Use of cardiac troponin and galectin-3 to distinguish between type I and type II myocardial infarction

Abstract

The present invention provides methods for determining whether a subject suspected of having a myocardial infarction is suffering from type I or type II myocardial infarction. In particular, systems and methods are provided that employ a probability score based on a decision tree-based algorithm to process the subject's gender, age, and cardiac troponin concentration, as well as the subject's galectin-3 (Gal-3) concentration.

Description

[Technical Field]

[0001] This application claims priority to U.S. Patent Application No. 63 / 401,335, filed August 26, 2022, and U.S. Patent Application No. 63 / 464,412, filed May 5, 2023, the contents of each of which are incorporated herein by reference.

[0002] A method for determining whether a subject suspected of having a myocardial infarction has suffered from Type I or Type II myocardial infarction. In particular, systems and methods are provided that employ a probability score based on a decision tree-based algorithm that processes the subject's gender, age, and cardiac troponin concentration along with the subject's galectin-3 (Gal-3) concentration. [Background technology]

[0003] More than 6 million patients visit hospitals each year in the United States for evaluation of suspected acute coronary syndrome (ACS). The most serious diagnosis associated with ACS is myocardial infarction (MI), which usually presents with chest pain and associated symptoms. There are several types of MI. Type I is the classic type associated with plaque rupture or erosion. Type I MI usually leads to platelet activation, embolus formation, and ultimately occlusion of the coronary artery, stopping blood flow to the muscle (myocardium) supplied by that artery. Typically, patients diagnosed with Type I MI are immediately transported to a catheterization laboratory and undergo coronary angiography with or without percutaneous coronary intervention (PCI, balloon and stent insertion) or, less frequently, coronary artery bypass graft (CABG) surgery if indicated.

[0004] Type II MI is most often due to an imbalance between myocardial oxygen supply and demand, with or without arteriosclerosis and endothelial dysfunction, resulting in an increased demand for oxygen by the myocardium that is outpaced by supply. The increased demand can be caused by problems such as sepsis, severe anemia, and / or abnormal heart rhythm. Treatment for Type II MI generally involves addressing the underlying etiology. Because the problem is not primarily caused by a blocked artery, PCI or CABG alone are unlikely to be effective.

[0005] Distinguishing between types of MI is important for providing the best possible care to patients, as their etiologies and treatments differ. Currently, differentiation is usually based on clinical and electrocardiographic (EKG) criteria, but these are not always accurate. Summary of the Invention [Problem to be solved by the invention]

[0006] (Summary of the Invention) Provided herein are methods for determining whether a subject suspected of having a myocardial infarction is suffering from type I or type II myocardial infarction. [Means for solving the problem]

[0007] In some embodiments, the method includes the steps of: a) obtaining subject values ​​for a subject, the subject values ​​comprising: i) a subject gender value; ii) a subject age value; iii) a subject initial cardiac troponin concentration from an initial sample from the subject; and iv) a subject galectin-3 (Gal-3) concentration from the initial sample from the subject; b) processing the subject gender, age, and cardiac troponin values ​​with a processing system, thereby determining an algorithmic index score for the subject, the processing system comprising: i) a computer processor; and ii) a non-transitory computer memory comprising one or more computer programs and a database, the one or more computer programs comprising an additive tree algorithm, the database comprising at least M decision trees, each individual decision tree comprising at least two predetermined splitting variables and at least three predetermined terminal node values, the at least two predetermined splitting variables being associated with the initial cardiac troponin concentration. a troponin concentration threshold, a gender value, and / or an age value, and the one or more computer programs, in conjunction 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 terminal node values ​​for each of the at least M decision trees; ii) apply the additive tree algorithm to (a) determine a combination value from the M terminal node values; and (b) process the combination value to determine an algorithm index score that the subject has suffered from myocardial infarction, where 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 type I or type II myocardial infarction based on the probability score.

[0008] In some embodiments, the subject value further comprises first, second, or first and second subsequent cardiac troponin concentrations from corresponding first and / or second subsequent samples from the subject. In some embodiments, the at least two predetermined splitting variables are a cardiac troponin change rate threshold, an initial cardiac troponin concentration threshold, or a combination thereof, and a gender value and / or an age value. In some embodiments, the one or more computer programs, in conjunction with the computer processor, are further configured to apply the change rate algorithm to determine a cardiac troponin change rate value for the subject 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.

[0009] In some embodiments, M is an integer between 2 and 1000. In other embodiments, M is an integer between 2 and 100,000. The integer selected for M is determined based on the optimal number of trees for boosting the algorithm, which can be determined using routine techniques known in the art.

