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Unsupervised Machine Learning Analysis to Enhance Risk Stratification in Patients with Asymptomatic Aortic Stenosis

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Title: Unsupervised Machine Learning Analysis to Enhance Risk Stratification in Patients with Asymptomatic Aortic Stenosis
Authors: Fleury, Marie-Ange, Ohl, Louis, Tastet, Lionel, Leclercq, Mickaël, Precioso, Frédéric, Mattei, Pierre-Alexandre, Capoulade, Romain, Abdoun, Kathia, Bédard, Élisabeth, Arsenault, Marie, Beaudoin, Jonathan, Bernier, Mathieu, Salaun, Erwan, Bernard, Jérémy, Shen, Mylène, Hecht, Sébastien, Côté, Nancy, Droit, Arnaud, Pibarot, Philippe
Source: The European Heart Journal - Digital Health.
Subject Terms: Unsupervised machine learning, clustering, aortic stenosis, echocardiography, risk stratification
Description: Background: 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: 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.Results: 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).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.
File Description: electronic
Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-219150
https://doi.org/10.1093/ehjdh/ztaf115
Database: SwePub
Description
Abstract:<strong>Background:</strong> 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.<strong>Methods:</strong> 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.<strong>Results:</strong> 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).<strong>Conclusion:</strong> 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.
ISSN:26343916
DOI:10.1093/ehjdh/ztaf115