Abstract 4369685: Multi-Task Deep Learning for Noninvasive Rapid BNP and NT-proBNP Estimation and Classification

Background: Natriuretic peptides such as BNP and NT-proBNP remain cornerstone biomarkers for diagnosing and management of heart failure (HF). However, their measurement is done mainly when HF is suspected or requires monitoring, posing a missed opportunity for early HF detection and proactive intervention in at-risk patients. This gap highlights the need for accessible, non-invasive tools that can support earlier screening and continuous assessment. Aim: To develop and validate multi-task deep learning models that simultaneously estimate natriuretic peptide values and classify clinically relevant strata directly from raw 12-lead and Lead I ECG waveforms. Methods: Two distinct one-dimensional residual convolutional neural network architectures were separately trained on 12-lead and Lead I ECG inputs, using paired ECG and BNP measurements within a ±2-hour window to ensure rigorous label alignment. The models performed both BNP regression and classification into clinically meaningful low (<100 pg/mL) and elevated (>500 pg/mL) strata. Internal validation used a holdout cohort from Wake Forest Baptist Health (WF) (Winston-Salem, NC), while external validation employed an independent cohort from the University of Tennessee Health Science Center (UTHSC) (Memphis, TN), which included NT-proBNP data and specific clinical thresholds (low <125 pg/mL and elevated >300 pg/mL). Performance was evaluated using AUC, PPV, and NPV for classification and Spearman correlation for regression. Results: The internal dataset (WF) included 102,311 paired ECG–BNP samples from 54,526 patients, with a holdout set of 10,264 samples for validation. The external cohort (UTHSC) comprised 88,179 same day ECG–NT-proBNP pairs. The multi-task models demonstrated strong classification accuracy and regression correlation across both datasets. Specifically, the 12-lead model achieved AUCs of 0.88–0.89 and Spearman correlations of 0.75 in internal BNP strata, with similar performance in the external NT-proBNP cohort (AUC 0.88, Spearman 0.75–0.76). The Lead I model showed slightly lower but robust performance. Detailed metrics with 95% confidence intervals are summarized in Table 1. Conclusion: This multi-task models provide accurate, simultaneous estimation and classification of natriuretic peptides from ECGs. This approach offers a rapid, non-invasive tool for HF biomarker assessment that may improve clinical workflows, enable ambulatory monitoring, and enhance timely clinical decision-making.