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- Title
Phenogrouping heart failure with preserved or mildly reduced ejection fraction using electronic health record data.
- Authors
Soltani, Fardad; Jenkins, David A.; Kaura, Amit; Bradley, Joshua; Black, Nicholas; Farrant, John P.; Williams, Simon G.; Mulla, Abdulrahim; Glampson, Benjamin; Davies, Jim; Papadimitriou, Dimitri; Woods, Kerrie; Shah, Anoop D.; Thursz, Mark R.; Williams, Bryan; Asselbergs, Folkert W.; Mayer, Erik K.; Herbert, Christopher; Grant, Stuart; Curzen, Nick
- Abstract
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 < 0.001). Phenogroup membership significantly improved survival prediction compared to conventional factors. Phenogroups were not predictive of hospitalisation for HF. Conclusions: Applying unsupervised machine learning to routinely collected electronic health record data identified phenogroups with distinct clinical characteristics and unique survival profiles.
- Subjects
UNITED Kingdom; HEART failure; ELECTRONIC health records; VENTRICULAR ejection fraction; DATA recorders &; recording; MEDICAL informatics; HEART metabolism disorders
- Publication
BMC Cardiovascular Disorders, 2024, Vol 24, Issue 1, p1
- ISSN
1471-2261
- Publication type
Article
- DOI
10.1186/s12872-024-03987-9