Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement

Abstract

An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important for clinical decision making. The aims of this study were to investigate the significance and utility of pre‐ and post‐TAVR ECG data and compare machine learning approaches with traditional logistic regression in predicting pacemaker risk following TAVR.