Does an electrocardiogram-based machine learning model improve mortality risk stratification compared to the HEART score in patients with suspected acute coronary syndrome?
An ECG-based machine learning model outperforms the standard HEART score for mortality risk stratification in patients with acute chest pain, potentially improving triage and resource allocation.
The externally validated machine learning-based model, exclusively utilizing features from the 12-lead electrocardiogram, outperformed the HEART score in stratifying the mortality risk of patients with acute chest pain. This may have the potential to impact the precision of care delivery and the allocation of resources to those at highest risk of adverse events.
Bouzid et al. (Mon,) studied this question.