[0010] In some embodiments, the method includes: a) obtaining subject values ​​for a subject, the subject values ​​comprising: i) a subject gender value; ii) a subject age value; iii) a subject initial cardiac troponin concentration from an initial sample from the subject; iv) a subject galectin-3 (Gal-3) concentration from the initial sample from the subject; and v) first, second, or first and second subsequent cardiac troponin concentrations from corresponding first and / or second subsequent samples from the subject; b) processing the subject gender, age, and cardiac troponin values ​​with a processing system, thereby determining an algorithmic index score for the subject, the processing system comprising: i) a computer processor; and ii) a non-transitory computer memory comprising one or more computer programs and a database, the one or more computer programs comprising a rate of change algorithm and an additive tree algorithm, the database comprising at least M decision trees, each individual decision tree comprising at least two predetermined splitting variables and at least three predetermined terminal node values. the at least two predetermined splitting variables are a cardiac troponin change rate threshold, an initial cardiac troponin concentration threshold, or a combination thereof, and a gender value and / or an age value, and the one or more computer programs, in conjunction with the computer processor, are configured to: i) determine a cardiac troponin change rate value for the subject 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's cardiac troponin change rate value, the subject's initial cardiac troponin concentration, the subject's gender value, and / or the age value to the database to determine terminal node values ​​for each of the at least M decision trees; iii) apply the additive tree algorithm to (a) determine a combination value from the M terminal node values; and (b) process the combination value to determine an algorithm index score for the subject having a 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) 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 or type II myocardial infarction based on the probability score.

[0011] In some embodiments, the subject is determined to have a type I myocardial infarction based on the probability score. In some embodiments, the subject is determined to have a type II myocardial infarction based on the probability score.

[0012] In some embodiments, obtaining a subject value comprises receiving the subject value from a laboratory, from the subject, from an analytical testing system, and / or from a handheld or point-of-care testing device, hi some embodiments, the processing system further comprises the analytical testing system and / or the handheld or point-of-care testing device.

[0013] In some embodiments, obtaining the target value comprises receiving the target value electronically.

[0014] 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.

[0015] 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.

[0016] In some embodiments, the method further comprises manually or automatically entering the subject values ​​into the processing system. In some embodiments, the subject values ​​are entered into the processing system using a combination of manual and automatic entry. For example, age and / or gender are entered manually, and Gal-3 levels and / or cardiac troponin levels are entered automatically.

[0017] 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 troponin is cTnI and cTnT.

[0018] In some embodiments, the initial sample is collected from the subject in an emergency room, urgent care clinic, outpatient clinic, rehabilitation facility, nursing facility, ambulance, the subject's place of work, the subject's home, or any combination thereof.

[0019] In some embodiments, the subject is a human.

[0020] In some embodiments, the initial sample from the subject comprises a blood, serum, or plasma sample, hi some embodiments, the first and / or second subsequent sample comprises a blood, serum, or plasma sample.

[0021] 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.

[0022] Other embodiments and aspects of the present disclosure will become apparent in light of the following detailed description and associated drawings. [Brief explanation of the drawings]

[0023] [Figure 1]1 is a graph comparing the area under the curve (AUC) of algorithm index score alone versus algorithm index score and baseline Gal-3 for type I versus type II MI in a population of 123 patients for whom a baseline troponin sample was available. [Figure 2] 1 is a graph comparing the AUC of algorithm index score alone versus algorithm index score and baseline Gal-3 for type I versus type II MI in a population of 86 patients for whom serial troponin samples were available. [Figure 3] 1 shows distribution plots of predicted probabilities from logistic regression for Gal-3 and baseline MI3 scores, with horizontal lines representing optimal cutoffs. [Figure 4] 1 shows distribution plots of predicted probabilities from logistic regression for Gal-3 and sequential MI3 scores, with horizontal lines representing optimal cutoffs. DETAILED DESCRIPTION OF THE INVENTION

[0024] Previously, algorithms have been used to distinguish MI patients (type I alone or type I and type II combined) from non-MI patients, taking patient age, sex, and two sequential high-sensitivity troponin measurements. Disclosed herein is a method that applies an algorithm involving both initial and sequential troponin measurements, along with galectin-3 (Gal-3) concentrations, to distinguish type I from type II MI, thereby enabling better patient management in identifying patients at highest risk for complications and who should undergo invasive management.

[0025] definition The terms "comprise," "include," "having," "has," "can," "contain," and variations thereof, as used herein, are intended to be open-ended transitional phrases, terms, or words that do not exclude the possibility of additional acts or structures. The singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. The present disclosure also contemplates other embodiments that "comprising," "consisting of," and "consisting essentially of" the embodiments or elements presented herein, whether explicitly stated or not.

[0026] For reference herein to ranges of values, each intermediate value therebetween is expressly contemplated to the same degree of precision. For example, for the range of 6 to 9, the values ​​7 and 8 are contemplated in addition to 6 and 9, and for the range of 6.0 to 7.0, the values ​​6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are illustratively contemplated.

[0027] Unless otherwise defined herein, scientific and technical terms used in connection with this disclosure shall have the meanings that are commonly understood by those of ordinary skill in the art. The meaning and scope of terms should be clear, but in the event of any latent ambiguity, the definitions provided herein shall take precedence over any dictionary or foreign definitions. Furthermore, unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular.

