Decision tree based systems and methods for estimating the risk of acute coronary syndrome

A method for reporting an estimated risk of acute coronary syndrome (ACS) in a subject suspected of having ACS, and performing an action based on said estimated risk of ACS, comprising:

• a) obtaining subject values for said subject, wherein said subject is suspected of having ACS, and wherein said subject values comprise:
• i) at least one of the following:
• subject gender value, a subject ECG classification, a subject hematology parameter value, and subject age value,
• ii) subject initial cardiac troponin I (cTnI) concentration from an initial sample from said subject, and
• iii) a first and/or second subsequent cTnI concentration from corresponding first and/or second subsequent samples from said subject;
• b) processing said subject values with a processing system such that an estimated risk of ACS is determined for said subject, wherein said processing system comprises:
• i) a computer processor, and
• ii) non-transitory computer memory comprising one or more computer programs and a database, wherein said one or more computer programs comprise:
• a rate of change algorithm and an additive tree algorithm, and wherein said database comprises at least 10 number of decision trees, wherein each individual decision tree comprises at least two pre-determined splitting variables and at least three pre-determined terminal node values, wherein said at least two pre-determined splitting variables are selected from the group consisting of:
• a threshold cTnI rate of change value, a threshold initial cTnI concentration value, and at least one of the following:
• a gender value, an ECG threshold classification, a hematology parameter threshold value, and an age threshold value,
• wherein said one or more computer programs, in conjunction with said computer processor, is/are configured to:
• A) apply said rate of change algorithm to determine a subject cTnI rate of change value from at least two of:
• said subject initial cTnI concentration, said first subsequent cTnI concentration, and said second subsequent cTnI concentration,
• B) apply said subject cTnI rate of change value, said subject initial cTnI concentration, and at least one of the following:
• said subject gender value, said subject ECG classification, said subject hematology parameter value, and said age value;
• to said database to determine a terminal node value for each of said at least 10 number of decision trees, and
• C) apply said additive tree algorithm to:
• I) determine a combined value from M number of said terminal node values, and II) process said combined value to determine an estimated risk of ACS for said subject;
• c) reporting said estimated risk of ACS for said subject determined by said processing system;
• d) identifying said subject as having a high estimated risk of ACS;
• and
• e) performing an action on the subject identified as having a high estimated risk of ACS, wherein said action is at least one of the following:
• 1) Performing coronary catheterization on the subject;
• 2) Treating the subject with a cardiovascular (CVD) therapeutic, or
• 3) admitting the subject to a hospital.