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BACKGROUND: Heart failure (HF) with preserved or mildly reduced ejection fraction includes a heterogenous group of patients. Reclassification into distinct phenogroups to enable targeted interventions is a priority. This study aimed to identify distinct phenogroups, and compare phenogroup characteristics and outcomes, from electronic health record data. METHODS: 2,187 patients admitted to five UK hospitals with a diagnosis of HF and a left ventricular ejection fraction ≥ 40% were identified from the NIHR Health Informatics Collaborative database. Partition-based, model-based, and density-based machine learning clustering techniques were applied. Cox Proportional Hazards and Fine-Gray competing risks models were used to compare outcomes (all-cause mortality and hospitalisation for HF) across phenogroups. RESULTS: Three phenogroups were identified: (1) Younger, predominantly female patients with high prevalence of cardiometabolic and coronary disease; (2) More frail patients, with higher rates of lung disease and atrial fibrillation; (3) Patients characterised by systemic inflammation and high rates of diabetes and renal dysfunction. Survival profiles were distinct, with an increasing risk of all-cause mortality from phenogroups 1 to 3 (p 

Original publication

DOI

10.1186/s12872-024-03987-9

Type

Journal article

Journal

BMC Cardiovasc Disord

Publication Date

05/07/2024

Volume

24

Keywords

Electronic health records, Heart failure with preserved or mildly reduced ejection fraction, Machine learning, Humans, Heart Failure, Electronic Health Records, Female, Male, Aged, Stroke Volume, Middle Aged, Risk Assessment, Ventricular Function, Left, United Kingdom, Risk Factors, Prognosis, Aged, 80 and over, Databases, Factual, Unsupervised Machine Learning, Hospitalization, Time Factors, Comorbidity, Cause of Death, Phenotype, Data Mining