[0028] As used herein, the term "acute coronary syndrome," or "ACS," refers to a group of conditions in which reduced blood flow in the coronary arteries prevents parts of the heart muscle from functioning properly or leads to death. The most common symptom is chest pain, often radiating to the left arm or angle of the jaw, characterized by a feeling of pressure, accompanied by nausea and sweating. ACS usually results from 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, incorporated herein by reference in its entirety). These types are named non-ST segment elevation myocardial infarction and ST segment elevation myocardial infarction according to electrocardiogram (EKG) findings. There can be some variation in which forms of myocardial infarction (MI) are classified as acute coronary syndromes. ACS should be distinguished from stable angina, which occurs with exertion and resolves at rest. In contrast to stable angina, unstable angina often occurs suddenly at rest or with minimal exertion, or with exertion less severe than the individual's previous angina ("crescendo angina"). New-onset angina is also considered unstable angina because it indicates a new problem in the coronary arteries. ACS is usually associated with a coronary artery embolus but may also be associated with cocaine use. Cardiac chest pain may be precipitated by anemia, bradycardia (excessively slow heart rate), or tachycardia (excessively fast heart rate). 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 accompanied by diaphoresis (sweating), nausea and vomiting, and shortness of breath. In many cases, sensations are "atypical," and pain may be experienced in a variety of ways or may be absent altogether (this is more common in women and diabetics). Some report palpitations, anxiety, or a sense of impending doom (imminence), and a sense of actual illness. Patients with chest pain very frequently present to hospital emergency rooms. However, chest pain can also be due to many causes: stomach discomfort (e.g., indigestion), pulmonary distress, pulmonary embolism, dyspnea, musculoskeletal pain (strained muscles, bruises), dyspepsia, pneumothorax, conditions other than coronary heart failure, and acute coronary syndrome (ACS).As mentioned above, ACS is usually one of three diseases 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) depending on the electrocardiogram (EKG) findings. ACS usually involves a coronary embolism. Physicians must determine whether a patient has a life-threatening ACS. In such cardiac events, prompt treatment by opening the blocked coronary artery is important to prevent further loss of myocardial tissue.

[0029] As used herein, "suspected of having acute coronary syndrome" means that the subject has at least one of the above symptoms of acute coronary syndrome (e.g., chest pain experienced as tightness around the chest and often radiating to the left arm and left angle of the jaw, diaphoresis (sweating), nausea and vomiting, shortness of breath).

[0030] A "subject" or "patient" may be human or non-human, and may include, for example, animal strains or species used as "model systems" for research purposes, such as the mouse model described herein. Similarly, a subject may include an adult or a juvenile (e.g., a child). Furthermore, a patient may refer to any organism, preferably a mammal (e.g., human or non-human), that will benefit from the administration of the compositions contemplated herein. Examples of mammals include, but are not limited to, any member of the mammalian class, i.e., humans; non-human primates, such as chimpanzees and other ape and monkey species; farm animals, such as cows, horses, sheep, goats, and pigs; livestock, such as rabbits, dogs, and cats; and laboratory animals, including rodents, such as rats, mice, and guinea pigs, among others. Examples of non-mammals include, but are not limited to, birds, fish, and others. In one embodiment, the mammal is a human.

[0031] Preferred methods and materials are described below, although methods and materials similar or equivalent to those described herein can be used in the practice or testing of this 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 not intended to be limiting.

[0032] Distinguishing between Type I and Type II Myocardial Infarction The present invention provides systems and methods for determining whether a subject suspected of having a myocardial infarction is suffering from a type I or type II myocardial infarction.

[0033] The disclosed method employs two predictors to classify the type of myocardial infarction (MI): 1) Algorithm Index Score and 2) Galectin-3 concentration.

[0034] 1. Algorithm Index Score The first predictor is an algorithmic index score. The methods of the present disclosure can use any machine learning algorithm known in the art to generate the algorithmic index score. In some embodiments, the machine learning algorithm is an adaptive index modeling (AIM) algorithm. In other embodiments, the machine learning algorithm is a random forest algorithm. In still other embodiments, 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.

[0035] Algorithm index scores may be generated using the methods 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 entireties.

[0036] In some embodiments, the generation of the algorithmic index score utilizes 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, the subject's age, and the subject's sex to calculate the probability that the patient has suffered a myocardial infarction (MI). These variable inputs are evaluated via decision tree-based statistical calculations to provide an estimate of the likelihood that the patient has suffered a Type I MI or a Type II MI, which can stratify the subject into the appropriate category.

[0037] In certain embodiments, the systems and methods herein address timing variations between sample collections by determining the rate of change of cardiac troponin based on the exact or near-exact time (e.g., in minutes) between the first and second collections of a sample from a subject.

[0038] The systems and methods herein, in certain embodiments, address the age variable by determining the influence of the age decile in which the patient falls. In some embodiments, the subject's age value is a set value based on the subject's age in years or an age range. In selected embodiments, the set value is determined based on the following ranges: 0-29 years, 30-39 years, 40-49 years, 50-59 years, 60-69 years, 70-79 years, and 80 years and older.

[0039] The systems and methods herein, in some embodiments, address gender by categorizing patients into male and female gender profiles. In selected embodiments, the gender value is one number (e.g., 1.0) for males and another number (e.g., 0) for females.

