ArticleEuropean heart journal. Digital health2021
Echocardiographic phenogrouping by machine learning for risk stratification in the general population.
Article in European heart journal. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- AI-Enabled Precision Echocardiography: Toward Personalized Cardiovascular Care.Diagnostics (Basel, Switzerland) · 2026Review
- Preoperative activation of the renin-angiotensin system and myocardial injury in noncardiac surgery: exploratory mechanistic analysis of the SPACE randomised controlled trial.British journal of anaesthesia · 2025Article
- Comprehensive review of reinforcement learning for medical ultrasound imaging.Artificial intelligence review · 2025Article
- Atrial Strain and Strain Rate in a General Population: Do These Measures Improve the Assessment of Elevated NT-proBNP Levels?Cardiology research and practice · 2024Article
- Integrative Interpretation of Cardiopulmonary Exercise Tests for Cardiovascular Outcome Prediction: A Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus.PloS one · 2023Article
- Feature-based clustering of the left ventricular strain curve for cardiovascular risk stratification in the general population.Frontiers in cardiovascular medicine · 2023Article
Corrections and comments
- Erratum issuedErratum.2022
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: There is a need for better phenotypic characterization of the asymptomatic stages of cardiac maladaptation. We tested the hypothesis that an unsupervised clustering analysis utilizing echocardiographic indexes reflecting left heart structure and function could identify phenotypically distinct groups of asymptomatic individuals in the general population. Methods and results: We prospectively studied 1407 community-dwelling individuals (mean age, 51.2 years; 51.1% women), in whom we performed clinical and echocardiographic examination at baseline and collected cardiac events on average 8.8 years later. Cardiac phenotypes that were correlated at Conclusion: Unsupervised learning algorithms integrating routinely measured cardiac imaging and haemodynamic data can provide a clinically meaningful classification of cardiac health in asymptomatic individuals. This approach might facilitate early detection of cardiac maladaptation and improve risk stratification.
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Registered trials
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