal node values for each of M decision trees, and iii) applying the additive tree algorithm to: (a) determine a combined value from the M terminal node values, and (b) process the combined value to determine the algorithm index score that the subject is experiencing a myocardial infarction; wherein M is an integer of at least 2, and c) report the algorithm index score for the subject determined by the processing system; d) generate a probability score based on: i) the Gal-3 concentration of the subject and ii) the algorithm index score; and e) determine whether the subject has a Type I myocardial infarction or a Type II myocardial infarction based on the probability score.

[0012] In some embodiments, based on the probability score, the subject is determined to have a Type I myocardial infarction. In some embodiments, based on the probability score, the subject is determined to have a Type II myocardial infarction.

[0013] In some embodiments, obtaining a subject value comprises receiving the subject value from a testing laboratory, from the subject, from an analytical test system, and / or from a handheld or point-of-care test device. In some embodiments, the processing system further comprises the analytical test system and / or the handheld or point-of-care test device.

[0014] In some embodiments, obtaining a subject value comprises receiving the subject value electronically.

[0015] In some embodiments, the initial cardiac troponin concentration, the first cardiac troponin concentration, and / or the second cardiac troponin concentration are obtained by performing a cardiac troponin detection assay. In some embodiments, the cardiac troponin detection assay comprises an immunoassay. In some embodiments, the cardiac troponin detection assay is a single molecule detection assay.

[0016] In some embodiments, the Gal-3 concentration is obtained by performing a Gal-3 detection assay. In some embodiments, the Gal-3 detection assay comprises an immunoassay. In some embodiments, the Gal-3 detection assay is a single molecule detection assay.

[0017] In some embodiments, the method further comprises manually or automatically inputting the subject values into the processing system. In some embodiments, the subject values are input into the processing system using a combination of manual and automatic input. For example, age and / or sex can be manually input, and Gal-3 concentration and / or cardiac troponin concentration can be automatically input.

[0018] In some embodiments, the cardiac troponin is cardiac troponin I (cTnI). In some embodiments, the cardiac troponin is cardiac troponin T (cTnT). In some embodiments, the cardiac troponins are cTnI and cTnT.

[0019] In some embodiments, the initial sample is taken from the subject at an emergency room, urgent care clinic, mobile clinic, rehabilitation facility, nursing facility, ambulance, the subject's workplace, the subject's home, or any combination thereof.

[0020] In some embodiments, the subject is a human.

[0021] In some embodiments, the initial sample from the subject comprises a blood, serum, or plasma sample. In some embodiments, the first subsequent sample and / or the second subsequent sample comprises a blood, serum, or plasma sample.

[0022] In some embodiments, the M decision trees are at least 100 different decision trees. In some embodiments, the M decision trees are at least 800 different decision trees.

[0023] Other embodiments of the present disclosure will become apparent from the following detailed description and associated drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Comparison of the area under the curve (AUC) of the algorithm index score alone versus the algorithm index score plus baseline Gal-3 for type I versus type II MI in a population of n=123 patients using available baseline troponin samples.

[0025] Figure 2 Comparative plot of the AUC of the algorithm index score alone versus the algorithm index score plus baseline Gal-3 for Type I MI versus Type II MI in a population of n=86 patients using available serial troponin samples.

[0026] Figure 3 Distribution plots showing predicted probabilities from logistic regression for Gal-3 plus baseline MI3 score are shown, with the horizontal line indicating the optimal cutoff value.

[0027] Figure 4 Distribution plots showing predicted probabilities from logistic regression for Gal-3 plus serial MI3 scores are shown, with the horizontal line indicating the optimal cutoff value. DETAILED DESCRIPTION

[0028] Previously, an algorithm that takes in patient age, sex, and two high-sensitivity troponin measurements was used to differentiate patients with MI (type I alone or a combination of type I and type II) from patients without MI. Disclosed herein is a method for applying this algorithm using initial and serial troponin measurements, as well as galectin-3 (Gal-3) concentrations, to differentiate type I MI from type II MI, thereby enabling better patient management in identifying patients at highest risk for complications and those who should undergo invasive management.

[0029] definition

[0030] As used herein, the terms "comprise," "include," "comprising," "having," "may," "containing," and variations thereof are intended to be open-ended conjunctions, terms, or words that do not exclude the possibility of additional actions or structures. The singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. The present disclosure also encompasses other embodiments that "comprise," "consist of," and "consist essentially of" the embodiments or elements presented herein, whether explicitly stated or not.

[0031] For the recitation of numerical ranges herein, each intervening number therebetween is expressly contemplated with equal precision. For example, for the range 6-9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0-7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are expressly contemplated.

[0032] Unless otherwise defined herein, scientific and technical terms used in conjunction with this disclosure shall have the meanings commonly understood by those of ordinary skill in the art. The meaning and scope of the terms should be clear; however, if there is any implicit ambiguity, the definitions provided herein shall take precedence over any dictionary or external definitions. In addition, unless the context requires otherwise, singular terms shall include plural forms, and plural terms shall include the singular form.