[0040] In some embodiments, the systems and methods include a non-transitory computer memory component including one or more computer programs configured to access a computer processor and a database, the one or more computer programs including an additive tree algorithm and optionally a change rate algorithm, the database including at least M decision trees, each respective decision tree including 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, the at least two predetermined splitting variables being an initial cardiac troponin concentration threshold, a gender value, and / or an age value, or a cardiac troponin change rate threshold, an initial cardiac troponin concentration threshold, or a combination thereof and a gender value and / or an age value, and in conjunction with a computer processor: i) applying 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 M cardiac troponin concentration. and (ii) 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 an algorithmic index score for the subject having a myocardial infarction, or (i) applying the change rate algorithm to determine a cardiac troponin change rate value for the subject 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, and (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 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) process the combined value to determine an algorithmic index score for the subject having a myocardial infarction. In certain embodiments, the non-transitory computer memory component further comprises a database.

[0041] The additive trees 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 between 2 and 100,000. The integer selected for M is determined based on the optimal number of trees for boosting the algorithm, which can be determined using routine techniques known in the art. For example, M can be 10-100,000, 100-100,000, 200-100,000, 300-100,000, 400-100,000, 500-100,000, 600-100,000, 700-100,000, 800-100,000, 900-100,000, 1000-100,000, 2000-100,000, 3000-100,000, 4000-100,000, 5000-100,000, 6000- 100,000, 7000-100,000, 8000-100,000, 9000-100,000, 10-90,000, 100-90,000, 200-90,000, 300-90,000, 400-90,000, 500-90,000, 600-90,000, 700-90,000, 800-90,000, 900-90,000, 1000-90,000, 2000-90,000, 3000-90,000, 400 0~90,000, 5000~90,000, 6000~90,000, 7000~90,000, 8000~90,000, 9000~90,000, 10~80,000, 100~80,000, 200~80,000, 300~80,000, 400~80,000, 500~80,000, 600~80,000, 700~80,000, 800~80,000, 900~80,000, 1000~80,000, 2000~ 80,000, 3000-80,000, 4000-80,000, 5000-80,000, 6000-80,000, 7000-80,000, 8000-80,000, 9000-80,000, 10-70,000, 100-70,000, 200-70,000, 300-70,000, 400-70,000, 500-70,000, 600-70,000, 700-70,000, 800-70,000, 900-70,000、1000~70,000、2000~70,000、3000~70,000、4000~70,000、5000~70,000、6000~70,000、7000~70,000、8000~70,000、9000~70,000、10~60,000、100~60,000、200~60,000、300~60,000、400~60,000、500~60,000、600~60,000、700~60,000、800~60,000、900~60,000、1000~60,000、2000~60,000、3000~60,000、4000~60,000、5000~60,000、6000~60,000、7000~60,000、8000~60,000、9000~60,000、10~50,000、100~50,000、200~50,000、300~50,000、400~50,000、500~50,000、600~50,000、700~50,000、800~50,000、900~50,000、1000~50,000、2000~50,000、3000~50,000、4000~50,000、5000~50,000、6000~50,000、7000~50,000、8000~50,000、9000~50,000、10~40,000、100~40,000、200~40,000、300~40,000、400~40,000、500~40,000、600~40,000、700~40,000、800~40,000、900~40,000、1000~40,000、2000~40,000、3000~40,000、4000~40,000、5000~40,000、6000~40,000、7000~40,000、8000~40,000、9000~40,000、10~30,000、100~30,000、200~30,000、300~30,000、400~30,000、500~30,000、600~30,000、700~30,000、800~30,000、900~30,000、1000~30,000、2000~30,000、3000~30,000、4000~30,000、5000~30,000、6000~30,000、7000~30,000、8000~30,000、9000~30,000、10~20,000、100~20,000、200~20,000、300~20,000、400~20,000、500~20,000、600~20,000、700~20,000、800~20,000、900~20,000、1000~20,000、2000~20,000、3000~20,000、4000~20,000、5000~20,000、6000~20,000、7000~20,000、8000~20,000、9000~20,000、10~10000、100~10,000、200~10,000、300~10,000、400~10,000、500~10,000、600~10,000、700~10,000、800~10,000、900~10,000、1000~10,000、2000~10,000、3000~10,000、4000~10,000、5000~10,000、6000~10,000、7000~100,00、8000~10,000、9000~10,000, 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, 70 It may be 0 to 800, 10 to 700, 100 to 700, 200 to 700, 300 to 700, 400 to 700, 500 to 700, 600 to 700, 10 to 600, 100 to 600, 200 to 600, 300 to 600, 400 to 600, 500 to 600, 10 to 500, 100 to 500, 200 to 500, 300 to 500, 400 to 500, 10 to 400, 100 to 400, 200 to 400, 300 to 400, 10 to 300, 100 to 300, 200 to 300, 10 to 200, 100 to 200, or 10 to 100. 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 includes a single decision tree.

[0042] In some embodiments, the algorithmic index score is based on a respective unweighted or weighted combination of the node values. In further embodiments, the combined values ​​from the M terminal nodes are calculated using the formula

[0043]

number

[0044]

number

[0045]

number

[0046] In some embodiments, the algorithm is based on the following general formula:

[0047]

number

[0048] For example, the algorithm may generate 987 individual tree scores that are aggregated into an SS using the formula below and into an algorithm index score using the formula provided above.

