ArticleEuropean heart journal open2024
Phenotyping of heart failure with preserved ejection faction using electronic health records and echocardiography.
Article in European heart journal open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.BMC medical informatics and decision making · 2026Pooled it
- A Sentence Classification-Based Medical Status Extraction Pipeline for Electronic Health Records: Institutional Case Study.JMIR medical informatics · 2026Article
- Machine-Learning-Driven Phenotyping in Heart Failure with Preserved Ejection Fraction: Current Approaches and Future Directions.Medicina (Kaunas, Lithuania) · 2025Review
- Cardiac intermediary metabolism in heart failure: substrate use, signalling roles and therapeutic targets.Nature reviews. Cardiology · 2025Review
- Sex-specific cardiometabolic multimorbidity, metabolic syndrome and left ventricular function in heart failure with preserved ejection fraction in the UK Biobank.Cardiovascular diabetology · 2025Article
- Non-Invasive Hemodynamic Assessment of Heart Failure With Preserved Ejection Fraction.Korean circulation journal · 2025Review
- Towards a phenotype profiling of the patients with heart failure and preserved ejection fraction.European heart journal supplements : journal of the European Society of Cardiology · 2025Article
- Can heart failure phenotypes be predicted by cardiac remodelling peripartum or postpartum?Frontiers in cardiovascular medicine · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Patients presenting symptoms of heart failure with preserved ejection fraction (HFpEF) are not a homogenous population. Different phenotypes can differ in prognosis and optimal management strategies. We sought to identify phenotypes of HFpEF by using the medical information database from a large university hospital centre using machine learning. Methods and results: We explored the use of clinical variables from electronic health records in addition to echocardiography to identify different phenotypes of patients with HFpEF. The proposed methodology identifies four phenotypic clusters based on both clinical and echocardiographic characteristics, which have differing prognoses (death and cardiovascular hospitalization). Conclusion: This work demonstrated that artificial intelligence-derived phenotypes could be used as a tool for physicians to assess risk and to target therapies that may improve outcomes.
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Registered trials
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