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Updated: Aug 5, 2026
Upper-extremity Approach for Secondary Access in Transfemoral Transcatheter Aortic Valve Implantation
Published on: August 8, 2025
Early predictive value of prediction models for mortality after transcatheter aortic valve replacement: a systematic
Ruiyan Wang1, Mengyu He2, Ziting Yuan1
1School of Nursing, Bengbu Medical University, Bengbu, Anhui, China.
Background:
Transcatheter aortic valve replacement (TAVR) is increasingly used due to the rising incidence of aortic stenosis (AS). Early identification of mortality risk after TAVR is challenging. Although various prediction models have been developed, no systematic review has evaluated their effectiveness in predicting mortality risk. Therefore, this study aimed to systematically evaluate the performance of models for early prediction of mortality risk after TAVR, so as to provide evidence-based support for the future development or updating of risk assessment tools.
Methods:
Databases (PubMed, Web of Science, Embase, and Cochrane Library) were systematically searched for studies on tools for predicting the risk of mortality after TAVR, up to June 2024. PROBAST was used to assess the risk of bias in the included studies. A subgroup analysis was conducted based on different time points.
Results:
This systematic review included 36 studies with 272,390 patients receiving TAVR and 6 major scoring tools encompassing 23 new machine learning models. The meta-analysis showed that the concordance index (C-index) was 0.610 (95% CI: 0.588-0.632) for European System for Cardiac Operative Risk Evaluation I (EuroSCORE I), 0.615 (95% CI: 0.588-0.643) for EuroSCORE II, 0.578 (95% CI: 0.531-0.625) for French Aortic National CoreValve and Edwards II (France II), 0.594 (95% CI: 0.554-0.633) for the OBSERVANT score, 0.648 (95% CI: 0.622-0.674) for the Society of Thoracic Surgeons (STS) risk model, 0.632 (95% CI: 0.616-0.648) for the American College of Cardiology Transcatheter Valve Therapy (ACC TVT) risk model, and 0.705 (95% CI: 0.677-0.733) for summarized machine learning models.
Conclusion:
Determining the predictive performance of current established risk assessment tools for predicting the risk of modality after TAVR is challenging. Machine learning models seem to be more effective. Therefore, future research should include more subjects to develop more accurate models.
Systematic Review Registration:
https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023485237.