[0033] As used herein, the term "acute coronary syndrome" or "ACS" refers to a group of conditions in which part of the heart muscle stops working properly or dies because of reduced blood flow in the coronary arteries. The most common symptom is chest pain, often radiating to the left arm or the angle of the jaw, that is like pressure, and is associated with nausea and sweating. ACS is usually caused by one of three problems: S and T wave (ST) elevation myocardial infarction (STEMI), non-ST elevation myocardial infarction (NSTEMI), or unstable angina (Torres and Moayedi, 2007 Clin. Geriatr. Med. 23(2):307-25, vi; which is incorporated herein by reference in its entirety). These types are named non-ST elevation myocardial infarction and ST elevation myocardial infarction based on their appearance on the electrocardiogram (EKG). There can be some variation as to which forms of myocardial infarction (MI) are classified as acute coronary syndrome. ACS should be distinguished from stable angina, which occurs during activity and is relieved at rest. In contrast to stable angina, unstable angina develops suddenly, often at rest or with minimal activity, or with activity levels lower than the individual's previous angina ("ascending angina"). New-onset angina is also considered unstable angina because it indicates a new problem in the coronary arteries. While ACS is often associated with coronary thrombosis, it can also be associated with cocaine use. Anemia, bradycardia (a slow heart rate), or tachycardia (a fast heart rate) can also cause cardiac chest pain. The primary symptom of reduced blood flow to the heart is chest pain, experienced as tightness around the chest and radiating to the left arm and left angle of the jaw. This may be associated with diaphoresis (sweating), nausea and vomiting, and shortness of breath. In many cases, this sensation is "atypical," with the pain experienced differently or even absent altogether (this is more likely in women and those with diabetes). Some patients may report palpitations, anxiety, or a sense of impending doom (fear of death), as well as a feeling of acute illness. Patients with chest pain frequently present to hospital emergency rooms. However, chest pain can have many causes: stomach upset (e.g., indigestion), pulmonary distress, pulmonary embolism, dyspnea, musculoskeletal pain (muscle strain, bruise), indigestion, pneumothorax, or non-coronary cardiac conditions such as acute coronary syndrome (ACS). As mentioned above, ACS is usually one of three conditions involving the coronary arteries: ST-elevation myocardial infarction, non-ST-elevation myocardial infarction, or unstable angina. These types are named non-ST-segment elevation myocardial infarction (NSTEMI) and ST-segment elevation myocardial infarction (STEMI) based on their appearance on the electrocardiogram (EKG). ACS is often associated with coronary artery thrombosis. Doctors must decide whether a patient has a life-threatening ACS. In the case of this cardiac event, rapid treatment by unblocking the blocked coronary artery is crucial to prevent further loss of heart tissue.

[0034] As used herein, "suspected of having an acute coronary syndrome" means that the subject has at least one symptom of an acute coronary syndrome as described above (e.g., chest pain (experienced as tightness around the chest, often radiating to the left arm and left angle of the jaw), diaphoresis (sweating), nausea and vomiting, shortness of breath).

[0035] A "subject" or "patient" can be human or non-human, and can include, for example, animal strains or species used as "model systems" for research purposes, such as the mouse models described herein. Likewise, subjects can include adults or adolescents (e.g., children). In addition, a patient can mean any organism, preferably a mammal (e.g., human and non-human) that can benefit from the administration of the compositions contemplated herein. Examples of mammals include, but are not limited to, any member of the class mammals: humans, non-human primates, such as chimpanzees and other apes and monkey species; farm animals, such as cattle, horses, sheep, goats, and pigs; domestic animals, such as rabbits, dogs, and cats; laboratory animals, including rodents, such as rats, mice, and guinea pigs, etc. Examples of non-mammals include, but are not limited to, birds, fish, etc. In one embodiment, the mammal is a human.

[0036] Preferred methods and materials are described below, but methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. The materials, methods, and examples disclosed herein are illustrative only and are not intended to be limiting.

[0037] Differentiating between type I and type II myocardial infarction

[0038] The present invention provides systems and methods for determining whether a subject suspected of having a myocardial infarction is experiencing a Type I myocardial infarction or a Type II myocardial infarction.

[0039] The disclosed method utilizes two predictors to classify the type of myocardial infarction (MI): 1) the algorithm index score and 2) the galectin-3 concentration.

[0040] 1. Algorithm Index Rating

[0041] The first predictor is an algorithm index score. Any machine learning algorithm known in the art can be used in the methods of the present disclosure to generate an algorithm index score. In some embodiments, the machine learning algorithm is an adaptive exponential modeling (AIM) algorithm. In other embodiments, the machine learning algorithm is a random forest algorithm. In other embodiments, the at least one machine learning algorithm is a logistic regression algorithm. In selected embodiments, the machine learning algorithm is an additive decision tree-based algorithm.

[0042] The algorithmic index score can be generated using methods such as those described in Than, MP et al., Circulation. 2019; 140:899-909, U.S. Patent No. 11,147,498, and U.S. Patent Application No. 17 / 398,589, which are incorporated herein by reference in their entirety.

[0043] In some embodiments, the algorithmic index score is generated using an additive decision tree-based algorithm to process the subject's cardiac troponin concentration and optionally the subject's first, second, or first and second subsequent cardiac troponin concentrations, as well as the subject's age and the subject's sex, to calculate the probability that the patient is experiencing a myocardial infarction (MI). These variable inputs are evaluated via a decision tree-based statistical calculation to estimate the likelihood that the patient is experiencing a Type I MI or a Type II MI, so that the subject can be stratified into the appropriate category.

[0044] In certain embodiments, the systems and methods herein account for temporal variation between sample collections by determining the rate of change of cardiac troponin based on the precise or nearly precise time (e.g., in minutes) between the first and second sample collections from a subject.

[0045] In certain embodiments, the systems and methods herein address age variables by determining the influence of the age decile to which the patient belongs. In some embodiments, the subject's age value is the subject's age (in years) or a set value based on an age range. In selected embodiments, the set value is determined based on the following range: 0-29 years, 30-39 years, 40-49 years, 50-59 years, 60-69 years, 70-79 years, and 80 years or more.

[0046] In some embodiments, the systems and methods herein address gender differences by classifying patients into male and female gender characteristics. In selected embodiments, the gender value is a number (e.g., 1.0) for males and another number (e.g., 0) for females.