[0049]

number

[0050] In some embodiments, the predetermined splitting variables and / or the predetermined terminal node values ​​are empirically derived from an analysis of population data. In other embodiments, the analysis of the population data includes employing a machine learning algorithm as described above. For example, the analysis of the population data may include using an algorithm based on additive decision trees.

[0051] In some embodiments, the at least two predetermined splitting variables comprise 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 comprise a cardiac troponin change rate 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 change rate threshold, an initial cardiac troponin concentration threshold, a gender value, and an age value. Accordingly, in some embodiments, the computer program further applies the additive tree algorithm and applies the change rate algorithm to determine a cardiac troponin change rate value for the subject 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.

[0052] In some embodiments, the algorithm index score is a baseline algorithm index score. The baseline algorithm index score utilizes the subject's gender, age, and initial cardiac troponin concentration.

[0053] In some embodiments, the algorithm index score is a sequential algorithm index score. The sequential algorithm index score utilizes the subject's gender, age, initial cardiac troponin concentration, and a first subsequent cardiac troponin concentration, a second subsequent cardiac troponin concentration, or first and second subsequent cardiac troponin concentrations corresponding to the subsequently collected samples. In addition to the first subsequent sample or the first and second subsequent samples, the method may use any number of subsequent samples, such as a third subsequent sample, a fourth subsequent sample, a fifth subsequent sample, a sixth subsequent sample, a seventh subsequent sample, etc. Subsequent samples may be collected at any interval, from minutes to hours to days, after the previous sample.

[0054] In some embodiments, the algorithm index score is reported as a result between 0 and 100. For example, the algorithm index score may originally be generated on a scale from 0 to 1, but is multiplied by 100 for ease of presentation.

[0055] In some embodiments, the method further comprises reporting the algorithm index score for the subject. In some embodiments, the processing system generates a report based on the results and / or analysis of the algorithm index score.

[0056] 2. Probability Score The galectin-3 concentration, together with the algorithm index score, allows for the generation of 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 index 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 trees algorithm, a naive Bayes classification, a support vector machine, K-nearest neighbors (KNN), K-means clustering, a neural network, or any combination thereof.

[0057] In yet other embodiments, the at least one machine learning algorithm is a regression algorithm (eg, logistic regression).

[0058] In selected embodiments, the machine learning algorithm is a logistic regression model. The algorithm index score and baseline galectin-3 concentration can be introduced into a logistic regression model using available statistical software, such as R, SPSS, Systat, STATA, Eviews, AMOS, SAS, Python, and Mplus. Any suitable logistic regression model may be used, and the methods described herein are not limited in this regard. A predicted probability from the model is generated using statistical software, resulting in a predicted probability of type I MI.

[0059] The probability score provides insight into how likely a patient is to suffer from a type I MI (e.g., the probability of type I MI is modeled). The probability score can be compared to a cutoff score to determine whether a subject has type I or type II myocardial infarction. A shortest distance method can be used to determine the optimal cutoff for the probability score, which ranges from 0 to 1. For example, a type I MI may be above the cutoff score, and a probability score below the cutoff score represents a type II MI.

[0060] In some embodiments, a clinician or other healthcare professional can compare the probability score for a subject to a cutoff score, which can be provided in a product insert or other publication, or on a website or mobile device (e.g., via an app).

[0061] In some embodiments, the cutoff scores are 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, 0.23, 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, 2, 0.23, 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 8, 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, 0.74, 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, 0.99, 0.100, 0.101, 0.102, 0.103, 0.104, 0.105, 0.106, 0.107, 0.108, 0.110, 0.111, 0.112, 0.113, 0.114, 0.115, 0.116, 0.117, 0.118, 0.119, 0.120, 0.121, 0.122, 0.123, 0.124, 0.125, 0.126, 0.127, 0.128, 0.129 The cutoff score is 0.4, 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.

[0062] An exemplary logistic regression analysis for generating probability scores is provided in Example 1.

[0063] 3. Target value In some embodiments, the method includes obtaining a subject's gender value, a subject's age value, a subject's initial cardiac troponin concentration from an initial sample from the subject, and a subject's galectin-3 (Gal-3) concentration from the initial sample from the subject, and optionally first, second, or first and second subsequent cardiac troponin concentrations from corresponding first and / or second subsequent samples from the subject.

[0064] The method is not limited to the method of obtaining the subject value. In some embodiments, the method includes receiving the subject value from a laboratory, from the subject, from an analytical testing system, and / or from a handheld or point-of-care testing device.

[0065] In selected embodiments, the method includes receiving the target value from an analytical testing system. In some embodiments, a processing system further includes the analytical testing system. In some embodiments, the method includes receiving the target value from a handheld or point-of-care testing device. A "point-of-care device" refers to a device used to provide medical diagnostic testing at or near the point of care (i.e., outside of a laboratory) at the time and place of patient care (e.g., a hospital, clinic, emergency or other medical facility, a patient's home, a rehabilitation facility, a nursing or assisted living facility, an ambulance, a long-term care and / or hospice facility, or a 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., i-STAT®, i-STAT® Alinity, ID Now®), Universal Biosensors (Rowville, Australia) (see US 2006 / 0134713), Axis-Shield PoC AS (Oslo, Norway), and Clinical Lab Products (Los Angeles, USA). Thus, in some embodiments, the processing system further comprises a handheld or point-of-care testing device.

