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 indicated a significant association between PRAISE score and risk of both AF (AUC 0.89, p = 0.0001; optimal cut-off 5.77%) and VA (AUC 0.69, p = 0.0001; optimal cut-off 2.17%). Based on these thresholds, high/low AF PRAISE score groups and high/low VA PRAISE score groups were created, respectively. Patients with a high AF PRAISE score more frequently developed in-hospital AF (19% vs. 1%). Multivariate analysis showed a high AF PRAISE score risk as an independent predictor of AF (HR 4.30, p = 0.016). Patients with high VA PRAISE scores more frequently developed in-hospital VA (25% vs. 8% for VA ≤ 48 h; 33% vs. 3% for VA > 48 h). Multivariate analysis demonstrated a high VA PRAISE score risk as an independent predictor of both VA ≤ 48 h (HR 2.48, p = 0.032) and VA > 48 h (HR 4.93, p = 0.014).

Conclusion: The PRAISE score has a comprehensive ability to identify with high specificity those patients at risk for arrhythmic events during hospitalization for ACS.

Keywords: PRAISE risk score; acute coronary syndrome; arrhythmias; machine learning.