[0047] In some embodiments, the systems and methods include a computer processor and a non-transitory computer memory component, the non-transitory computer memory component including one or more computer programs configured to access a database, wherein the one or more computer programs include an additive tree algorithm and optionally a rate of change algorithm, and wherein the database includes at least M decision trees, wherein each individual decision tree includes at least two (e.g., two, three, four, or more) predetermined splitting variables and at least three (e.g., three, four, five, six, or more) predetermined terminal node values, wherein the at least two predetermined splitting variables are: an initial cardiac troponin concentration threshold, a sex value, and / or an age value; or a cardiac troponin rate of change threshold, an initial cardiac troponin concentration threshold, or a combination thereof, and a sex value and / or an age value, in combination with a computer processor, configured to: i) apply the subject's initial cardiac troponin concentration, the subject's sex value, and / or the age value to the database to determine In some embodiments, the non-transitory computer memory component further comprises a database.

[0048] The additive tree algorithm may include at least M decision trees. Each individual decision tree includes at least two predetermined splitting variables and at least three predetermined terminal node values. M may be an integer of at least 2. In some embodiments, M is an integer from 2 to 100,000. The integer selected for M will be determined based on the optimal number of trees for the boosting algorithm and can be determined using conventional techniques known in the art. For example, M can be 10-100000, 100-100000, 200-100000, 300-100000, 400-100000, 500-100000, 600-100000, 700-100000, 800-100000, 900-100000, 1000-100000, 2000-100000, 3000-100000, 4000-100000, 5000-100000, 6000-100000, 7000-100000, 8000-100000, 9000 00-100000, 10-90000, 100-90000, 200-90000, 300-90000, 400-90000, 500-90000, 600-90000, 700-90000, 800-90000, 900-90000, 1000-90000, 2000-90000, 3000-90000, 4000-90000, 5000-90000, 6000-90000, 7000-90000, 8000-90000, 9000-90000, 10-800 00, 100-80000, 200-80000, 300-80000, 400-80000, 500-80000, 600-80000, 700-80000, 800-80000, 900-80000, 1000-80000, 2000-80000, 3000-80000, 4000-80000, 5000-80000, 6000-80000, 7000-80000, 8000-80000, 9000-80000, 10-70000, 100-70000, 200 -70000, 300-70000, 400-70000, 500-70000, 600-70000, 700-70000, 800-70000, 900-70000, 1000-70000, 2000-70000, 3000-70000, 4000-70000, 5000-70000, 6000-70000, 7000-70000, 8000-70000, 9000-70000, 10-60000, 100-60000, 200-60000, 300-60000,400-60000、500-60000、600-60000、700-60000、800-60000、900-60000、1000-60000、2000-60000、3000-60000、4000-60000、5000-60000、6000-60000、7000-60000、8000-60000、9000-60000、10-50000、100-50000、200-50000、300-50000、400-50000、500-50000、600-50000、700-50000、800-50000、900-50000、1000-50000、2000-50000、3000-50000、4000-50000、5000-50000、6000-50000、7000-50000、8000-50000、9000-50000、10-40000、100-40000、200-40000、300-40000、400-40000、500-40000、600-40000、700-40000、800-40000、900-40000、1000-40000、2000-40000、3000-40000、4000-40000、5000-40000、6000-40000、7000-40000、8000-40000、9000-40000、10-30000、100-30000、200-30000、300-30000、400-30000、500-30000、600-30000、700-30000、800-30000、900-30000、1000-30000、2000-30000、3000-30000、4000-30000、5000-30000、6000-30000、7000-30000、8000-30000、9000-30000、10-20000、100-20000、200-20000、300-20000、400-20000、500-20000、600-20000、700-20000、800-20000、900-20000、1000-20000、2000-20000、3000-20000、4000-20000、5000-20000、6000-20000、7000-20000、8000-20000、9000-20000、10-10000、100-10000、200-10000、300-10000、400-10000、500-10000, 600-10000, 700-10000, 800-10000, 900-10000, 1000-10000, 2000-10000, 3000-10000, 4000-10000, 5000-10000, 6000-10000, 7000-100,00, 8000-10000, 9000-10000 , 10-1000, 100-1000, 200-1000, 300-1000, 400-1000, 500-1000, 500-2000, 600-1000, 700-1000, 800-1000, 900-1000, 10-900, 100-900, 200-900, 300-900, 400-900, 500-900, 600- 900, 700-900, 800-900, 10-800, 100-800, 200-800, 300-800, 400-800, 500-800, 600-800, 700-800, 10-700, 100-700, 200-700, 300-700, 400-700, 500-700, 600-700, 10-600, 100-6 In some embodiments, M is at least 100, at least 200, at least 300, at least 400, at least 500, at least 600, at least 700, at least 800, at least 900, at least 1000, at least 1500, or at least 2000. In some embodiments, M is 1 because the algorithm comprises a single decision tree.

[0049] In some embodiments, the algorithm index score is based on a non-weighted or weighted combination of the values of each node. In another embodiment, the combined value of the M terminal nodes is a weighted combined value, which is represented by the following formula: Where T i represents a separate decision tree, X represents the subject value, B i represents the at least two splitting variables, a i represents the weight value, and In order to solve the estimated indicator score, the combined value of the M terminal nodes is further processed using the following equation: Where p1 represents the estimated ACS risk. In some aspects, such as in the Examples below, p1 is solved for as an algorithmic index score.

[0050] In some aspects, the algorithm may generate hundreds or thousands of individual tree scores that are combined into a sum score (SS) and an algorithm index score using the following general formula, where Indicates the average of the results.

[0051]

[0052] For example, the algorithm can generate 987 individual tree scores that are combined into the SS using the formula below and into the algorithm index score using the formula provided above.

[0053]

[0054] In some embodiments, the predetermined splitting variables and / or predetermined terminal node values are empirically derived from analysis of population data. In other embodiments, analysis of population data comprises employing a machine learning algorithm as described above. For example, analysis of population data can comprise employing an algorithm based on additive decision trees.

