Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients.

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 m

SUBMITTER: Meredith T 

PROVIDER: S-EPMC11836646 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature