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.