An evidence-based screening tool for heart failure with preserved ejection fraction: the HFpEF-ABA score.
Yogesh N V ReddyRickey E CarterVarun SundaramDavid M KayeM Louis HandokoRyan J TedfordMads Jønsson AndersenKavita SharmaMasaru ObokataFrederik Hendrik VerbruggeBarry A BorlaugPublished in: Nature medicine (2024)
Heart failure with preserved ejection fraction (HFpEF) is under-recognized in clinical practice. Although a previously developed risk score, termed H 2 FPEF, can be used to estimate HFpEF probability, this score requires imaging data, which is often unavailable. Here we sought to develop an HFpEF screening model that is based exclusively on clinical variables and that can guide the need for echocardiography and further testing. In a derivation cohort (n = 414, 249 women), a clinical model using age, body mass index and history of atrial fibrillation (termed the HFpEF-ABA score) showed good discrimination (area under the curve (AUC) = 0.839 (95% confidence interval (CI) = 0.800-0.877), P < 0.0001). The performance of the model was validated in an international, multicenter cohort (n = 736, 443 women; AUC = 0.813 (95% CI = 0.779-0.847), P < 0.0001) and further validated in two additional cohorts: a cohort including patients with unexplained dyspnea (n = 228, 136 women; AUC = 0.840 (95% CI = 0.782-0.900), P < 0.0001) and a cohort for which HF hospitalization was used instead of hemodynamics to establish an HFpEF diagnosis (n = 456, 272 women; AUC = 0.929 (95% CI = 0.909-0.948), P < 0.0001). Model-based probabilities were also associated with increased risk of HF hospitalization or death among patients from the Mayo Clinic (n = 790) and a US national cohort across the Veteran Affairs health system (n = 3076, 110 women). Using the HFpEF-ABA score, rapid and efficient screening for risk of undiagnosed HFpEF can be performed in patients with dyspnea using only age, body mass index and history of atrial fibrillation.
Keyphrases
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- machine learning
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