Heart failure & cardiomyopathies
Machine learning to optimize the diagnostic performance of natriuretic peptides for acute heart failure across age groups
Authors' 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.
Study context
- Population
- Patients with suspected acute heart failurefourteen studies, median age 73 years (25th-75th percentile: 59–82)
- Participants
- 10,369 patients
- Comparison
- Diagnostic performance of guideline-recommended NT-proBNP thresholds and the Collaboration for the Diagnosis and Evaluation of Heart Failure (CoDE-HF) machine learning model across age groups
- Outcome
- Adjudicated diagnosis of acute heart failure
Methodological detailsmachine learning
- Machine learning
Pooled result
As reported in the abstract
Patients with adjudicated diagnosis of acute heart failure
43.9%
4549/10 369
Further pooled results
- CoDE-HF diagnostic performance across all age groups
- 96.4% to 99.5% (93.8-99.8% CIs) · NPV · PPV 81.1% to 84.2% (74.7-90.4% CIs)
NPV
Patients ≥80 years
88.7%(CI 84.2–92.1%)
<50 years
98.9%(CI 97.6–99.5%)
- Lower in older patients
PPV
<50 years
62.0%(CI 56.2–67.5%)
≥80 years
79.6%(CI 70.7–86.3%)
- Lower in younger patients
CoDE-HF diagnostic performance across all age groups
- CoDE-HF was more accurate than guideline-recommended thresholds across all age groups
Original publication
Machine learning to optimize the diagnostic performance of natriuretic peptides for acute heart failure across age groups. ESC Heart Fail. Published 8 January 2026. doi:10.
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