Harnessing the Power of Artificial Intelligence in Predicting All-cause Mortality in Transcatheter Aortic Valve Replacement: a Systematic Review and Meta-analysis

Overview

Journal Front Cardiovasc Med

Specialty Cardiology & Vascular Diseases

Date 2024 Jun 17

PMID 38883982

Authors

Faizus Sazzad

Ashlynn Ai Li Ler

Mohammad Shaheryar Furqan

Linus Kai Zhe Tan

Hwa Liang Leo

Ivandito Kuntjoro

Edgar Tay

Theo Kofidis

Affiliations

Soon will be listed here.

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, < 0.00001). Subgroup analyses of 30-day mortality (MD: -0.08, CI: -0.13 to -0.03, = 0.001) and 1-year mortality (MD: -0.18, CI: -0.27 to -0.10, < 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).

Citing Articles

Machine-learning versus traditional methods for prediction of all-cause mortality after transcatheter aortic valve implantation: a systematic review and meta-analysis.

Zaka A, Mustafiz C, Mutahar D, Sinhal S, Gorcilov J, Muston B Open Heart. 2025; 12(1).

PMID: 39842939 PMC: 11784135. DOI: 10.1136/openhrt-2024-002779.

Identifying the Severity of Heart Valve Stenosis and Regurgitation Among a Diverse Population Within an Integrated Health Care System: Natural Language Processing Approach.

Xie F, Lee M, Allahwerdy S, Getahun D, Wessler B, Chen W JMIR Cardio. 2024; 8:e60503.

PMID: 39348175 PMC: 11474122. DOI: 10.2196/60503.

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