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

Doudesis, D

; Lee, KK

; Anwar, M; Singer, AJ; Hollander, JE; Chenevier-Gobeaux, C; Claessens, Y; Wussler, D

; Weil, D; Kozhuharov, N; +52 more...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; Cameron, P; Lam, L; Freedman, B

; Chung, T; Collins, SP; Lindsell, CJ; Newby, DE; Japp, AG; Shah, AS

; Villacorta, H; Richards, AM; McMurray, JJ; Mueller, C; Januzzi, JL; Mills, NL; Moe, G; Fernando, C; Gaggin, HK; Bayes-Genis, A; van Kimmenade, RR; Pinto, Y; Rutten, JH; van den Meiracker, AH; Gargani, L; Pugliese, NR; Pemberton, C; Neumaier, M; Behnes, M; Akin, I; Bombelli, M; Grassi, G; Nazerian, P; Albano, G; Bahrmann, P and (2025) Machine learning to optimize use of natriuretic peptides in the diagnosis of acute heart failure. European heart journal. Acute cardiovascular care, 14. pp. 474-488. ISSN 2048-8726 DOI: 10.1093/ehjacc/zuaf051

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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.