Chapman, Andrew R.

Abstract Background Type 2 myocardial infarction (T2MI) is frequently encountered in clinical practice and associated with adverse outcomes. Content T2MI occurs most frequently due to noncoronary etiologies that alter myocardial oxygen supply and/or demand. The diagnosis of T2MI is often confused with acute nonischemic myocardial injury, in part because of difficulties in delineating the nature of symptoms and misunderstandings about disease categorization. The use of objective features of myocardial ischemia using electrocardiographic (ECG) or imaging abnormalities may facilitate more precise T2MI diagnosis. High-sensitivity cardiac troponin (hs-cTn) assays allow rapid MI diagnosis and risk stratification, yet neither maximum nor delta values facilitate differentiation of T2MI from T1MI. Several investigational biomarkers have been evaluated for T2MI, but none have robust data. There is interest in evaluating risk profiles among patients with T2MI. Clinically, the magnitude of maximum and delta cTn values as well as the presence and magnitude of ischemia on ECG or imaging is used to indicate disease severity. Scoring systems such as GRACE, TIMI, and TARRACO have been evaluated, but all have limited to modest performance, with substantial variation in time intervals used for risk-assessment and endpoints used. Summary The diagnosis of T2MI requires biomarker evidence of acute myocardial injury and clear clinical evidence of acute myocardial ischemia without atherothrombosis. T2MIs are most often caused by noncoronary etiologies that alter myocardial oxygen supply and/or demand. They are increasingly encountered in clinical practice and associated with poor short- and long-term outcomes. Clinicians require novel biomarker or imaging approaches to facilitate diagnosis and risk-stratification.

Background: Variations in cardiac troponin concentrations by age, sex, and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. Methods: A machine learning algorithm (myocardial-ischemic-injury-index [MI 3 ]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3013 patients and tested on 7998 patients with suspected myocardial infarction. MI 3 uses gradient boosting to compute a value (0–100) reflecting an individual’s likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value, specificity and positive predictive value for that individual. Assessment was by calibration and area under the receiver operating characteristic curve. Secondary analysis evaluated example MI 3 thresholds from the training set that identified patients as low risk (99% sensitivity) and high risk (75% positive predictive value), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology rule-out pathways. Results: Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI 3 was well calibrated with a very high area under the receiver operating characteristic curve of 0.963 [0.956–0.971] in the test set and similar performance in early and late presenters. Example MI 3 thresholds identifying low- and high-risk patients in the training set were 1.6 and 49.7, respectively. In the test set, MI 3 values were <1.6 in 69.5% with a negative predictive value of 99.7% (99.5–99.8%) and sensitivity of 97.8% (96.7–98.7%), and were ≥49.7 in 10.6% with a positive predictive value of 71.8% (68.9–75.0%) and specificity of 96.7% (96.3–97.1%). Using these thresholds, MI 3 performed better than the European Society of Cardiology 0/3-hour pathway (sensitivity, 82.5% [74.5–88.8%]; specificity, 92.2% [90.7–93.5%]) and the 99th percentile at any time point (sensitivity, 89.6% [87.4–91.6%]); specificity, 89.3% [88.6–90.0%]). Conclusions: Using machine learning, MI 3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low- and high-risk patients who may benefit from earlier clinical decisions. Clinical Trial Registration: URL: https://www.anzctr.org.au . Unique identifier: ACTRN12616001441404.