[0055] In some embodiments, the at least two predetermined splitting variables include an initial cardiac troponin concentration threshold, a gender value, and / or an age value. Alternatively, in some embodiments, the at least two predetermined splitting variables include: a cardiac troponin rate of change threshold or an initial cardiac troponin concentration threshold; and a gender value; and / or an age value. In some embodiments, the at least two predetermined splitting variables are selected from the group consisting of: a cardiac troponin rate of change threshold, an initial cardiac troponin concentration threshold, a gender value, and an age value. Thus, in some embodiments, the computer program further applies the additive tree algorithm to: apply the rate of change algorithm to determine a subject's cardiac troponin rate of change value from at least two of: the subject's initial cardiac troponin concentration, the first subsequent cardiac troponin concentration, and the second subsequent cardiac troponin concentration.

[0056] In some embodiments, the algorithm index score is a baseline algorithm index score.The baseline algorithm index score utilizes the subject's sex, age, and initial cardiac troponin concentration.

[0057] In some embodiments, the algorithm index score is a serial algorithm index score. The serial algorithm index score utilizes the subject's sex, age, initial cardiac troponin concentration, and a first subsequent cardiac troponin concentration, a second subsequent cardiac troponin concentration, or a first subsequent cardiac troponin concentration and a second subsequent cardiac troponin concentration corresponding to a subsequently obtained sample. In addition to the first subsequent sample or the first subsequent sample and the second subsequent sample, the method can also use any number of subsequent samples. For example, a third subsequent sample, a fourth subsequent sample, a fifth subsequent sample, a sixth subsequent sample, a seventh subsequent sample, etc. The subsequent samples can be obtained at any time interval, such as minutes, hours, or days, after the previous sample.

[0058] In some embodiments, an algorithm index score is reported as a result on a scale of 0 to 100. For example, an algorithm index score may be initially generated on a scale of 0 to 1, but multiplied by 100 to increase interpretability.

[0059] In some embodiments, the method further comprises reporting the algorithm index score for the subject.In some embodiments, the processing system generates an algorithm index score result and / or report based on the analysis.

[0060] 2. Probability Scoring

[0061] The Galectin-3 concentration, together with the algorithmic index score, can generate a probability score. Any machine learning algorithm known in the art can be used in the methods of the present disclosure to generate a probability score. In some embodiments, the machine learning algorithm is an adaptive exponential modeling (AIM) algorithm. In other embodiments, the machine learning algorithm is a random forest algorithm. In other embodiments, the machine learning algorithm is a boosted tree algorithm, a naive Bayes classification, a support vector machine, a K-nearest neighbor (KNN), a K-means clustering, a neural network, or any combination thereof.

[0062] In other embodiments, at least one machine learning algorithm is a regression algorithm (e.g., logistic regression).

[0063] In selected embodiments, the machine learning algorithm is a logistic regression model. Using available statistical software, such as R, SPSS, Systat, STATA, Eviews, AMOS, SAS, Python, and Mplus, the algorithm index score and the baseline concentration of Galectin-3 can be entered into the logistic regression model. Any suitable logistic regression model can be used, and the methods described herein are not limited in this respect. The statistical software is used to generate a predicted probability for the model to provide a predicted probability of Type I MI.

[0064] The probability score provides an understanding of the likelihood that the patient will experience a Type I MI (e.g., models the probability of a Type I MI). To determine whether a subject is experiencing a Type I or Type II myocardial infarction, the probability score can be compared to a cutoff score. A minimum distance method can be used to determine the optimal cutoff for a probability score ranging from 0 to 1. For example, a Type I MI may be above the cutoff score, while a probability score below the cutoff score indicates a Type II MI.

[0065] In some embodiments, a clinician or other medical personnel can compare a subject's probability score to a cutoff score. The cutoff score can be provided in a product insert or other publication, or provided on a website or mobile device (e.g., such as through an app).

