{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T22:23:58Z","timestamp":1793139838083,"version":"4.5.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>We applied unsupervised machine learning clustering to a large cohort of hypertensive patients undergoing echocardiography with strain imaging to identify phenotypes with distinct clinical profiles, comorbidities, remodelling trajectories, and outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>We analysed 1607 patients from the STRATS-HHD registry who underwent echocardiography at baseline and after 6\u201318\u2005months of therapy. Twenty clinical, laboratory, and echocardiographic variables\u2014including left atrial and left ventricular strain\u2014underwent principal component analysis and K-means clustering (K = 4). Clusters were derived in the SNUBH cohort (n = 1204) and validated in the CAUH cohort (n = 403), two institutional subsets of the registry. Remodelling trajectories were assessed using baseline-adjusted models, and associations with outcomes were evaluated using multivariable Cox regression. Four clusters emerged: (i) atrial fibrillation-predominant, with advanced remodelling and the highest event risk; (ii) elderly, with metabolic\u2013renal comorbidities but preserved function; (iii) middle-aged, with prevalent coronary disease and relatively preserved function; and (iv) younger, with severe hypertension, marked strain impairment, and the greatest remodelling regression with therapy. Prognosis varied: cluster 1 had the highest risk of cardiovascular death, heart failure hospitalization, stroke, and major adverse cardiovascular events (MACE); cluster 2 exhibited increased cardiovascular death and intermediate heart failure hospitalization risk; cluster 3 showed elevated coronary risk; and cluster 4 the most favourable outcomes. Associations between medication and remodelling varied, with renin\u2013angiotensin blockade linked to LV mass regression in cluster 4.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine learning -based clustering incorporating strain identified four distinct HHD phenotypes with divergent remodelling, therapeutic responses, and outcomes. Data-driven phenotyping may improve risk stratification and enable tailored management in hypertension.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztag027","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T12:46:49Z","timestamp":1770295609000},"source":"Crossref","is-referenced-by-count":6,"title":["Machine-learning-derived phenotypes of hypertensive patients using multidimensional clinical and echocardiographic data including strain imaging"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4966-3924","authenticated-orcid":false,"given":"In-Chang","family":"Hwang","sequence":"first","affiliation":[{"name":"Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital , 82 Gumi-ro-173-gil, Bundang, Seongnam, Gyeonggi 13620 ,","place":["South Korea"]},{"name":"Department of Internal Medicine, Seoul National University College of Medicine , 103 Daehak-ro, Jongno-gu, Seoul 03080 ,","place":["South Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyue Mee","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Department of Internal Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine , 102 Heukseok-ro, Dongjak-gu, Seoul 06973 ,","place":["South Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiesuck","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital , 82 Gumi-ro-173-gil, Bundang, Seongnam, Gyeonggi 13620 ,","place":["South Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Mi","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital , 82 Gumi-ro-173-gil, Bundang, Seongnam, Gyeonggi 13620 ,","place":["South Korea"]},{"name":"Department of Internal Medicine, Seoul National University College of Medicine , 103 Daehak-ro, Jongno-gu, Seoul 03080 ,","place":["South Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeonyee E","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital , 82 Gumi-ro-173-gil, Bundang, Seongnam, Gyeonggi 13620 ,","place":["South Korea"]},{"name":"Department of Internal Medicine, Seoul National University College of Medicine , 103 Daehak-ro, Jongno-gu, Seoul 03080 ,","place":["South Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7067-5535","authenticated-orcid":false,"given":"Goo-Yeong","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital , 82 Gumi-ro-173-gil, Bundang, Seongnam, Gyeonggi 13620 ,","place":["South Korea"]},{"name":"Department of Internal Medicine, Seoul National University College of Medicine , 103 Daehak-ro, Jongno-gu, Seoul 03080 ,","place":["South Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"2026022611265860600_ztag027-B1","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/S0140-6736(21)01330-1","article-title":"Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants","volume":"398","author":"Collaboration NCDRF","year":"2021","journal-title":"Lancet"},{"key":"2026022611265860600_ztag027-B2","article-title":"2025 AHA\/ACC\/AANP\/AAPA\/ABC\/ACCP\/ACPM\/AGS\/AMA\/ASPC\/NMA\/PCNA\/SGIM Guideline for the prevention, detection, evaluation and management of high blood pressure in adults: a report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines","author":"Jones"},{"key":"2026022611265860600_ztag027-B3","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1161\/HYPERTENSIONAHA.123.20894","article-title":"Characterization of individuals with apparent resistant hypertension using contemporary guidelines: insights from CV-QUIC","volume":"80","author":"Ebinger","year":"2023","journal-title":"Hypertension"},{"key":"2026022611265860600_ztag027-B4","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1093\/eurheartj\/ehac395","article-title":"Hypertension management in patients with cardiovascular comorbidities","volume":"44","author":"Lauder","year":"2023","journal-title":"Eur Heart J"},{"key":"2026022611265860600_ztag027-B5","doi-asserted-by":"crossref","first-page":"3912","DOI":"10.1093\/eurheartj\/ehae178","article-title":"2024 ESC Guidelines for the management of elevated blood pressure and hypertension","volume":"45","author":"McEvoy","year":"2024","journal-title":"Eur Heart J"},{"key":"2026022611265860600_ztag027-B6","doi-asserted-by":"crossref","first-page":"e009707","DOI":"10.1161\/CIRCIMAGING.119.009707","article-title":"Unsupervised cluster analysis of patients with aortic stenosis reveals distinct population with different phenotypes and outcomes","volume":"13","author":"Kwak","year":"2020","journal-title":"Circ Cardiovasc Imaging"},{"key":"2026022611265860600_ztag027-B7","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1136\/heartjnl-2022-321305","article-title":"Long-term outcomes in distinct phenogroups of patients with primary mitral regurgitation undergoing valve surgery","volume":"109","author":"Kwak","year":"2023","journal-title":"Heart"},{"key":"2026022611265860600_ztag027-B8","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.jacasi.2023.12.001","article-title":"Machine learning-based discrimination of cardiovascular outcomes in patients with hypertrophic cardiomyopathy","volume":"4","author":"Rhee","year":"2024","journal-title":"JACC Asia"},{"key":"2026022611265860600_ztag027-B9","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s12265-017-9739-z","article-title":"Phenomapping for the identification of hypertensive patients with the myocardial substrate for heart failure with preserved ejection fraction","volume":"10","author":"Katz","year":"2017","journal-title":"J Cardiovasc Transl Res"},{"key":"2026022611265860600_ztag027-B10","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1177\/2047487319856733","article-title":"Phenomapping of subgroups in hypertensive patients using unsupervised data-driven cluster analysis: an exploratory study of the SPRINT trial","volume":"26","author":"Yang","year":"2019","journal-title":"Eur J Prev Cardiol"},{"key":"2026022611265860600_ztag027-B11","doi-asserted-by":"crossref","first-page":"1546","DOI":"10.1111\/jch.13984","article-title":"Unsupervised hierarchical clustering identifies a metabolically challenged subgroup of hypertensive individuals","volume":"22","author":"Vaura","year":"2020","journal-title":"J Clin Hypertens"},{"key":"2026022611265860600_ztag027-B12","doi-asserted-by":"publisher","author":"Rauseo","DOI":"10.1161\/HYPERTENSIONAHA.125.25187"},{"key":"2026022611265860600_ztag027-B13","doi-asserted-by":"crossref","first-page":"e796","DOI":"10.1016\/S2589-7500(22)00170-4","article-title":"Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials","volume":"4","author":"Oikonomou","year":"2022","journal-title":"Lancet Digit Health"},{"key":"2026022611265860600_ztag027-B14","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1093\/ehjci\/jez300","article-title":"Derivation and validation of a mortality risk prediction model using global longitudinal strain in patients with acute heart failure","volume":"21","author":"Hwang","year":"2020","journal-title":"Eur Heart J Cardiovasc Imaging"},{"key":"2026022611265860600_ztag027-B15","doi-asserted-by":"crossref","first-page":"2327","DOI":"10.1016\/j.jcmg.2020.05.014","article-title":"Left atrial strain measurement: a new normal for diastolic assessment?","volume":"13","author":"Cho","year":"2020","journal-title":"JACC Cardiovasc Imaging"},{"key":"2026022611265860600_ztag027-B16","first-page":"577","article-title":"Recommendations on the use of echocardiography in adult hypertension: a report from the European Association of Cardiovascular Imaging (EACVI) and the American Society