Machine-learning approaches for risk prediction in transcatheter aortic valve implantation: Systematic review and meta-analysis

Objectives With the expanding integration of artificial intelligence (AI) and machine learning (ML) into the structural heart domain, numerous ML models have emerged for the prediction of adverse outcomes following transcatheter aortic valve implantation (TAVI). We aim to identify, describe, and critically appraise ML prediction models for adverse outcomes after TAVI. Key objectives consisted in summarizing model performance, evaluating adherence to reporting guidelines, and transparency. Methods We searched PubMed, SCOPUS, and Embase through August 2023. We selected published machine learning models predicting TAVI outcomes. Two reviewers independently screened articles, extracted data, and assessed the study quality according to the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Outcomes included summary C-statistics and model risk of bias assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). C-statistics were pooled using a random-effects model. Results Twenty-one studies (118,153 patients) employing various ML algorithms (76 models) were included in the systematic review. Predictive ability of models varied: 11.8% inadequate (C-statistic <0.60), 26.3% adequate (C-statistic 0.60–0.70), 31.6% acceptable (C-statistic 0.70–0.80), and 30.3% demonstrated excellent (C-statistic >0.80) performance. Meta-analyses revealed excellent predictive performance for early mortality (C-statistic: 0.81 [95% CI, 0.65-0.91]), acceptable performance for 1-year mortality (C-statistic: 0.76 [95% CI, 0.67-0.84]), and acceptable performance for predicting permanent pacemaker implantation (C-statistic: 0.75 [95% CI, 0.51-0.90]). Conclusion ML models for TAVI outcomes exhibit adequate to excellent performance, suggesting potential clinical utility. We identified concerns in methodology and transparency, emphasizing the need for improved scientific reporting standards.