[0066] In some embodiments, the cutoff score is 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 0.2 3. 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.30, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.40, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.50, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0. In some embodiments, the cutoff score is 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98 or 0.99. In selected embodiments, the cutoff score is 0.10. In selected embodiments, the cutoff score is 0.11. In selected embodiments, the cutoff score is 0.12. In selected embodiments, the cutoff score is 0.13. In selected embodiments, the cutoff score is 0.14. In selected embodiments, the cutoff score is 0.15. In selected embodiments, the cutoff score is 0.16. In selected embodiments, the cutoff score is 0.17. In selected embodiments, the cutoff score is 0.18. In selected embodiments, the cutoff score is 0.19. In selected embodiments, the cutoff score is 0.20. In selected embodiments, the cutoff score is 0.21. In selected embodiments, the cutoff score is 0.22. In selected embodiments, the cutoff score is 0.23. In selected embodiments, the cutoff score is 0.24. In selected embodiments, the cutoff score is 0.25. In selected embodiments, the cutoff score is 0.26. In selected embodiments, the cutoff score is 0.27. In selected embodiments, the cutoff score is 0.28. In selected embodiments, the cutoff score is 0.29. In selected embodiments, the cutoff score is 0.30. In selected embodiments, the cutoff score is 0.31. In selected embodiments, the cutoff score is 0.32. In selected embodiments, the cutoff score is 0.33.In selected embodiments, the cutoff score is 0.34. In selected embodiments, the cutoff score is 0.35. In selected embodiments, the cutoff score is 0.36. In selected embodiments, the cutoff score is 0.37. In selected embodiments, the cutoff score is 0.38. In selected embodiments, the cutoff score is 0.39. In selected embodiments, the cutoff score is 0.40. In selected embodiments, the cutoff score is 0.42. In selected embodiments, the cutoff score is 0.43. In selected embodiments, the cutoff score is 0.44. In selected embodiments, the cutoff score is 0.45. In selected embodiments, the cutoff score is 0.46. In selected embodiments, the cutoff score is 0.47. In selected embodiments, the cutoff score is 0.48. In selected embodiments, the cutoff score is 0.49. In selected embodiments, the cutoff score is 0.50. In selected embodiments, the cutoff score is 0.51. In selected embodiments, the cutoff score is 0.52. In selected embodiments, the cutoff score is 0.54. In selected embodiments, the cutoff score is 0.55. In selected embodiments, the cutoff score is 0.56. In selected embodiments, the cutoff score is 0.57. In selected embodiments, the cutoff score is 0.58. In selected embodiments, the cutoff score is 0.59. In selected embodiments, the cutoff score is 0.60. In selected embodiments, the cutoff score is 0.61. In selected embodiments, the cutoff score is 0.62. In selected embodiments, the cutoff score is 0.63. In selected embodiments, the cutoff score is 0.64. In selected embodiments, the cutoff score is 0.65. In selected embodiments, the cutoff score is 0.66. In selected embodiments, the cutoff score is 0.67. In selected embodiments, the cutoff score is 0.68. In selected embodiments, the cutoff score is 0.69. In selected embodiments, the cutoff score is 0.70. In selected embodiments, the cutoff score is 0.71. In selected embodiments, the cutoff score is 0.72. In selected embodiments, the cutoff score is 0.73. In selected embodiments, the cutoff score is 0.74. In selected embodiments, the cutoff score is 0.75. In selected embodiments, the cutoff score is 0.76. In selected embodiments, the cutoff score is 0.77. In selected embodiments, the cutoff score is 0.78. In selected embodiments, the cutoff score is 0.79. In selected embodiments, the cutoff score is 0.80. In selected embodiments, the cutoff score is 0.81. In selected embodiments, the cutoff score is 0.82. In selected embodiments, the cutoff score is 0.83. In selected embodiments, the cutoff score is 0.84. In selected embodiments, the cutoff score is 0.85.In selected embodiments, the cutoff score is 0.86. In selected embodiments, the cutoff score is 0.87. In selected embodiments, the cutoff score is 0.88. In selected embodiments, the cutoff score is 0.89. In selected embodiments, the cutoff score is 0.90.

[0067] An exemplary logistic regression analysis to generate probability scores is provided in Example 1.

[0068] 3. Subject Values

[0069] In some embodiments, the method comprises obtaining a value for subject sex; a value for subject age; an initial cardiac troponin concentration for the subject from an initial sample from the subject; and a galectin-3 (Gal-3) concentration for the subject from the initial sample from the subject; and optionally, a first subsequent cardiac troponin concentration, a second subsequent cardiac troponin concentration, or a first subsequent cardiac troponin concentration and a second subsequent cardiac troponin concentration from a corresponding first subsequent sample and / or second subsequent sample from the subject.

[0070] The methods are not limited by the method by which the subject value is obtained. In some embodiments, the methods include receiving the subject value from a testing laboratory, from the subject, from an analytical test system, and / or from a handheld or point-of-care test device.

[0071] In selected embodiments, the method includes receiving the subject value from an analytical test system. In some embodiments, the processing system further includes the analytical test system. In some embodiments, the method includes receiving the subject value from a handheld or point-of-care testing device. A "point-of-care device" refers to a device for providing medical diagnostic tests at or near a point of care (i.e., outside a laboratory), at a time and place of patient care (such as in a hospital, physician's office, emergency or other medical care facility, patient's home, rehabilitation facility, nursing home or facility, ambulance, long-term care and / or hospice facility, or subject's home or workplace). Such point-of-care devices may also include portable, desktop-sized devices. Examples of point-of-care devices include devices produced by Abbott Laboratories (Abbott Park, IL) (e.g., Alinity, ID ), ubiquitous biosensors (Rowville, Australia) (see US2006 / 0134713), Axis-Shield PoC AS (Oslo, Norway), and clinical laboratory products (Los Angeles, USA). Therefore, in some embodiments, the processing system also includes a handheld or fixed-point care test device.

[0072] In some embodiments, the method comprises obtaining the subject value electronically. In some embodiments, the method comprises manually entering the subject value into the processing system. In some embodiments, the method comprises automatically entering the subject value into the processing system.

[0073] 4. Biological samples

[0074] The biological sample of the subject is tested to determine the concentration of cardiac troponin and galectin-3. Biological samples include but are not necessarily limited to body fluids, such as blood-related samples (e.g., whole blood, serum, plasma and other blood-derived samples), urine, cerebrospinal fluid, bronchoalveolar lavage fluid, etc. Another example of a biological sample is a tissue sample. The biological sample can be fresh or stored (e.g., blood or blood fractions stored in a blood bank). The biological sample can be a body fluid specifically obtained for the determination of the present invention or a body fluid obtained for another purpose, and a secondary sample can be taken for the determination of the present invention. In certain embodiments, the biological sample is whole blood. Whole blood can be obtained from the subject using standard clinical procedures. In other embodiments, the biological sample is plasma. Plasma can be obtained from a whole blood sample by known means, including but not limited to centrifugation (e.g., centrifugation of anticoagulated blood), membrane or filter-based separation, coagulation-based plasma separation, acoustic force and microfluidics. This method provides a buffy coat of leukocyte components and a supernatant of plasma. In certain embodiments, the biological sample is serum. Serum can be obtained by centrifuging the whole blood sample collected in the tube that does not contain anticoagulant. Allow blood to coagulate before centrifugation. The pale yellow-reddish fluid obtained by centrifugation is serum. In another embodiment, the sample is urine. Sample can be pre-treated as needed by diluting, heparinizing, concentrating (if necessary) or by any method classification in a suitable buffer solution, these any methods include but are not limited to ultracentrifugation, by fast high performance liquid chromatography (FPLC) classification or by dextran sulfate or other method precipitation containing protein apolipoprotein B. Any of the many standard aqueous buffer solutions under physiological pH can be used, such as phosphate, Tris etc.

[0075] In some embodiments, the initial sample is a blood, serum, or plasma sample. In some embodiments, the first subsequent sample and / or the second subsequent sample comprises a blood, serum, or plasma sample.

[0076] The sample can be obtained using techniques known to those skilled in the art, and the sample can be used directly as obtained from the source or after pretreatment to change the characteristics of the sample. Such pretreatment may include, for example, preparing plasma from blood, diluting viscous fluids, filtering, precipitation, diluting, distilling, mixing, concentrating, inactivating interfering components, adding reagents, lysing, etc.

[0077] The sample can be obtained in a medical facility, such as an emergency room, urgent care clinic, walk-in clinic, long-term care facility, mobile clinic, rehabilitation facility, nursing facility, ambulance, or other appropriate medical practice. The sample can be obtained at home or in a residential setting (e.g., a senior living (e.g., facility) or hospice setting) or in the workplace, at the site of suspected myocardial infarction, or during transport to a medical facility (e.g., ambulance).

[0078] 5. Detection and Assay

[0079] The present invention is not limited by the type of assay used to detect and / or quantify cardiac troponin or Galectin-3 (Gal-3).

[0080] In certain embodiments, immunoassays are used to detect cardiac troponin and / or Gal-3. Any suitable assay known in the art can be used, including commercially available cardiac troponin or Gal-3 assays. Examples of such assays include, but are not limited to, immunoassays, such as sandwich immunoassays (e.g., monoclonal-polyclonal sandwich immunoassays, including radioisotope detection (radioimmunoassay (RIA)) and enzyme detection (enzyme immunoassay (EIA) or enzyme-linked immunosorbent assay (ELISA) (e.g., Quantikine ELISA assay, R&D Systems, Minneapolis, Minn.)), competitive inhibition immunoassays (e.g., forward and reverse), fluorescence polarization immunoassay (FPIA), enzyme expanded immunoassay technology (EMIT), bioluminescence resonance energy transfer (BRET) and homogeneous chemiluminescence assays, one-step antibody detection assays, homogeneous assays, heterogeneous assays, flight capture assays, single molecule detection assays, lateral flow assays, and the like.

[0081] Cardiac troponin and / or Gal-3 can be detected or quantified in a sample using one or more separation methods. For example, suitable separation methods may include mass spectrometry, such as electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS) n (n is an integer greater than zero), matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption / ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS) n or atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS, and APPI-(MS) nOther suitable separation methods include chemical extraction partitioning, column chromatography, ion exchange chromatography, hydrophobic (reversed phase) liquid chromatography, isoelectric focusing, one-dimensional polyacrylamide gel electrophoresis (PAGE), two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), or other chromatographic techniques such as thin layer chromatography, gas chromatography, or liquid chromatography, or any combination thereof. In one embodiment, the biological sample to be assayed can be fractionated prior to applying the separation method.

[0082] The nature of the method can be any assay known in the art, such as, for example, an immunoassay, a point-of-care assay, a clinical chemistry assay, protein immunoprecipitation, immunoelectrophoresis, chemical analysis, SDS-PAGE and immunoblot analysis, or protein immunostaining, electrophoretic analysis, protein assay, competitive binding assay, lateral flow assay, functional protein assay, or chromatography or spectrometry, such as high performance liquid chromatography (HPLC) or liquid chromatography-mass spectrometry (LC / MS). Moreover, the assay can be performed in a clinical chemistry format known to those of ordinary skill in the art.

[0083] Determination of cardiac troponin or galectin-3 concentration by immunoassay can be adapted for use with a variety of automated and semi-automated systems or platforms known in the art (including systems or platforms in which the solid phase comprises microparticles). The following adaptations of automated and / or semi-automated systems are included herein by way of example only. Specifically, the methods can utilize automated and semi-automated systems or platforms, such as, for example, those described in U.S. Patent No. 5,063,081, U.S. Patent Application Publication Nos. 2003 / 0170881, 2004 / 0018577, 2005 / 0054078, and 2006 / 0160164, and as, for example, available from Abbott Laboratories (Abbott Park, IL) as Abbott Point of Care ( or i-STAT Alinity, ID Abbott Laboratories), and those commercially sold by U.S. Patents 5,089,424 and 5,006,309 and as described, for example, by Abbott Laboratories (Abbott Park, IL). Or those sold commercially by the range of Abbott Alinity devices.

[0084] Other detection methods include the use of nanopore devices or nanowell devices or can be adapted for use on them, for example, for single molecule detection. As used herein, the term "single molecule detection" refers to the detection and / or measurement of single molecules of analytes in test samples at very low concentration levels (such as pg / mL or femtogram / mL levels). Many different single molecule analyzers or devices are known in the art and include nanopore and nanowell devices. The example of a nanopore device is described in PCT International Application WO 2016 / 161402, which is hereby incorporated by reference in its entirety. The example of a nanowell device is described in PCT International Application WO 2016 / 161400, which is hereby incorporated by reference in its entirety.

[0085] In certain embodiments, methods for detecting cardiac troponin T and I (cTnT and cTnI) are as described in U.S. Patent Application Publication No. 2012 / 0076803 and U.S. Patent Nos. 8,535,895 and 8,8325,120, all of which are incorporated herein by reference in their entirety, but with particular focus on the assay methods. In certain embodiments, cTnI is detected using the ERENNA assay system from Singulex Inc. or the hs cTnI STAT ARCHITECT assay from Abbott. In certain embodiments, methods for detecting troponin T utilize Troponin T high sensitivity (TnT-hs) assay (ROCHE) (see, Li et al., Arch Cardiovasc Dis. 2016 March; 109(3): 163-70, which is hereby incorporated by reference in its entirety, particularly with respect to the description of high sensitivity troponin T detection).

