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Machine learning to optimize use of natriuretic peptides in the diagnosis of acute heart failure.

Publication ,  Journal Article
Doudesis, D; Lee, KK; Anwar, M; Singer, AJ; Hollander, JE; Chenevier-Gobeaux, C; Claessens, Y-E; Wussler, D; Weil, D; Kozhuharov, N; Strebel, I ...
Published in: Eur Heart J Acute Cardiovasc Care
August 7, 2025

AIMS: B-type natriuretic peptide (BNP) and mid-regional pro-atrial natriuretic peptide (MR-proANP) testing are guideline-recommended to aid in the diagnosis of acute heart failure. Nevertheless, the diagnostic performance of these biomarkers is uncertain. METHODS AND RESULTS: We performed a systematic review and individual patient-level data meta-analysis to evaluate the diagnostic performance of BNP and MR-proANP. We subsequently developed and externally validated a decision-support tool called CoDE-HF that combines natriuretic peptide concentrations with clinical variables using machine learning to report the probability of acute heart failure. Fourteen studies from 12 countries provided individual patient-level data in 8493 patients for BNP and 3899 patients for MR-proANP, in whom, 48.3% (4105/8493) and 41.3% (1611/3899) had an adjudicated diagnosis of acute heart failure, respectively. The negative predictive value (NPV) of guideline-recommended thresholds for BNP (100 pg/mL) and MR-proANP (120 pmol/L) was 93.6% (95% confidence interval 88.4-96.6%) and 95.6% (92.2-97.6%), respectively, whilst the positive predictive value (PPV) was 68.8% (62.9-74.2%) and 64.8% (56.3-72.5%). Significant heterogeneity in the performance of these thresholds was observed across important subgroups. CoDE-HF was well calibrated with excellent discrimination in those without prior acute heart failure for both BNP and MR-proANP [area under the curve of 0.914 (0.906-0.921) and 0.929 (0.919-0.939), and Brier scores of 0.110 and 0.094, respectively]. CoDE-HF with BNP and MR-proANP identified 30% and 48% as low-probability [NPV of 98.5% (97.1-99.3%) and 98.5% (97.7-99.0%)], and 30% and 28% as high-probability [PPV of 78.6% (70.4-85.0%) and 75.1% (70.9-78.9%)], respectively, and performed consistently across subgroups. CONCLUSION: The diagnostic performance of guideline-recommended BNP and MR-proANP thresholds for acute heart failure varied significantly across patient subgroups. A decision-support tool that combines natriuretic peptides and clinical variables was more accurate and supports more individualized diagnosis. STUDY REGISTRATION: PROSPERO number, CRD42019159407.

Duke Scholars

Published In

Eur Heart J Acute Cardiovasc Care

DOI

EISSN

2048-8734

Publication Date

August 7, 2025

Volume

14

Issue

8

Start / End Page

474 / 488

Location

England

Related Subject Headings

  • Predictive Value of Tests
  • Natriuretic Peptide, Brain
  • Machine Learning
  • Humans
  • Heart Failure
  • Biomarkers
  • Atrial Natriuretic Factor
  • Acute Disease
  • 3201 Cardiovascular medicine and haematology
  • 1102 Cardiorespiratory Medicine and Haematology
 

Citation

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Doudesis, D., Lee, K. K., Anwar, M., Singer, A. J., Hollander, J. E., Chenevier-Gobeaux, C., … CoDE-HF investigators. (2025). Machine learning to optimize use of natriuretic peptides in the diagnosis of acute heart failure. Eur Heart J Acute Cardiovasc Care, 14(8), 474–488. https://doi.org/10.1093/ehjacc/zuaf051
Doudesis, Dimitrios, Kuan Ken Lee, Mohamed Anwar, Adam J. Singer, Judd E. Hollander, Camille Chenevier-Gobeaux, Yann-Erick Claessens, et al. “Machine learning to optimize use of natriuretic peptides in the diagnosis of acute heart failure.Eur Heart J Acute Cardiovasc Care 14, no. 8 (August 7, 2025): 474–88. https://doi.org/10.1093/ehjacc/zuaf051.
Doudesis D, Lee KK, Anwar M, Singer AJ, Hollander JE, Chenevier-Gobeaux C, et al. Machine learning to optimize use of natriuretic peptides in the diagnosis of acute heart failure. Eur Heart J Acute Cardiovasc Care. 2025 Aug 7;14(8):474–88.
Doudesis, Dimitrios, et al. “Machine learning to optimize use of natriuretic peptides in the diagnosis of acute heart failure.Eur Heart J Acute Cardiovasc Care, vol. 14, no. 8, Aug. 2025, pp. 474–88. Pubmed, doi:10.1093/ehjacc/zuaf051.
Doudesis D, Lee KK, Anwar M, Singer AJ, Hollander JE, Chenevier-Gobeaux C, Claessens Y-E, Wussler D, Weil D, Kozhuharov N, Strebel I, Sabti Z, deFilippi C, Seliger S, Mesquita ET, Wiemer JC, Möckel M, Coste J, Jourdain P, Kimiaki K, Yoshimura M, Ibrahim I, Ooi SBS, Kuan WS, Gegenhuber A, Mueller T, Hanon O, Vidal J-S, Cameron P, Lam L, Freedman B, Chung T, Collins SP, Lindsell CJ, Newby DE, Japp AG, Shah ASV, Villacorta H, Richards AM, McMurray JJV, Mueller C, Januzzi JL, Mills NL, CoDE-HF investigators. Machine learning to optimize use of natriuretic peptides in the diagnosis of acute heart failure. Eur Heart J Acute Cardiovasc Care. 2025 Aug 7;14(8):474–488.
Journal cover image

Published In

Eur Heart J Acute Cardiovasc Care

DOI

EISSN

2048-8734

Publication Date

August 7, 2025

Volume

14

Issue

8

Start / End Page

474 / 488

Location

England

Related Subject Headings

  • Predictive Value of Tests
  • Natriuretic Peptide, Brain
  • Machine Learning
  • Humans
  • Heart Failure
  • Biomarkers
  • Atrial Natriuretic Factor
  • Acute Disease
  • 3201 Cardiovascular medicine and haematology
  • 1102 Cardiorespiratory Medicine and Haematology