Machine learning to optimize the diagnostic performance of natriuretic peptides for acute heart failure across age groups
Year: 2026
Type: article
Abstract: BACKGROUND AND AIMS: N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentrations are influenced by age, which may influence the diagnostic performance of this peptide. Machine learning approaches incorporating NT-proBNP and age as continuous measures may have improved diagnostic performance. METHODS: We pooled individual patient-level data for 10 369 patients [median age 73 years (25th-75th percentile: 59-82)] with suspected acute heart failure across fourteen studies. The diagnostic performance of guideline-recommended NT-proBNP thresholds (uniform rule-out threshold of 300 pg/mL and age-stratified rule-in thresholds of 450, 900, and 1800 pg/mL for patients <50, 50-75, and >75 years, respectively) and the Collaboration for the Diagnosis and Evaluation of Heart Failure (CoDE-HF) machine learning model were evaluated using random effects meta-analysis across age groups. RESULTS: Overall, 43.9% (4549/10 369) of patients had an adjudicated diagnosis of acute heart failure. The negative predictive value (NPV) of the rule-out threshold of 300 pg/mL was lower in older patients [NPV 88.7% (confidence interval (CI) 84.2-92.1%) in patients ≥80 years vs 98.9% (97.6-99.5%) <50 years]. Conversely, the positive predictive value (PPV) of age-stratified rule-in thresholds was lower in younger patients [PPV 62.0% (56.2-67.5%) in those <50 years vs 79.6% (70.7-86.3%) ≥80 years]. CoDE-HF was more accurate than guideline-recommended thresholds across all age groups, with NPV and PPV ranging from 96.4% to 99.5% (93.8-99.8% CIs) and 81.1% to 84.2% (74.7-90.4% CIs), respectively. CONCLUSION: The diagnostic performance of guideline-recommended thresholds of NT-proBNP varies significantly with age. A decision-support tool incorporating NT-proBNP with age as a continuous variable provides a more consistent and accurate approach.
Source: ESC Heart Failure
Authors: Daniel Perez Vicencio, Dimitrios Doudesis, Alexander J F Thurston, Camille Chenevier-Gobeaux, Yann-Erick Claessens, Pedro Lopez Ayala, Maria Belkin, Desiree Wussler, Christopher deFilippi, Stephen Seliger, Gordon Moe, Carlos Fernando, Antoni Bayes-Genis, Yigal Pinto, Hanna K Gaggin, Jan C Wiemer, Martin Möckel, Joost H W Rutten, Luna Gargani, Nicola R Pugliese, Christopher Pemberton, Irwani Ibrahim, Alfons Gegenhuber, Thomas Mueller, Michael Neumaier, Michael Behnes, Ibrahim Akin, Michele Bombelli, Guido Grassi, Peiman Nazerian, Giovanni Albano, Philipp Bahrmann, A Mark Richards, John J V McMurray, Christian Mueller, James L Januzzi, Nicholas L Mills, Kuan Ken Lee
Institutions: British Heart Foundation, University of Edinburgh, Hôpital Cochin, Assistance Publique – Hôpitaux de Paris, Princess Grace Hospital Centre, University Hospital of Basel, University of British Columbia, Vancouver General Hospital, University of Maryland, Baltimore, St. Michael's Hospital, University of Toronto, Hospital Universitari Germans Trias i Pujol, Amsterdam Cardiovascular Sciences, University of Amsterdam, Harvard University, Massachusetts General Hospital, Charité - Universitätsmedizin Berlin, Radboud University Nijmegen, Radboud University Medical Center, University of Pisa, University of Otago, National University Hospital, Heidelberg University, University Medical Centre Mannheim, Medizinische Fakultät Mannheim, University of Milano-Bicocca, Azienda Ospedaliero-Universitaria Careggi, Friedrich-Alexander-Universität Erlangen-Nürnberg, National University Heart Centre Singapore, University of Glasgow
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,850
Funders: National Science Foundation, Abbott Laboratories, Israel Cancer Research Fund, Pfizer, AstraZeneca, Sanofi, Alexion Pharmaceuticals, Alnylam Pharmaceuticals, British Heart Foundation, Chief Scientist Office, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Novo Nordisk, Schweizerische Herzstiftung, Deutsche Herzstiftung, Siemens Healthineers, Abbott Diagnostics, Medical Research Council
Awards: High-sensitivity cardiac troponin beyond the acute coronary syndrome, G189777867, G2670505862, Artificial intelligence to improve the diagnosis of acute cardiovascular conditions, High-sensitivity cardiac troponin I and risk stratification of patients with suspected acute coronary syndrome, Sex-specific differences in spontaneous coronary artery dissection: Biochemical signals, echocardiographic characteristics and long-term outcome, G5277452020, G5460676972, G5845063458, G8843327816
OpenAlex ID: W7118905855