Meredith, Thomas and Mohammed, Farhan and Pomeroy, Amy and Barbieri, Sebastiano and Meijering, Erik and Jorm, Louisa and Roy, David and Kovacic, Jason and Feneley, Michael and Hayward, Christopher and Muller, David and Namasivayam, Mayooran (2025) Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients. Frontiers in Cardiovascular Medicine, 12. ISSN 2297-055X
Full text not available from this repository.Abstract
Background
Long-term mortality risk is seldom re-assessed in contemporary clinical practice following successful transcatheter aortic valve implantation (TAVR). Unsupervised machine learning permits pattern discovery within complex multidimensional patient data and may facilitate recognition of groups requiring closer post-TAVR surveillance.
Methods
We analysed and differentiated routinely collected demographic, biochemical, and cardiac imaging data into distinct clusters using unsupervised machine learning. k-means clustering was performed on data from 200 patients who underwent TAVR for severe aortic stenosis (AS). Input features were ranked according to their influence on cluster assignment. Survival analyses were performed with Kaplan–Meier and Cox proportional hazards models. Nested cox models were used to identify any incremental prognostic benefit cluster assignment achieved beyond conventional risk scores.
Results
Analysis identified two distinct clusters. Compared to Cluster 1, Cluster 2 demonstrated significantly worse all-cause mortality at 12 months (HR 6.3, p < 0.01), and was characterised by more advanced cardiac remodelling with worse indices of multi-chamber cardiac function, as quantified by strain imaging. Cluster assignment demonstrated greater predictive power for 12-month mortality as compared with conventional risk and frailty calculators.
Conclusion
k-means clustering identified two prognostically distinct phenogroups of patients who had undergone TAVR with better discriminatory power than conventional risk and frailty calculators. Our results highlight the utility of machine learning applications for clinical risk prediction and scope to improve patient surveillance.
| Item Type: | Article |
|---|---|
| Subjects: | R Medicine > R Medicine (General) |
| Depositing User: | Repository Administrator |
| Date Deposited: | 05 May 2025 06:43 |
| Last Modified: | 05 May 2025 06:43 |
| URI: | https://eprints.victorchang.edu.au/id/eprint/1692 |
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