Predicting long-term risk of sudden cardiac death with automatic computer-interpretations of electrocardiogram.

ABSTRACT:

Background

Computer-interpreted electrocardiogram (CIE) data is provided by almost all commercial software used to capture and store digital electrocardiograms. CIE is widely available, inexpensive, and accurate. We tested the potential of CIE in long-term sudden cardiac death (SCD) risk prediction.

Methods

This is a retrospective of 8,568 consecutive patients treated for acute coronary syndrome. The primary endpoint was five-year occurrence of SCDs or equivalent events (SCDs aborted by successful resuscitation or adequate ICD therapy). CIE statements were extracted from summary statements and measurements made by the GE Muse 12SL algorithm from ECGs taken during admission. Three supervised machine learning algorithms (logistic regression, extreme gradient boosting, and rand

SUBMITTER: Jarvensivu-Koivunen M 

PROVIDER: S-EPMC11537987 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature