ArticleEuropean heart journal. Digital health2026
Unsupervised machine learning analysis to enhance risk stratification in patients with asymptomatic aortic stenosis.
Article in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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19 authors.
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Abstract
Aims: 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. Methods and results: 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 (V Conclusion: 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.
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