of Echocardiography (ASE)","volume":"16","author":"Marwick","year":"2015","journal-title":"Eur Heart J Cardiovasc Imaging"},{"key":"2026022611265860600_ztag027-B17","doi-asserted-by":"crossref","first-page":"e042032","DOI":"10.1161\/JAHA.125.042032","article-title":"Left ventricular mass-to-strain ratio to predict change in left ventricular hypertrophy and prognosis in hypertensive heart disease","volume":"14","author":"Hwang","year":"2025","journal-title":"J Am Heart Assoc"},{"key":"2026022611265860600_ztag027-B18","doi-asserted-by":"crossref","first-page":"1082008","DOI":"10.3389\/fcvm.2022.1082008","article-title":"Prognostic implication of left ventricular hypertrophy regression after antihypertensive therapy in patients with hypertension","volume":"9","author":"Kim","year":"2022","journal-title":"Front Cardiovasc Med"},{"key":"2026022611265860600_ztag027-B19","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1038\/s41440-023-01571-w","article-title":"Age-dependent implications of left ventricular hypertrophy regression in patients with hypertension","volume":"47","author":"Chu","year":"2024","journal-title":"Hypertens Res"},{"key":"2026022611265860600_ztag027-B20","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1097\/HJH.0000000000003875","article-title":"Impact of changes in left heart geometry on predicting new-onset atrial fibrillation in patients with hypertension","volume":"43","author":"Kim","year":"2025","journal-title":"J Hypertens"},{"key":"2026022611265860600_ztag027-B21","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1093\/ehjci\/jev014","article-title":"Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging","volume":"16","author":"Lang","year":"2015","journal-title":"Eur Heart J Cardiovasc Imaging"},{"key":"2026022611265860600_ztag027-B22","doi-asserted-by":"crossref","first-page":"743","DOI":"10.4070\/kcj.2024.0060","article-title":"An artificial intelligence-based automated echocardiographic analysis: enhancing efficiency and prognostic evaluation in patients with revascularized STEMI","volume":"54","author":"Jang","year":"2024","journal-title":"Korean Circ J"},{"key":"2026022611265860600_ztag027-B23","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1007\/s12574-025-00694-y","article-title":"Novel deep learning framework for simultaneous assessment of left ventricular mass and longitudinal strain: clinical feasibility and validation in patients with hypertrophic cardiomyopathy","volume":"23","author":"Park","year":"2025","journal-title":"J Echocardiogr"},{"key":"2026022611265860600_ztag027-B24","doi-asserted-by":"crossref","first-page":"352","DOI":"10.21037\/cdt-24-25","article-title":"Artificial intelligence-enhanced automation of left ventricular diastolic assessment: a pilot study for feasibility, diagnostic validation, and outcome prediction","volume":"14","author":"Park","year":"2024","journal-title":"Cardiovasc Diagn Ther"},{"key":"2026022611265860600_ztag027-B25","doi-asserted-by":"crossref","first-page":"105560","DOI":"10.1016\/j.ebiom.2025.105560","article-title":"Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiography","volume":"112","author":"Park","year":"2025","journal-title":"EBioMedicine"},{"key":"2026022611265860600_ztag027-B26","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.1093\/eurheartj\/ehab368","article-title":"2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure","volume":"42","author":"McDonagh","year":"2021","journal-title":"Eur Heart J"},{"key":"2026022611265860600_ztag027-B27","doi-asserted-by":"crossref","first-page":"e038108","DOI":"10.1161\/JAHA.124.038108","article-title":"Influence of echocardiographic criteria on the prevalence and prognostic implication of left ventricular hypertrophy: a nationwide population-based study","volume":"14","author":"Cai","year":"2025","journal-title":"J Am Heart Assoc"}],"container-title":["European Heart Journal - Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/ehjdh\/advance-article-pdf\/doi\/10.1093\/ehjdh\/ztag027\/66822204\/ztag027.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/7\/2\/ztag027\/66822204\/ztag027.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/7\/2\/ztag027\/66822204\/ztag027.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:27:06Z","timestamp":1772123226000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/ehjdh\/article\/doi\/10.1093\/ehjdh\/ztag027\/8469564"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,27]]},"references-count":27,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,1,27]]}},"URL":"https:\/\/doi.org\/10.1093\/ehjdh\/ztag027","relation":{},"ISSN":["2634-3916"],"issn-type":[{"value":"2634-3916","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,3]]},"published":{"date-parts":[[2026,1,27]]},"article-number":"ztag027"}}