Interpretable laboratory-data model for risk stratification of elevated NT-proBNP and its deployment in diagnostic support middleware

BackgroundHeart failure (HF) is a growing global burden. Although N-terminal pro-B-type natriuretic peptide (NT-proBNP) guides diagnosis, assay cost and analyzer availability limit routine use. Routine laboratory data may offer a low-cost triage alternative.MethodsWe developed and validated an interpretable decision tree to stratify the risk of elevated NT-proBNP >300 pg/mL and assessed deployment in a diagnostic support system (DSS). We analyzed 19,889 encounters at Gifu University Hospital (Aug 2022-May 2024). All 20 candidate predictors were included without prior feature selection to capture non-linear associations. Hyperparameters were tuned by 10-fold cross-validation. Final classification used a fixed decision rule optimized for high sensitivity (≥0.90 in training) to support effective triage. Performance comprised AUROC (DeLong 95% CIs) and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy (Wilson 95% CIs) on an internal hold-out set (n = 3978) and a temporal external cohort (n = 14,903; Jun 2024-Jun 2025). Analyses were complete-case with no imputation.ResultsThe decision tree inherently utilized clinically relevant predictors including serum albumin, eGFR, and age. Internal test performance: AUROC 0.804 (0.791-0.818); sensitivity 0.879 (0.863-0.893); specificity 0.505 (0.484-0.526); and accuracy 0.674 (0.660-0.689). External performance within the DSS: AUROC 0.806 (0.799-0.813); sensitivity 0.882 (0.874-0.890); specificity 0.518 (0.508-0.529); and NPV 0.852 (0.842-0.861). Calibration and decision-curve analysis supported clinical utility.ConclusionsAn interpretable tree built from routine laboratories detects clinically relevant NT-proBNP elevation with high sensitivity and performs robustly after deployment. This scalable, low-cost approach could enable risk-directed triage and more efficient resource allocation.