[0066] In some embodiments, the method includes obtaining target values ​​electronically, hi some embodiments, the method includes manually entering the target values ​​into the processing system, hi some embodiments, the method includes automatically entering the target values ​​into the processing system.

[0067] 4. Biological Samples A biological sample from a subject is tested to determine the concentrations of cardiac troponin and galectin-3. Biological samples include, but are not limited to, bodily fluids such as blood-related samples (e.g., whole blood, serum, plasma, and other blood-derived samples), urine, spinal fluid, and bronchoalveolar lavage fluid. Another example of a biological sample is a tissue sample. Biological samples can be fresh or preserved (e.g., blood or blood fractions stored in a blood bank). Biological samples can be bodily fluids obtained expressly for the assays of the present invention, or bodily fluids obtained for other purposes that can be set aside for the assays of the present invention. In certain embodiments, the biological sample is whole blood. Whole blood is obtained from a 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., of anticoagulated blood), membrane or filter separation, plasma separation by aggregation, acoustic force, and microfluidics. Such processes yield a buffy coat of white blood cell components and a plasma supernatant. In certain embodiments, the biological sample is serum. Serum is obtained by centrifugation of a whole blood sample collected in a tube without an anticoagulant. The blood is allowed to clot before centrifugation. The yellow-red fluid obtained by centrifugation is serum. In another embodiment, the sample is urine. The sample is optionally pretreated by dilution in an appropriate buffer, heparinized, concentrated if desired, or fractionated by any number of methods, including, but not limited to, ultracentrifugation, fractionation by fast performance liquid chromatography (FPLC), or precipitation of apolipoprotein B-containing proteins by dextran sulfate or other methods. Any of several standard aqueous buffers at physiological pH can be used, such as phosphate, Tris, and others.

[0068] In some embodiments, the initial sample is a blood, serum, or plasma sample. In some embodiments, the first and / or second subsequent sample comprises a blood, serum, or plasma sample.

[0069] Samples can be obtained using techniques known to those skilled in the art, and may be used directly as obtained from the source or after pretreatment to modify the characteristics of the sample, which may include, for example, preparation of plasma from blood, dilution of viscous fluids, filtration, precipitation, dilution, distillation, mixing, concentration, inactivation of interfering components, addition of reagents, lysis, etc.

[0070] The sample may be obtained at a medical facility, such as an emergency room, urgent care clinic, walk-in clinic, long-term care facility, outpatient clinic, rehabilitation facility, nursing facility, ambulance, or another appropriate location of medical care. The sample may be obtained at a home or residential setting (e.g., a nursing home (e.g., institutional) or hospice setting), or at the workplace, at the location of a suspected myocardial infarction, or during transport to a medical facility (e.g., ambulance).

[0071] 5. Detection Assay The present invention is not limited by the type of assay used to detect and / or quantitate cardiac troponin or galectin-3 (Gal-3).

[0072] In certain embodiments, immunoassays are employed 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 with radioisotope detection (radioimmunoassay (RIA)) and enzyme detection (enzyme immunoassay (EIA) or enzyme-linked immunosorbent assay (ELISA) (e.g., Quantikine ELISA assays, R&D Systems, Minneapolis, Minn.)), competitive inhibition immunoassays (e.g., forward and reverse), fluorescence polarization immunoassays (FPIA), enzyme-multiplexed immunoassay technique (EMIT), bioluminescence resonance energy transfer (BRET), and homogeneous chemiluminescence assays, one-step antibody detection assays, homogeneous assays, heterogeneous assays, capture-on-the-fly assays, single-molecule detection assays, lateral flow assays, and others.

[0073] Cardiac troponin and / or Gal-3 can be detected or quantified in a sample with the aid of one or more separation techniques. For example, suitable separation techniques include mass spectrometry, such as electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS) n (n is an integer greater than 0), 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 (reverse-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, gas, or liquid chromatography, or any combination thereof. In one embodiment, the biological sample to be assayed may be fractionated prior to applying the separation method.

[0074] The nature of the method and test can be any assay known in the art, such as immunoassays, point-of-care assays, clinical chemistry assays, protein immunoprecipitation, immunoelectrophoresis, chemical analysis, SDS-PAGE and Western blot analysis, or protein immunostaining, electrophoretic analysis, protein assays, competitive binding assays, lateral flow assays, functional protein assays, or chromatographic or spectroscopic methods such as high performance liquid chromatography (HPLC) or liquid chromatography-mass spectrometry (LC / MS). Assays can also be employed in clinical chemistry formats known to those skilled in the art, for example.

[0075] Determining cardiac troponin or galectin-3 concentrations by immunoassay can be adapted for use in a variety of automated and semi-automated systems or platforms known in the art, including those 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. In particular, the methods can utilize automated and semi-automated systems or platforms, such as those described in U.S. Pat. No. 5,063,081, U.S. Patent Application Publication Nos. 2003 / 0170881, 2004 / 0018577, 2005 / 0054078, and 2006 / 0160164, and commercially available, for example, by Abbott Laboratories (Abbott Park, Ill.) as Abbott Point of Care (i-STAT® or i-STAT Alinity, ID Now®, Abbott Laboratories), and those described in U.S. Pat. Nos. 5,089,424 and 5,006,309, and commercially available, for example, by Abbott Laboratories (Abbott Park, Ill.) as the ARCHITECT® or Abbott Alinity series of devices.

