Machine learning vs. conventional methods for prediction of 30-day readmission following percutaneous mitral edge-to-edge repair.

Background: Identifying predictors of readmissions after mitral valve transcatheter edge-to-edge repair (MV-TEER) is essential for risk stratification and optimization of clinical outcomes.

Aims: We investigated the performance of machine learning [ML] algorithms vs. logistic regression in predicting readmissions after MV-TEER.