Key result
AI/ML models predict 1-year mortality post-TAVR with moderate discrimination and an AUC of 0.76.
Why the study?
Conventional surgical and TAVR-specific risk scores have poor predictive ability for mortality after TAVR, while AI/ML models may better capture complex variable interdependencies.
Do artificial intelligence/machine learning models improve the prediction of 1-year mortality in patients undergoing TAVR?
Meta-Analysis (n=1,732)
Do artificial intelligence/machine learning models improve the prediction of 1-year mortality in patients undergoing TAVR?
Effect estimate: AUC 0.76 (95% CI 0.71-0.81)
p-value: p=<0.01
AI/ML models demonstrate moderate discrimination ability (pooled AUC 0.76) for predicting 1-year mortality following TAVR, highlighting their potential utility in risk stratification.
AI/ML models may improve post-TAVR mortality prediction and selection; confirms superior performance over conventional scores.
The prediction of mortality after transcatheter aortic valve replacement (TAVR) is important in risk stratification, selection of patients, and the joint decision-making process. Conventional surgical and TAVR-specific risk scores have poor predictive ability, while artificial intelligence (AI)/machine learning (ML) models can provide better performance in capturing the complex nature of interdependencies between variables. This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 protocol. PubMed, Scopus, and Google Scholar were systematically searched from inception to September 2025 for papers describing AI/ML models used in predicting mortality at 1 year following TAVR. The eligible papers reported either area under the receiver operating characteristic curve (AUC)/C-statistics with 95% confidence intervals (95% CIs) or had enough data for calculation. Papers that lacked comparable outcome measures, did not report on discrimination, or did not employ AI/ML methods were excluded from further analysis. The pooled estimates were estimated using the random-effects model. The risk of bias and applicability assessment was performed using PROBAST criteria. Four retrospective studies comprising 1,732 patients who underwent TAVR were included. ML-based gradient boosting and clustering were used as methods. The overall pooled estimate of AUC for 1-year mortality prediction was 0.76 (95% CI: 0.71–0.81; p < 0.01) with considerable heterogeneity (I 2 = 64.76%, p = 0.04). Sensitivity analysis with the leave-one-out method showed consistent results for pooled estimates. AI/ML models proved their ability in predicting 1-year mortality following TAVR with moderate discrimination ability. Future research requires larger sample sizes in prospective studies.
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Senapati et al. (2026) conducted a meta-analysis in Transcatheter Aortic Valve Replacement (TAVR) (n=1,732). Artificial intelligence and machine learning models was evaluated on 1-year mortality prediction (AUC) (AUC 0.76, 95% CI 0.71-0.81, p=<0.01). Artificial intelligence and machine learning models predicted 1-year mortality following TAVR with moderate discrimination (pooled AUC 0.76; 95% CI 0.71-0.81; p<0.01).
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