Harnessing the power of artificial intelligence in predicting all-cause mortality in transcatheter aortic valve replacement: a systematic review and meta-analysis - Sazzad, Faizus; Ler, Ashlynn Ai Li; Furqan, Mohammad Shaheryar; Tan, Linus Kai Zhe; Leo, Hwa Liang; Kuntjoro, Ivandito; Tay, Edgar; Kofidis, Theo (Frontiers in Cardiovascular Medicine, 2024)
Objectives: In recent years, the use of artificial intelligence (AI) models to generate individualised risk assessments and predict patient outcomes post-Transcatheter Aortic Valve Implantation (TAVI) has been a topic of increasing relevance in literature. This study aims to evaluate the predictive accuracy of AI algorithms in forecasting post-TAVI mortality as compared to traditional risk scores. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Systematic Reviews (PRISMA) standard, a systematic review was carried out. We searched four databases in total—PubMed, Medline, Embase, and Cochrane—from 19 June 2023 to 24 June, 2023. Results: From 2239 identified records, 1504 duplicates were removed, 735 manuscripts were screened, and 10 studies were included in our review. Our pooled analysis of 5 studies and 9398 patients revealed a significantly higher mean area under curve (AUC) associated with AI mortality predictions than traditional score predictions (MD: -0.16, CI: -0.22 to -0.10, p<0.00001). Subgroup analyses of 30-day mortality (MD: -0.08, CI: -0.13 to -0.03, p=0.001) and 1-year mortality (MD: -0.18, CI: -0.27 to -0.10, p<0.0001) also showed significantly higher mean AUC with AI predictions than traditional score predictions. Pooled mean AUC of all 10 studies and 22933 patients was 0.79 [0.73, 0.85]. Conclusion: AI models have a higher predictive accuracy as compared to traditional risk scores in predicting post-TAVI mortality. Overall, this review demonstrates the potential of AI in achieving personalised risk assessment in TAVI patients.