[0076] Other detection methods include, for example, the use of nanopore or nanowell devices for single-molecule detection or can be adapted for use in these devices. As used herein, the term "single-molecule detection" refers to the detection and / or measurement of a single molecule of an analyte at extremely low levels of concentration (e.g., pg / mL or femtogram / mL levels) in a test sample. Several different single-molecule analyzers or devices are known in the art, including nanopore and nanowell devices. Examples of nanopore devices are described in PCT International Application WO2016 / 161402, which is incorporated herein by reference in its entirety. Examples of nanowell devices are described in PCT International Application WO2016 / 161400, which is incorporated herein by reference in its entirety.

[0077] 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 entireties, with particular focus on assay methods. In certain embodiments, cTnI is detected by Singulex Inc.'s ERENNA detection assay system or Abbott's hs cTnI STAT ARCHITECT assay. In certain embodiments, methods for detecting troponin T employ the Elecsys® Troponin T high-sensitivity (TnT-hs) assay (ROCHE) (see Li et al., Arch Cardiovasc Dis. 2016 March;109(3):163-70, incorporated herein by reference in its entirety, for a description of high-sensitivity troponin T detection in particular).

[0078] Determining the level of galectin-3 in the subject typically involves measuring the level of the polypeptide using methods known in the art and / or described herein, such as an immunoassay, e.g., 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 using methods, again known in the art and / or described herein, such as quantitative PCR or Northern blot analysis. [Example]

[0079] The following examples are for illustrative purposes only and are not intended to limit the scope of the claims.

[0080] [Example 1] Galectin-3 is a biomarker associated with various biological processes important in heart failure, including myofibroblast proliferation, fibrogenesis, tissue repair, cardiac remodeling, and inflammation. We investigated whether the addition of galectin-3 (either baseline or sequentially) to the cardiac algorithm index score improves the distinction between patients with type I and type II MI.

[0081] Patient samples were evaluated for type I and type II MI using the primary endpoint and endpoint adjudication described below. Distinction between type I and type II MI attributable to baseline or sequential cardiac algorithm index scores supplemented with Gal-3 was compared to these benchmark classifications.

[0082] Primary outcome - Composite of death, non-fatal MI, and cardiac-related ED and readmission (all components adjudicated) After randomization, participants were followed for 1 to 3 years to determine the occurrence of this outcome.

[0083] a) Deaths include deaths from any cause.

[0084] b) Non-fatal MI was defined using the "universal definition" of MI, i.e., at least one troponin rise and / or fall above the 99th percentile of the upper reference limit accompanied by at least one of the following: a) symptoms of ischemia, b) ECG changes indicative of new ischemia, c) appearance of pathologic Q waves on the ECG, and d) imaging evidence of new loss of viable myocardium or new abnormalities in regional wall motion. This endpoint does not include infarcts present at randomization because they may not be related to the study intervention.

[0085] Adjudication of Evaluation Items All components of the primary composite were adjudicated using the consensus of three cardiovascular and critical care experts. Triggers for adjudication included reported death, uncertain vital status due to incomplete follow-up information, elevated troponin levels (excluding sequential increases and decreases present at enrollment), hospital readmission, ED visit, repeat cardiac testing after discharge, invasive angiography, and / or coronary revascularization. Adjudicated outcomes included the primary outcome, secondary outcomes, repeat cardiac testing and cardiac-related ED visits, and the post-discharge safety endpoint of ACS.

[0086] To conduct the evaluation, the reviewers had access to participants' index admission and discharge records, relevant test results, follow-up examination information, records obtained from follow-up, and trial definitions, in abstract form or actual data where necessary.

[0087] Algorithm performance Baseline Gal-3 concentrations were statistically significantly elevated in patients with type II MI compared with patients with type I MI. Adding baseline Gal-3 to the algorithm index score significantly improved the AUC compared with the MI3 baseline score alone for type I / II MI differentiation (Table 1 and Figure 1). The model yielded an area under the curve (AUC) of 0.776 (95% CI 0.693, 0.858) for discriminating between type I and type II MI. This AUC represents a statistically significant improvement compared with the AUC of the baseline algorithm alone (Delong's p-value 0.0416 for comparing AUCs).

[0088] The addition of baseline Gal-3 to the sequential algorithm index score improved the AUC compared to the MI3 sequential score alone for type I / II differentiation (Table 2 and Figure 2). This model yielded an area under the curve (AUC) of 0.791 (95% confidence interval (CI) 0.694, 0.888) for discriminating between type I and type II MI. While not a statistically significant improvement in AUC, similar to the baseline algorithm index above, the addition of Gal-3 to the sequential score yielded an Akaike information criterion (AIC) of 98.0, which was lower than the MI3 sequential score model alone, which had an AIC of 109.2, indicating improved fit (Table 2 and Figure 2).