[0086] Determining the level of Galectin-3 in a subject typically involves measuring the level of the polypeptide using methods known in the art and / or described herein, for example, an immunoassay, such as an enzyme-linked immunosorbent assay (ELISA). An exemplary commercially available ELISA kit is the Galectin-3 ELISA kit available from EMD Chemicals. Alternatively, the level of Galectin-3 mRNA can be measured, for example, by quantitative PCR or Northern blot analysis, again using methods known in the art and / or described herein.

[0087] Example

[0088] The following examples are for illustrative purposes only and are not intended to limit the scope of the claims.

[0089] Example 1

[0090] Galectin-3 is a biomarker involved in multiple biological processes important in heart failure, including myofibroblast proliferation, fibrogenesis, tissue repair, cardiac remodeling, and inflammation. We investigated whether adding Galectin-3 to the Cardiac Algorithm Index score (baseline or serial) could improve differentiation between patients with type I MI and those with type II MI.

[0091] Using the primary endpoint and endpoint adjudication described below, patient samples were evaluated as patients with Type I MI and patients with Type II MI. The distinction between Type I MI and Type II MI derived from the Cardiac Algorithm Index score (baseline or serial) plus Gal-3 was compared to these baseline categories.

[0092] The primary outcome was a composite of death, nonfatal MI, and cardiac-related ED and readmissions (all elements were (After adjudication)

[0093] Participants were followed 1 to 3 years after randomization to determine the occurrence of this end point.

[0094] a) Death includes all-cause death

[0095] b) Nonfatal MI was defined using the "universal definition" of MI: a rise and / or fall in troponin with at least one value above the 99th percentile of the upper reference limit and at least one of the following: a) symptoms of ischemia, b) ECG changes indicating new ischemia, c) the presence of pathological Q waves on the ECG, and d) imaging evidence of new loss of viable myocardium or new regional wall motion abnormalities. This endpoint did not include infarcts present at the time of randomization because they were not related to the study intervention.

[0096] End point determination

[0097] All components of the primary composite outcome were adjudicated using consensus among three cardiovascular and emergency care experts. Triggers for adjudication included death reports, uncertain vital status due to incomplete follow-up information, elevated troponin values (excluding serial increases and decreases in values present at enrollment), hospital readmission, ED visit, repeat cardiac testing after discharge, invasive angiography, and / or coronary revascularization. Adjudicated endpoints included the primary outcome, secondary outcomes of repeat cardiac testing and cardiac-related ED visits, and the safety endpoint of ACS after discharge.

[0098] To conduct the assessment, the reviewers had access to the participant's index hospital admission and discharge records, relevant test results, follow-up telephone information, records obtained from follow-up visits, and study definitions in summary form or as actual data (if needed).

[0099] Algorithm performance

[0100] Patients with type II MI had statistically significantly higher baseline Gal-3 concentrations compared with patients with type I MI. Adding baseline Gal-3 to the algorithm index score significantly improved the AUC for type I / II discrimination compared with the MI3 baseline score alone (Tables 1 and Figure 1 The resulting area under the curve (AUC) for distinguishing type I from type II MI was 0.776 (95% CI 0.693, 0.858). This AUC showed a statistically significant improvement compared to the baseline algorithm alone (p-value 0.0416 by Delong's method for comparing AUCs).

[0101] Adding baseline Gal-3 to the serial algorithm index score improved the AUC for type I / II differentiation compared with the MI3 serial score alone ( Table 2 and Figure 2 ). The resulting AUC for distinguishing type I MI from type II MI was 0.791 (95% confidence interval (CI) 0.694, 0.888). Although there was no statistically significant improvement in AUC, as with the baseline algorithm indices described above, the addition of Gal-3 to the sequential scoring resulted in an Akaike information criterion (AIC) (98.0) that was lower than the AIC 109.2 for the MI3 sequential scoring model alone, indicating an improvement in goodness of fit (Tables 2 and Figure 2 ).

[0102] Table 1

[0103]

[0104] Table 2

[0105]

[0106] Tables 3 and 4 show the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of two models based on the minimum distance method at the optimal cutoff value, respectively, one model using baseline Gal-3 plus baseline index score and the other model using baseline Gal-3 plus serial index score.

[0107] Table 3

[0108] Cutoff value Sensitivity Specificity PPV NPV 0.58 0.722 0.745 0.800 0.655

[0109] Table 4

[0110] Cutoff value Sensitivity Specificity PPV NPV 0.54 0.633 0.703 0.738 0.591

[0111] Figure 3 and 4A plot of the predicted probabilities for the two models is shown, giving a visual representation of the model performance, with the horizontal line representing the optimal cutoff value. As shown, the majority of patients with type I MI have values above the cutoff score (dashed line), while the majority of patients with type II MI have values below the cutoff score.

[0112] Although only a few exemplary embodiments have been described in detail, it will be readily apparent to those skilled in the art that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of the present disclosure. Therefore, all such modifications and substitutions are intended to be included within the scope of the present invention, as defined in the following claims. It will also be appreciated by those skilled in the art that such modifications and equivalent constructions or methods do not depart from the spirit and scope of the present disclosure, and that they may make various changes, substitutions, and alterations without departing from the spirit and scope of the present disclosure.

