{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T12:39:59Z","timestamp":1789043999504,"version":"build-2803163510"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Canada research Chair in Valvular Heart Diseases"},{"name":"Canadian Institutes of Helath research","award":["143225"],"award-info":[{"award-number":["143225"]}]},{"name":"Canadian Institutes of Helath research","award":["FRQS\u2014Health Data Hub"],"award-info":[{"award-number":["FRQS\u2014Health Data Hub"]}]},{"name":"INRIA International Research Chair"},{"name":"3IA C\u00f4te d\u2019Azur Investments in the Future project"},{"DOI":"10.13039\/501100001665","name":"National Research Agency","doi-asserted-by":"crossref","award":["#ANR-19-P3IA-0002"],"award-info":[{"award-number":["#ANR-19-P3IA-0002"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>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 &amp;lt; 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 &amp;lt; 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 &amp;lt; 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 &amp;lt; 0.001).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztaf115","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T11:46:09Z","timestamp":1759837569000},"source":"Crossref","is-referenced-by-count":1,"title":["Unsupervised machine learning analysis to enhance risk stratification in patients with asymptomatic aortic stenosis"],"prefix":"10.1093","volume":"7","author":[{"given":"Marie-Ange","family":"Fleury","sequence":"first","affiliation":[{"name":"Institut universitaire de cardiologie et de pneumologie de Qu\u00e9bec, Universit\u00e9 Laval, Laval University , 2725 Chemin Sainte-Foy, Quebec city, Qu\u00e9bec G1V-4G5 ,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Louis","family":"Ohl","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, STIMA\u2014Link\u00f6ping University , Link\u00f6ping ,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lionel","family":"Tastet","sequence":"additional","affiliation":[{"name":"Department of Medicine, Cardiovascular Division, University of California San Francisco , San Francisco, CA ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Micka\u00ebl","family":"Leclercq","sequence":"additional","affiliation":[{"name":"Computational biology, Facult\u00e9 de m\u00e9decine, Universit\u00e9 Laval, CHU de Qu\u00e9bec Research Center , 2705 boul. 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