145 Machine learning for prediction of incident heart failure and heart failure hospitalisation: developlment, validation and assocation with clinical phenotypes

Background Delayed heart failure (HF) diagnosis is associated with adverse outcomes and costs of hospitalisation. Whilst previous tools have been developed to predict incident HF, risk of HF incidence may correlate poorly with risk of HF hospitalisation, which may be more important in targeting diagnostics. Purpose To develop and validate a novel decision support tool for prediction of incident HF and HF hospitalisation, and investigate association of score with HF with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF). Methods We developed the models (FIND-HF) in UK primary care health record data from individuals aged ≥40 years without known HF (Jan 2, 1998 to Feb 28, 2022), randomly divided into training (80%) and testing (20%) datasets. We evaluated logistic regression and supervised machine learning models for prediction of 1- and 5- year HF and HF hospitalisation risk in the testing dataset with internal bootstrap validation. Models were externally validated for HF hospitalisation in data from the National Taiwan University Hospital. Association of FIND-HF score with HF phenotypes of HFpEF and HFrEF was assessed in two prospective cohorts, the MATCH registry of patients diagnosed with HFrEF and the FIND-AF cohort (NCT 05898165) of individuals at higher predicted risk of atrial fibrillation who underwent echocardiography. Results Of 565 284 UK individuals (mean age 68.9 (SD 12.2) years, 49.2% women), incidence of HF and HF hospitalisation was 1.1% and 0.5% at 1-year, and 6.6% and 3.4% at 5-years, respectively. Prediction performance was superior for machine learning algorithms compared with logistic regression, with best performance for XGBoost (area under receiver operating characteristic curve (AUROC) at 1 and 5-years: 0.755 and 0.723 for incident HF, and 0.738 and 0.711 for incident HF hospitalization). On external validation in 106,026 individuals in Taiwan (mean age 64.7 (SD 10.6) years, 52.8 % women), HF hospitalisation incidence was 0.71%, and 2.46% at 1- and 5- years, respectively, and discrimination performance was excellent (AUROC at 1- and 5-years: 0.834 and 0.843). When the FIND-HF score was applied to individuals in the MATCH cohort (n = 133, mean age 69.0 (SD 11.7) years, 38.3% women) and FIND-AF cohort (n = 82, mean age 71.6 (SD 7.5) years, 50.0% women), the distribution of score to clinical phenotypes demonstrated a binomial distribution (figure 1), and likelihood of HFpEF diagnosis correlated with the numeric score (figure 2). Conclusions The machine learning FIND-HF decision support tool can accurately identify individuals at risk of incident HF and HF hospitalisation, to enable new approaches for early detection and prevention. The variation of score amongst high risk individuals with different HF clinical phenotypes emphasizes the distinct pathophysiological pathways but shared adverse outcome profile across the HF syndrome. Conflict of Interest None