Claims

1. A method for determining whether a subject suspected of having a myocardial infarction is experiencing a Type I myocardial infarction or a Type II myocardial infarction, the method comprising the steps of: a) obtaining a subject value of the subject, wherein the subject value comprises: i) Subject gender value; ii) age of the subject; iii) the subject's initial cardiac troponin concentration from an initial sample from the subject; and iv) the subject's galectin-3 (Gal-3) concentration in an initial sample from the subject; b) processing the subject's sex, age, and cardiac troponin value using a processing system so as to determine an algorithm index score for the subject, wherein the processing system comprises: i) a computer processor, and ii) non-transitory computer memory comprising one or more computer programs and a database, wherein the one or more computer programs include an additive tree algorithm, wherein the database comprises at least M decision trees, wherein each individual decision tree comprises at least two predetermined splitting variables and at least three predetermined terminal node values, wherein the at least two predetermined splitting variables are an initial cardiac troponin concentration threshold, a gender value, and / or an age value, wherein the one or more computer programs in combination with the computer processor are configured to: i) applying the subject's initial cardiac troponin concentration, the subject's gender value, and / or the subject's age value to the database to determine a terminal node value for each of the at least M decision trees, and ii) applying the additive tree algorithm to: (a) determine a combined value from the M terminal node values, and (b) processing the combined value to determine the algorithm index score that the subject is experiencing a myocardial infarction, Wherein M is an integer of at least 2, c) reporting the algorithm index score for the subject determined by the processing system; d) generating a probability score based on: i) the subject's Gal-3 concentration and ii) the algorithm index score; and e) determining whether the subject has suffered from Type I myocardial infarction or Type II myocardial infarction.

2. A method for determining whether a subject suspected of having a myocardial infarction is experiencing a Type I myocardial infarction or a Type II myocardial infarction, the method comprising the steps of: a) obtaining a subject value of the subject, wherein the subject value comprises: i) Subject gender value; ii) age of the subject; iii) the subject's initial cardiac troponin concentration from an initial sample from the subject; iv) the subject's galectin-3 (Gal-3) concentration in an initial sample from the subject; and v) a first subsequent cardiac troponin concentration, a second subsequent cardiac troponin concentration, or a first subsequent cardiac troponin concentration and a second subsequent cardiac troponin concentration of a corresponding first subsequent sample and / or second subsequent sample from the subject; b) processing the subject's sex, age, and cardiac troponin value using a processing system so as to determine an algorithm index score for the subject, wherein the processing system comprises: i) a computer processor, and ii) non-transitory computer memory comprising one or more computer programs and a database, wherein the one or more computer programs comprise: a rate of change algorithm and an additive tree algorithm, wherein the database comprises at least M decision trees, wherein each individual decision tree comprises at least two predetermined splitting variables and at least three predetermined terminal node values, wherein the at least two predetermined splitting variables are: a cardiac troponin rate of change threshold, an initial cardiac troponin concentration threshold, or a combination thereof; and a gender value and / or an age value, wherein the one or more computer programs in combination with the computer processor are configured to: i) applying the rate of change algorithm to determine a rate of change value for a subject's cardiac troponin from at least two of: the subject's initial cardiac troponin concentration, the first subsequent cardiac troponin concentration, and the second subsequent cardiac troponin concentration, ii) applying the subject's cardiac troponin change rate value, the subject's initial cardiac troponin concentration, the subject's gender value, and / or the subject's age value to the database to determine a terminal node value for each of the at least M decision trees, and iii) applying the additive tree algorithm to: (a) determine a combined value from the M terminal node values, and (b) processing the combined value to determine the algorithm index score that the subject is experiencing a myocardial infarction, wherein M is an integer of at least 2, and c) reporting the algorithm index score for the subject determined by the processing system; d) generating a probability score based on: i) the subject's Gal-3 concentration and ii) the algorithm index score; and e) determining whether the subject has type I myocardial infarction or type II myocardial infarction based on the probability score.

3. The method of claim 1 or claim 2, wherein the subject is determined to have type I myocardial infarction based on the probability score.

4. The method of claim 1 or claim 2, wherein the subject is determined to have type II myocardial infarction based on the probability score.

5. The method of any one of claims 1-4, wherein said obtaining a subject value comprises receiving the subject value from a testing laboratory, from the subject, from an analytical test system, and / or from a handheld or point-of-care testing device.

6. The method of claim 5, wherein the processing system further comprises the analytical testing system and / or the handheld or point-of-care testing device.

7. The method of any one of claims 1-4, wherein said obtaining a subject value comprises receiving the subject value electronically.

8. The method according to any one of claims 1 to 7, wherein the initial cardiac troponin concentration, the first cardiac troponin concentration and / or the second cardiac troponin concentration are obtained by performing a cardiac troponin detection assay.

9. The method of claim 8, wherein the cardiac troponin detection assay comprises an immunoassay.

10. The method of claim 8 or claim 9, wherein the cardiac troponin detection assay is a single molecule detection assay.

11. The method of any one of claims 1-10, wherein the cardiac troponin is cardiac troponin I (cTnI).

12. The method of any one of claims 1-11, wherein the cardiac troponin is cardiac troponin T (cTnT).

13. The method of any one of claims 1-12, wherein the Gal-3 concentration is obtained by performing a Gal-3 detection assay.

14. The method of claim 13, wherein the Gal-3 detection assay comprises an immunoassay.

15. The method of claim 13 or claim 14, wherein the Gal-3 detection assay is a single molecule detection assay.

16. The method of any one of claims 1-15, further comprising manually or automatically inputting the subject values into the processing system.

17. The method of any one of claims 1-16, wherein the initial sample is taken from the subject at an emergency room, urgent care clinic, mobile clinic, rehabilitation facility, nursing facility, ambulance, the subject's workplace, the subject's home, or any combination thereof.

18. The method of any one of claims 1-17, wherein the subject is a human.

19. The method of any one of claims 1-18, wherein the initial sample from the subject comprises a blood, serum, or plasma sample.

20. The method of any one of claims 1-18, wherein the first subsequent sample and / or the second subsequent sample comprises a blood, serum or plasma sample.

21. The method according to any one of claims 1-20, wherein the M decision trees are at least 100 different decision trees.

22. The method of any one of claims 1-21, wherein the M decision trees are at least 800 different decision trees.

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