[Development of a prediction model for acute heart failure based on B-type natriuretic peptide and multiple clinical indicators in acute dyspnea patients with varying renal function levels].

Abstract

Objective: To investigate the diagnostic value of B-type natriuretic peptide (BNP) for acute heart failure (AHF) in acute dyspnea patients with different renal function statuses and to develop a diagnostic model for AHF by integrating BNP with multiple clinical variables. Methods: A retrospective analysis was conducted on patients presenting with acute dyspnea as the primary or initial symptom who were admitted to the Emergency Department of Peking University First Hospital between January 2019 and June 2023. Demographic characteristics, clinical features, and laboratory parameters were collected as candidate variables. Renal function was assessed using the estimated glomerular filtration rate (eGFR) and classified into different stages. Least absolute shrinkage and selection operator (LASSO) regression was applied to 34 candidate variables to identify variables associated with AHF diagnosis. Receiver operating characteristic (ROC) curve analysis was performed for the selected variables. The diagnostic performance of BNP across different renal function subgroups was further evaluated using ROC analysis. Subsequently, 5 predictive models, including logistic regression, random forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost), were developed based on the selected variables. Model performance was assessed using the area under the curve (AUC) of ROC, calibration curves, and decision curve analysis (DCA). Differences in the AUC across subgroups were compared using the DeLong test. Model interpretability was evaluated using Shapley additive explanations (SHAP). Results: Among the 605 enrolled patients, 236 (39.0%) were diagnosed with AHF and 369 (61.0%) were classified as non-AHF. LASSO regression identified seven variables associated with AHF diagnosis, including history of diuretic use, diabetes mellitus, previous cardiac dysfunction, persistent atrial fibrillation, acute myocardial infarction, eGFR stage, and BNP level. Among the selected variables, BNP demonstrated the highest diagnostic performance for AHF, with an AUC of 0.949 (95%CI: 0.931-0.967), an optimal cutoff value of 488 ng/L, a sensitivity of 80.9%, a specificity of 96.7%, and a Youden index of 0.777. In patients with eGFR≥60 ml·min^-1·(1.73 m²)^-1 and eGFR<60 ml·min^-1·(1.73 m²)^-1, the AUC of BNP for diagnosing AHF were 0.944 (95%CI: 0.902-0.987) and 0.938 (95%CI: 0.913-0.962), respectively, with no significant difference between the 2 groups (P=0.793). The logistic regression, random forest, AdaBoost, LightGBM, and XGBoost models achieved AUC of 0.956 (95%CI: 0.937-0.972), 0.944 (95%CI: 0.925-0.962), 0.940 (95%CI: 0.918-0.960), 0.931 (95%CI: 0.909-0.952), and 0.929 (95%CI: 0.905-0.951), respectively. The logistic regression model demonstrated the best overall discrimination, with a sensitivity of 87.7%, a specificity of 91.6%, and a Youden index of 0.793. Hosmer-Lemeshow testing, calibration curves, and DCA further indicated good calibration and clinical utility of the logistic regression model. SHAP analysis identified BNP as the most influential predictor, with the highest mean absolute SHAP value (4.278). Conclusions: BNP exhibited high diagnostic value for AHF regardless of renal function status. The logistic regression model incorporating BNP and routinely available clinical indicators demonstrated excellent diagnostic performance, calibration, and clinical utility, and may serve as a useful tool for assisting the diagnosis of AHF in emergency settings.