10-Year Risk Prediction of Higher-Grade AV Block in Patients with First-Degree AV Block

Abstract

Background: First-degree atrioventricular (AV) block has traditionally been considered benign, but emerging evidence suggests it may indicate a risk of progression to higher-degree AV block. This study developed and externally validated a machine learning model to predict AV block progression using ECG-derived parameters. Methods: A retrospective cohort study was conducted using 12-lead ECG data from Severance Hospital (development) and Yongin Severance Hospital (external validation). The model was trained with six ECG-derived parameters (RR interval, P duration, PR segment, PR interval, QRS duration, QT interval), along with age and sex, using a Random Forest algorithm. Results: It achieved an AUROC of 0.823 (AUPRC 0.719) in internal validation and AUROC 0.808 (AUPRC 0.894) in external validation. SHAP analysis identified PR segment, QRS duration, and age as key predictors. Conclusion: This model enables early risk stratification for AV block progression using widely available ECG parameters, supporting clinical decision-making. ©2024 AMIA - All rights reserved.