Data regarding high-sensitivity troponin concentrations in patients presenting to the emergency department with symptoms suggestive of myocardial infarction may be useful in determining the probability of myocardial infarction and subsequent 30-day outcomes.

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.

Chest pain is one of the most common reasons for presentation to hospitals worldwide.¹ Despite the majority of patients not having myocardial infarction,² hospital admission for observation and serial cardiac troponin testing is required in many patients to identify those with and without myocardial infarction.³ Novel strategies to identify low-risk patients at presentation have been proposed to reduce hospital admissions, serial testing, and resource utilization as well as to improve care for patients.^4,5

Early diagnosis of acute myocardial infarction (AMI) in patients presenting with acute chest pain improves clinical outcome. Moreover, rapid exclusion of AMI is important to triage patients in view of limited resources in the emergency department. Measurement of cardiac troponin levels, as a marker of myocyte necrosis, is essential for diagnosing AMI.¹ Compared with sensitive troponin assays, high-sensitivity troponin assays enhance the accuracy and speed of the diagnosis,^1-4 improve outcome, and are cost-effective.⁵