Deep Learning for Cardiac Overload Estimation - Predicting B-Type Natriuretic Peptide (BNP) Levels From Heart Sounds and Electrocardiogram.

Abstract

Background: B-type natriuretic peptide (BNP) and N-terminal pro-BNP (NT-pro-BNP) are key biomarkers used for heart failure (HF) management. Although traditional auscultation lacks objective evaluation, the SSS01-series phonocardiogram enables rapid recording of heart sounds and ECG. We developed a deep-learning model to estimate plasma BNP levels from these non-invasive dynamic physiological signals, with the aim of validating the model's performance with an external validation dataset and assessing its feasibility for clinical application.

Methods and results: This multicenter study evaluated the estimated BNP (eBNP) model for predicting plasma BNP levels ≥100 pg/mL using 8 s of heart sound and ECG data. Validation was performed on an external validation dataset of 140 patients, achieving an area under the receiver operating characteristic curve (AUROC) of 0.895, with sensitivity and specificity of 84.3% and 82.9%, respectively. Subgroup analysis of patients with body mass index of 18.5-25 (n=127) showed more substantial predictive capability, with an AUROC of 0.959, sensitivity of 92.5%, and specificity of 84.8%.

Conclusions: The eBNP model demonstrated strong potential for non-invasive and rapid HF screening. Its simplicity and objectivity make it ideally suited for point-of-care testing, offering a promising approach for early HF diagnosis and detection monitoring of HF exacerbations. These findings, validated on datasets independent of training, highlight the model's robustness across diverse clinical populations.