Machine Learning to Assess for Acute Myocardial Infarction within 30 Minutes
McCord J, Gibbs J, Hudson M, Moyer M, Jacobsen G, Murtagh G, and Nowak R. Machine Learning to Assess for Acute Myocardial Infarction within 30 Minutes. Crit Pathw Cardiol 2022.
Crit Pathw Cardiol
Variations in high-sensitivity cardiac troponin I (hs-cTnI) by age and sex along with various sampling times can make the evaluation for acute myocardial infarction (AMI) challenging. Machine learning integrates these variables to allow a more accurate evaluation for possible AMI. The goal was to test the diagnostic and prognostic utility of a machine learning algorithm in the evaluation of possible AMI. We applied a machine learning algorithm (myocardial-ischemic-injury-index [MI3]) that incorporates age, sex, and hs-cTnI levels at time 0 and 30 minutes in 529 patients evaluated for possible AMI in a single urban emergency department. MI3 generates an index value from 0-100 reflecting the likelihood of AMI. Patients were followed at 30-45 days for major adverse cardiac events (MACE). There were 42 (7.9%) patients that had an AMI. Patients were divided into 3 groups by the MI3 score: low-risk (≤ 3.13), intermediate-risk (> 3.13-51.0), and high-risk (> 51.0). The sensitivity for AMI was 100% with a MI3 value ≤ 3.13 and 353 (67%) ruled-out for AMI at 30 minutes. At 30-45 days there were 2 (0.6%) MACEs (2 non-cardiac deaths) in the low-risk group, in the intermediate-risk group 4 (3.0%) MACEs (3 AMIs, 1 cardiac death), and in the high-risk group 4 (9.1%) MACEs (4 AMIs, 2 cardiac deaths). The MI3 algorithm had 100% sensitivity for AMI at 30 minutes and identified a low-risk cohort who may be considered for early discharge.
ePub ahead of print