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https://doi.org/10.3389/fcvm.2024.1343210
Title: | Harnessing the power of artificial intelligence in predicting all-cause mortality in transcatheter aortic valve replacement: a systematic review and meta-analysis | Authors: | Sazzad, Faizus Ler, Ashlynn Ai Li Furqan, Mohammad Shaheryar Tan, Linus Kai Zhe Leo, Hwa Liang Kuntjoro, Ivandito Tay, Edgar Kofidis, Theo |
Keywords: | aortic valve replacement transcatheter systematic review transcatheter aortic valve prosthesis mortality artificial intelligence machine learning |
Issue Date: | 31-May-2024 | Publisher: | Frontiers Media SA | Citation: | Sazzad, Faizus, Ler, Ashlynn Ai Li, Furqan, Mohammad Shaheryar, Tan, Linus Kai Zhe, Leo, Hwa Liang, Kuntjoro, Ivandito, Tay, Edgar, Kofidis, Theo (2024-05-31). Harnessing the power of artificial intelligence in predicting all-cause mortality in transcatheter aortic valve replacement: a systematic review and meta-analysis. Frontiers in Cardiovascular Medicine 11. ScholarBank@NUS Repository. https://doi.org/10.3389/fcvm.2024.1343210 | Abstract: | 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 2023–24 June, 2023. Results: From 2,239 identified records, 1,504 duplicates were removed, 735 manuscripts were screened, and 10 studies were included in our review. Our pooled analysis of 5 studies and 9,398 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 22,933 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. Registration and protocol: This systematic review and meta-analysis was registered under the International Prospective Register of Systematic Reviews (PROSPERO), under the registration name “All-Cause Mortality in Transcatheter Aortic Valve Replacement Assessed by Artificial Intelligence” and registration number CRD42023437705. A review protocol was not prepared. There were no amendments to the information provided at registration. Systematic Review Registration: https://www.crd.york.ac.uk/, PROSPERO (CRD42023437705). | Source Title: | Frontiers in Cardiovascular Medicine | URI: | https://scholarbank.nus.edu.sg/handle/10635/248610 | ISSN: | 2297-055X | DOI: | 10.3389/fcvm.2024.1343210 |
Appears in Collections: | Students Publications Staff Publications Elements |
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47. fcvm-11-1343210_TAVI-AI.pdf | Published version | 6.58 MB | Adobe PDF | OPEN | Published | View/Download |
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