A registry‐based algorithm to predict ejection fraction in patients with heart failure

Abstract Aims Left ventricular ejection fraction (EF) is required to categorize heart failure (HF) [i.e. HF with preserved (HFpEF), mid‐range (HFmrEF), and reduced (HFrEF) EF] but is often not captured in population‐based cohorts or non‐HF registries. The aim was to create an algorithm that identifies EF subphenotypes for research purposes. Methods and results We included 42 061 HF patients from the Swedish Heart Failure Registry. As primary analysis, we performed two logistic regression models including 22 variables to predict (i) EF≥ vs.