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

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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Al-Zaiti S,Besomi L,Bouzid Z,Faramand Z,Frisch S,Martin-Gill C,Gregg R,Saba S,Callaway C,Sejdić E

doi

10.1038/s41467-020-17804-2

subject

Has Abstract

pub_date

2020-08-07 00:00:00

pages

3966

issue

1

issn

2041-1723

pii

10.1038/s41467-020-17804-2

journal_volume

11

pub_type

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