Machine Learning-based Prediction of Pacing-Induced Cardiomyopathy in Patients with Atrioventricular Block Underlying Heart Failure with Mildly Reduced or Preserved Ejection Fraction

Background: Pacing-induced cardiomyopathy (PICM) is a major complication of chronic right ventricular pacing and develops in up to 25% of patients. Reliable prediction of PICM may facilitate individualized pacing strategy selection; however, currently available approaches demonstrate limited discriminative performance. This study aimed to develop and validate machine learning (ML) models for predicting PICM in patients with atrioventricular block (AVB) underlying heart failure with mildly reduced or preserved ejection fraction. Methods: In this multicenter prospective study derived from the RI

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