Artificial Intelligence-Based Frailty Assessment Outperforms Clinical Assessment in Predicting Outcomes After Transcatheter Aortic Valve Implantation

Heart Lung and Circulation 2026 Open access

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Authors: Annora Ai-Wei Kumar, Gavin Huangfu, Jake Kendrick, Jeremy S. L. Ong, Gerald Yong, Sharad V. Shetty, Nick Si Rui Lan, Abdul Rahman Ihdayhid, Girish Dwivedi
DOI: 10.1016/j.hlc.2025.12.015
Cited by: 1
References: 6

Abstract

In older patients at risk of frailty, transcatheter aortic valve implantation (TAVI) is an accessible and minimally invasive option for the treatment of aortic stenosis. Frailty is characterised by decreased physiological reserve and increased susceptibility to stressors, and accurate frailty assessment is crucial due to the implications for preprocedural optimisation and postprocedural recovery [1]. Traditional clinical frailty assessment is performed using validated tools including the Rockwood Clinical Frailty Scale (CFS).

Fields of Study & Subjects

Cardiac Valve Diseases and Treatments Frailty in Older Adults Cardiovascular Function and Risk Factors

Publication details

Journal / source
Heart Lung and Circulation
Publisher
Elsevier BV
Volume
35
Issue
6
Pages
e81–e83
Publication date
2 March 2026
Language
EN