Shah, Anoop

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.

Abstract BACKGROUND The universal definition of myocardial infarction (UDMI) standardizes the approach to the diagnosis and management of myocardial infarction. High-sensitivity cardiac troponin testing is recommended because these assays have improved precision at low concentrations, but concerns over specificity may have limited their implementation. METHODS We undertook a global survey of 1902 medical centers in 23 countries evenly distributed across 5 continents to assess adoption of key recommendations from the UDMI. Respondents involved in the diagnosis and management of patients with suspected acute coronary syndrome completed a structured telephone questionnaire detailing the primary biomarker, diagnostic thresholds, and clinical pathways used to identify myocardial infarction. RESULTS Cardiac troponin was the primary diagnostic biomarker at 96% of surveyed sites. Only 41% of centers had adopted high-sensitivity assays, with wide variation from 7% in North America to 60% in Europe. Sites using high-sensitivity troponin more frequently used serial sampling pathways (91% vs 78%) and the 99th percentile diagnostic threshold (74% vs 66%) than sites using previous-generation assays. Furthermore, high-sensitivity institutions more often used earlier serial sampling (≤3 h) and accelerated diagnostic pathways. Fewer than 1 in 5 high-sensitivity sites had adopted sex-specific thresholds (18%). CONCLUSIONS There has been global progress toward the recommendations of the UDMI, particularly in the use of the 99th percentile diagnostic threshold and serial sampling. However, high-sensitivity assays are still used by a minority of sites, and sex-specific thresholds by even fewer. Additional efforts are required to improve risk stratification and diagnosis of patients with myocardial infarction.