Journal of Clinical Medicine
Machine Learning for Clinical Outcome Prediction After Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis of Predictive Performance, Validation, and Clinical Readiness
By Azin Alizadehasl, Elahe Abdi Bastami, Seyedeh Fatemeh Hosseini Jebelli and 12 others.
Abstract
Background
Machine learning (ML) models are increasingly used to predict adverse outcomes after transcatheter aortic valve replacement (TAVR), but their generalizability and clinical readiness remain uncertain. We evaluated predictive performance across post-TAVR outcomes, emphasizing validation quality, transportability, methodological bias, and clinical utility.
Methods
PubMed/MEDLINE, Embase, Scopus, and Web of Science were searched from inception through 6 August 2026 for supervised ML studies reporting out-of-sample performance for clinically relevant post-TAVR outcomes. Overlapping source populations were audited before synthesis. Areas under the receiver operating characteristic curve (AUCs) were pooled on the logit scale using restricted maximum likelihood (REML) random-effects models with modified Hartung–Knapp inference and prediction intervals. Prespecified analyses examined validation maturity, predictor timing, prediction horizon, data architecture, and risk of bias. Within-study dependence was addressed using multilevel meta-regression with bias-reduced cluster-robust variance estimation (CR2); Bayesian analyses complemented the primary synthesis.