# Unsupervised machine learning analysis to enhance risk stratification in patients with asymptomatic aortic stenosis

## TL;DR

This study uses machine learning to identify five distinct groups of patients with asymptomatic aortic stenosis, improving risk prediction and management.

## Contribution

The novel use of unsupervised machine learning to classify asymptomatic aortic stenosis patients into distinct phenogroups for better risk stratification.

## Key findings

- Five distinct patient clusters were identified based on echocardiographic and clinical data.
- Cluster 1 showed higher baseline severity and faster progression of aortic stenosis.
- Cluster 1 was associated with higher risk of mortality and aortic valve replacement.

## Abstract

There is a lack of studies investigating the pathophysiologic and phenotypic distinctiveness of aortic stenosis (AS). This heterogeneity has important implications for identifying optimal intervention timing and potential medical management. This study seeks to identify phenogroups of AS using unsupervised machine learning to improve risk stratification.

A total of 349 patients with asymptomatic AS from the PROGRESSA study were included in this analysis. Echocardiographic, clinical and blood sample data were used in the unsupervised clustering process. Longitudinal echocardiographic data were used to evaluate AS progression. Five clusters of patients were revealed using 18 variables selected by an unsupervised machine learning algorithm. Amongst them, aortic valvular phenotype, mean gradient, peak jet velocity (Vpeak), and left ventricle stroke volume were selected as discriminatory variables. Following the clustering process, characteristics differed between clusters, including age, body mass index, and sex ratio (all P < 0.001). Of note, cluster 1 showed higher AS severity at baseline with significantly higher initial Vpeak (344 [314; 376] cm/s) and calcium score (1257 [806; 1837] UA) (P < 0.001). Patients from cluster 1 had a faster AS progression (progression of Vpeak = 22 [9; 39] cm/s/year), and calcium score (213 [111; 307] UA/year) (P < 0.001). Cluster 1 was also associated with a higher composite risk of mortality and aortic valve replacement when adjusted for age, sex, and baseline AS severity (P < 0.001).

Artificial intelligence-guided phenotypic classification revealed 5 distinct groups and enhanced risk stratification of patients with AS. This approach may be useful to optimize and individualize medical and interventional management of AS.

Graphical Abstract