Stratification of Early Arrhythmic Risk in Patients Admitted for Acute Coronary Syndrome: The Role of the Machine Learning-Derived "PRAISE Score".

ABSTRACT:

Background

The PRAISE (PRedicting with Artificial Intelligence riSk aftEr acute coronary syndrome) score is a machine learning-based model for predicting 1-year adverse cardiovascular or bleeding events in patients with acute coronary syndrome (ACS). Its role in predicting arrhythmic complications in ACS remains unknown.

Methods

Atrial fibrillation (AF) and ventricular arrhythmias (VA) were recorded by continuous electrocardiographic monitoring until discharge in a cohort of 365 participants with ACS prospectively enrolled. We considered two separate timeframes for VA occurrence: ≤ 48 and > 48 h. The objective was to evaluate the ability of the PRAISE score to identify ACS patients at higher risk of in-hospital arrhythmic complications.

Results

ROC curve analysis indi

SUBMITTER: Cumitini L 

PROVIDER: S-EPMC11656403 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature