Phenogrouping heart failure with preserved or mildly reduced ejection fraction using electronic health record data.

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  • Additional Information
    • Source:
      Publisher: BioMed Central Country of Publication: England NLM ID: 100968539 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2261 (Electronic) Linking ISSN: 14712261 NLM ISO Abbreviation: BMC Cardiovasc Disord Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : BioMed Central, [2001-
    • Subject Terms:
      Heart Failure*/physiopathology ; Heart Failure*/diagnosis ; Heart Failure*/mortality ; Electronic Health Records* ; Stroke Volume* ; Ventricular Function, Left*; Humans ; Female ; Male ; Aged ; Middle Aged ; Risk Assessment ; United Kingdom/epidemiology ; Risk Factors ; Prognosis ; Aged, 80 and over ; Databases, Factual ; Unsupervised Machine Learning ; Hospitalization ; Time Factors ; Comorbidity ; Cause of Death ; Phenotype ; Data Mining
    • 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.
      (© 2024. The Author(s).)
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    • Grant Information:
      AI_AWARD01864 National Institute for Health and Care Research; NIHR301338 National Institute for Health and Care Research; Horizon Europe Guarantee for DataTools4Heart UK Research and Innovation; AA/18/6/24223 United Kingdom BHF_ British Heart Foundation; FS/CRA/22/23036 United Kingdom BHF_ British Heart Foundation; AA/18/4/34221 United Kingdom BHF_ British Heart Foundation; RE/18/4/34215 BHF Imperial Centre for Research Excellence
    • Contributed Indexing:
      Keywords: Electronic health records; Heart failure with preserved or mildly reduced ejection fraction; Machine learning
    • Publication Date:
      Date Created: 20240705 Date Completed: 20240705 Latest Revision: 20241113
    • Publication Date:
      20241114
    • Accession Number:
      PMC11229019
    • Accession Number:
      10.1186/s12872-024-03987-9
    • Accession Number:
      38969974