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An evidence-based screening tool for heart failure with preserved ejection fraction: the HFpEF-ABA score

Nature Medicine volume 30, pages 2258–2264 (2024)Cite this article

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Abstract

Heart failure with preserved ejection fraction (HFpEF) is under-recognized in clinical practice. Although a previously developed risk score, termed H2FPEF, 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.

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Fig. 1: Discrimination of HFpEF from noncardiac dyspnea using the HFpEF-ABA score.
Fig. 2: Risk of HF hospitalization or death according to the HFpEF-ABA score.

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Data availability

The data from the derivation and validation cohorts needed to derive and validate the HFpEF-ABA score is publicly available from Figshare via https://doi.org/10.6084/m9.figshare.26020810.v1 (ref. 35) with restrictions. Data from the national VA health system can only be accessed through a remote desktop connection within the VA network; raw data cannot be transferred outside the remote desktop environment. If other investigators are interested in performing additional analyses, requests can be made to the corresponding author, B.A.B.; analyses can be performed in collaboration with the Mayo Clinic. Timelines for each request vary but can take up to 6 months for analysis, anonymization and sharing of the requested data.

Code availability

All code used to derive and validate the HFpEF-ABA score across all cohorts is available from Figshare via https://doi.org/10.6084/m9.figshare.26020810.v1 (ref. 35).

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Acknowledgements

This work was supported by National Institutes of Health (NIH) grant nos. R01 HL128526 (B.A.B.), R01 HL162828 (B.A.B.), U01 HL160226 (B.A.B.) and K23HL164901 (Y.N.V.R.) from the NIH, and no. W81XWH2210245 (B.A.B.) from the US Department of Defense.

Author information

Authors and Affiliations

  1. The Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA

    Yogesh N. V. Reddy & Barry A. Borlaug

  2. Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA

    Rickey E. Carter

  3. Department of Cardiology, Louis Stokes Cleveland Department of Veteran Affairs, Case Western Reserve University, Cleveland, OH, USA

    Varun Sundaram

  4. Department of Cardiology, Alfred Hospital, Melbourne, Victoria, Australia

    David M. Kaye

  5. Departments of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Cardiovascular Sciences (ACS), Amsterdam, the Netherlands

    M. Louis Handoko

  6. Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA

    Ryan J. Tedford

  7. Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark

    Mads J. Andersen

  8. Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Kavita Sharma

  9. Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan

    Masaru Obokata

  10. Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Belgium

    Frederik H. Verbrugge

  11. Centre for Cardiovascular Diseases, University Hospital Brussels, Brussels, Belgium

    Frederik H. Verbrugge

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  1. Yogesh N. V. Reddy
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Contributions

Y.N.V.R. and B.A.B. contributed to study design. Y.N.V.R., R.E.C., V.S. and B.A.B. were responsible for the statistical analyses. Y.N.V.R., V.S., D.M.K., M.L.H., R.J.T., M.J.A., K.S., M.O., F.H.V. and B.A.B. collected the clinical data. Y.N.V.R. wrote the first draft with oversight from B.A.B. All authors participated in the interpretation of the data and critical review of the paper, and had responsibility for the decision to submit for publication.

Corresponding author

Correspondence to Barry A. Borlaug.

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Competing interests

B.A.B. receives research support from the NIH and the US Department of Defense, as well as research grant funding from AstraZeneca, Axon, GSK, Medtronic, Mesoblast, Novo Nordisk and Tenax Therapeutics. He has served as a consultant for Actelion, Amgen, Aria, BD, Boehringer Ingelheim, Cytokinetics, Edwards Lifesciences, Eli Lilly, Janssen, Merck and Novo Nordisk. B.A.B. and S.J.A. are named inventors (US patent no. 10,307,179) for the tools and approach for a minimally invasive pericardial modification procedure to treat heart failure. Y.N.V.R. receives research support from the NIH, Sleep Number, Bayer, Merck and United Pharmaceuticals. M.L.H. reported receiving grants from the Dutch Heart Foundation and educational, speaker and consultancy fees from Novartis, Boehringer Ingelheim, AstraZeneca, Vifor Pharma, Bayer, Merck, Abbott, Daiichi Sankyo and Quin outside the submitted work. R.J.T. reports no direct conflicts of interest related to this manuscript. He is co-chair of the Pulmonary Hypertension due to Left Heart Disease Task Force for the 7th World Symposium on Pulmonary Hypertension. He reports general disclosures that include consulting relationships with Abbott, Acorai, Aria CV, Acceleron/Merck, Alleviant, CareDx, Cytokinetics, Edwards LifeSciences, Gradient, Lexicon Pharmaceuticals, Medtronic and United Therapeutics. R.J.T. serves on the steering committees for Merck, Edwards and Abbott, as well as a research advisory board member for Abiomed. He also does hemodynamic core laboratory work for Merck. M.J.A. reports no direct conflicts of interest related to this manuscript. He reports a consulting relationship with Johnson & Johnson. F.H.V. reports no direct conflicts of interest related to this manuscript. He reports a consulting relationship with Abbott Laboratories, Abiomed, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb Belgium, Daiichi Sankyo, Menarini Benelux, MSD, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics and Qompium. The other authors declare no competing interests.

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Nature Medicine thanks Stefan Koudstaal, Shelley Zieroth and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Michael Basson, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Calibration plots in Derivation and Primary Validation Cohort.

Predicted probabilities of HFpEF by the HFpEF-ABA score are grouped by deciles and plotted against the actual prevalence of HFpEF in each decile in ambulatory derivation (A) and primary validation cohort (B).

Extended Data Table 1 Baseline characteristics of Ambulatory Secondary Validation Cohort
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Extended Data Table 2 Baseline characteristics of VA ambulatory HFpEF cohort with type 2 diabetes
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Extended Data Table 3 Diagnostic performance of Single Variable based Prediction Algorithm in Derivation and Validation Cohorts
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Extended Data Table 4 Regression equations for clinical variable models
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Extended Data Table 5 Clinical characteristics of those with missing NT-proBNP values in the primary validation cohort compared to those with available NT-proBNP levels
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Extended Data Table 6 Diagnostic performance of Clinical Models in Ambulatory Secondary Validation Cohort
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Extended Data Table 7 Diagnostic performance of HFpEF-ABA Score in Derivation and Validation Cohorts Stratified by Sex
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Extended Data Table 8 Change from pre to post test probability of HFpEF at various rule-out or rule-in HFpEF-ABA thresholds
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Extended Data Table 9 Diagnostic performance of HFpEF-ABA Score in Derivation and Validation cohorts compared to H2FPEF and HFA-PEFF scores
Full size table

Supplementary information

Reporting Summary

Supplementary Data

Supplementary Table 1. The Excel-based calculator can be downloaded and used to compute the HFpEF probability using the HFpEF-ABA score.

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Reddy, Y.N.V., Carter, R.E., Sundaram, V. et al. An evidence-based screening tool for heart failure with preserved ejection fraction: the HFpEF-ABA score. Nat Med 30, 2258–2264 (2024). https://doi.org/10.1038/s41591-024-03140-1

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