Machine-learning-derived phenotypes of hypertensive patients using multidimensional clinical and echocardiographic data including strain imaging.

In-Chang Hwang, Hyue Mee Kim, Jiesuck Park, Hong-Mi Choi, Yeonyee E Yoon, Goo-Yeong Cho

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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. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

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6 authors.

In-Chang HwangDepartment of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, 82 Gumi-ro-173-gil, Bundang, Seongnam, Gyeonggi 13620, South Korea.ORCID https://orcid.org/0000-0003-4966-3924

Hyue Mee KimDivision of Cardiology, Department of Internal Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, 102 Heukseok-ro, Dongjak-gu, Seoul 06973, South Korea.

Jiesuck ParkDepartment of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, 82 Gumi-ro-173-gil, Bundang, Seongnam, Gyeonggi 13620, South Korea.

Hong-Mi ChoiDepartment of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, 82 Gumi-ro-173-gil, Bundang, Seongnam, Gyeonggi 13620, South Korea.

Yeonyee E YoonDepartment of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, 82 Gumi-ro-173-gil, Bundang, Seongnam, Gyeonggi 13620, South Korea.

Goo-Yeong ChoDepartment of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, 82 Gumi-ro-173-gil, Bundang, Seongnam, Gyeonggi 13620, South Korea.ORCID https://orcid.org/0000-0002-7067-5535

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: 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. Methods and results: We analysed 1607 patients from the STRATS-HHD registry who underwent echocardiography at baseline and after 6-18 months of therapy. Twenty clinical, laboratory, and echocardiographic variables-including left atrial and left ventricular strain-underwent principal component analysis and Conclusion: 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.

Indexed as

HypertensionHypertensive heart diseaseMachine-learningPhenotype clusteringUnsupervised clustering

Identifiers

PMID41768037

DOI10.1093/ehjdh/ztag027

PMCPMC12940115

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