First-line risk stratification with machine learning models facilitates rapid triage for non-ST-elevation myocardial infarction

Wei-Jia Luo, Yih-Mei Liou, Cheng-Han Hsiao, Chi-Sheng Hung, Heng-Yu Pan, Chien-Hua Huang, Pan-Chyr Yang and Kang-Yi Su

PLOS Digital Health, 2026, vol. 5, issue 2, 1-16

Abstract: Timely diagnosis of non-ST-elevation myocardial infarction (NSTEMI) remains challenging, as current protocols rely on serial high-sensitivity cardiac troponin (hs-cTn) tests that may delay decisions and overcrowd emergency departments. We retrospectively analyzed 54,636 patients receiving hs-cTn testing at emergency departments across Taiwan (May 2016–Dec 2021). Excluding STEMI and incomplete cases, we developed a machine learning (ML) model using demographics and 23 routine lab tests from the initial blood draw to enable early NSTEMI risk stratification. An actionable clinical decision supporting algorithm was also created based on ML-derived risk scores. A total of 15,096 eligible patients (mean age 69.94 ± 15.66 years; 42.2% female) were included in model training and evaluation. The ML model outperformed hs-cTn alone in both internal and external validation sets in terms of area under the receiver-operating characteristic curve. Beyond model development, a clinically actionable decision algorithm using risk score was established. Thresholds (

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0001260

DOI: 10.1371/journal.pdig.0001260

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