Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

Salah Al-Zaiti (), Lucas Besomi, Zeineb Bouzid, Ziad Faramand, Stephanie Frisch, Christian Martin-Gill, Richard Gregg, Samir Saba, Clifton Callaway and Ervin Sejdić
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Salah Al-Zaiti: University of Pittsburgh
Lucas Besomi: University of Pittsburgh
Zeineb Bouzid: University of Pittsburgh
Ziad Faramand: University of Pittsburgh
Stephanie Frisch: University of Pittsburgh
Christian Martin-Gill: University of Pittsburgh
Richard Gregg: Philips Healthcare
Samir Saba: University of Pittsburgh
Clifton Callaway: University of Pittsburgh
Ervin Sejdić: University of Pittsburgh

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17804-2

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DOI: 10.1038/s41467-020-17804-2

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