[0089] [Table 1]

[0090] [Table 2]

[0091] Tables 3 and 4 show the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) at the optimal cutoff based on the shortest distance method for two models: one using baseline Gal-3 and baseline index scores, and a second model using baseline Gal-3 and sequential index scores, respectively.

[0092] [Table 3]

[0093] [Table 4]

[0094] 3 and 4 show predicted probability plots for both models, with the horizontal line representing the optimal cutoff. This plot provides a visual indication of model performance. As shown, the majority of Type I MI patients have values ​​above the cutoff score (dotted line), while the majority of Type II MI patients have values ​​below the cutoff score.

[0095] While a few exemplary embodiments have been described in detail, those skilled in the art will readily recognize that many modifications are possible in the exemplary embodiments without substantially departing from the novel teachings and advantages of the present disclosure. Accordingly, all such modifications and alternatives are intended to be included within the scope of the present invention as defined in the following claims. Those skilled in the art will also recognize that such modifications and equivalent constructions or methods do not depart from the spirit and scope of the present disclosure, and that various changes, substitutions, and alterations can be made herein without departing from the spirit and scope of the present disclosure.

Claims

1. 1. A method for determining whether a subject suspected of having a myocardial infarction is suffering from type I or type II myocardial infarction, comprising: a) obtaining a target value for said target, said target value being: i) the gender value of the subject; ii) the subject's age value; iii) a subject's initial cardiac troponin concentration from an initial sample from the subject; and iv) a subject Galectin-3 (Gal-3) concentration from an initial sample from said subject; b) processing said subject's sex, age, and cardiac troponin value with a processing system to thereby determine an algorithmic index score for said subject, said processing system comprising: i) a computer processor, and ii) a non-transitory computer memory containing one or more computer programs and a database, wherein the one or more computer programs include an additive tree algorithm; the database includes at least M decision trees, each individual decision tree including at least two predetermined splitting variables and at least three predetermined terminal node values; the at least two predetermined splitting variables are an initial cardiac troponin concentration threshold, a gender value, and / or an age value, and the one or more computer programs, in conjunction with the computer processor, i) applying the subject's initial cardiac troponin concentration, the subject's gender value, and / or the age value to the database to determine terminal node values ​​for each of the at least M decision trees; and ii) applying the additive tree algorithm to (a) determine a combination value from the M terminal node values; and (b) processing the combination value to determine an algorithm index score for the subject suffering from myocardial infarction; M is an integer of at least 2; c) reporting the algorithmic index score for the subject as 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 or type II myocardial infarction.

2. 1. A method for determining whether a subject suspected of having a myocardial infarction is suffering from type I or type II myocardial infarction, comprising: a) obtaining a target value for said target, said target value being: i) the gender value of the subject; ii) the subject's age value; iii) a subject's initial cardiac troponin concentration from an initial sample from the subject; iv) a subject's Galectin-3 (Gal-3) concentration from an initial sample from the subject; and v) including first, second, or first and second subsequent cardiac troponin concentrations from corresponding first and / or second subsequent samples from said subject; b) processing said subject's sex, age, and cardiac troponin value with a processing system to thereby determine an algorithmic index score for said subject, said processing system comprising: i) a computer processor, and ii) a non-transitory computer memory containing one or more computer programs and a database, the one or more computer programs including a rate of change algorithm and an additive tree algorithm; the database includes at least M decision trees, each individual decision tree including at least two predetermined splitting variables and at least three predetermined terminal node values; the at least two predetermined splitting variables are a cardiac troponin change rate threshold, an initial cardiac troponin concentration threshold, or a combination thereof, and a gender value and / or an age value, and the one or more computer programs, in conjunction with the computer processor, i) applying the rate of change algorithm to determine a cardiac troponin rate of change value for the subject 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 age value to the database to determine terminal node values ​​for each of the at least M decision trees; and iii) applying the additive tree algorithm to (a) determine a combination value from the M terminal node values; and (b) processing the combination value to determine an algorithm index score for the subject suffering from myocardial infarction; M is an integer of at least 2; and c) reporting the algorithmic index score for the subject as 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 or type II myocardial infarction based on the probability score.

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

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

5. 5. The method of claim 1, wherein obtaining the subject value comprises receiving the subject value from a laboratory, from the subject, from an analytical testing 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 of claims 1 to 4, wherein obtaining the target value comprises receiving the target value electronically.

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

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

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

13. The method of any one of claims 1 to 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 14, wherein the Gal-3 detection assay is a single molecule detection assay.

16. The method of any preceding claim, further comprising the step of manually or automatically inputting said target values ​​into said processing system.

17. 17. The method of any of claims 1 to 16, wherein the initial sample is collected from the subject in an emergency room, urgent care clinic, outpatient clinic, rehabilitation facility, nursing facility, ambulance, the subject's place of work, the subject's home, or any combination thereof.

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

19. The method of any preceding claim, wherein the initial sample from the subject comprises a sample of blood, serum or plasma.

20. The method of any preceding claim, wherein the first and / or second subsequent sample comprises a sample of blood, serum or plasma.

21. The method of any preceding claim, wherein the M decision trees are at least 100 different decision trees.

22. The method of any preceding claim, wherein the M decision trees are at least 800 different decision trees.

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