Deep Learning Model Outperforms Guidelines for Heart Failure Prognosis

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medRxiv Published April 24, 2026 Medically reviewed April 24, 2026 Study authors: Tokodi, M.; Kagiyama, N.; Pandey, A.; Nakamura, Y.; Akama, Y.; Takamatsu, S.; Toki, M.; Kitai, T.; O… DOI ↗ By Dr. Amelia Tan, PhD · Internal Medicine & Chronic Disease

Key Takeaway

Consider that a deep learning model using routine echo variables may improve prognostic and diagnostic accuracy for heart failure compared to current guidelines.

This cohort study evaluated a deep learning model based on routinely acquired echocardiographic variables for assessing diastolic function and left ventricular filling pressures. The primary analysis included 5450 participants from the Atherosclerosis Risk in Communities (ARIC) cohort, with additional invasive hemodynamic validation cohorts from the United States (n=83) and Japan (n=130).

In the ARIC cohort, the deep learning model demonstrated superior prognostic performance compared with the 2016 and 2025 ASE/EACVI guidelines (C-index: 0.676 vs. 0.638 and 0.602, both p<0.001). Among participants with preserved ejection fraction, the model also outperformed both guidelines (C-index: 0.660 vs. 0.628 and 0.590, both p<0.001) and the H2FPEF score (C-index: 0.660 vs. 0.607, p<0.001).

In the US hemodynamic validation cohort, the deep learning model showed higher diagnostic performance than the 2025 guidelines (AUC: 0.879 vs. 0.822, p=0.041) but similar performance to the 2016 guidelines (AUC: 0.879 vs. 0.812, p=0.138). In the Japanese validation cohort, the model outperformed both the 2016 guidelines (AUC: 0.816 vs. 0.634, p<0.05) and the 2025 guidelines (AUC: 0.816 vs. 0.694, p<0.05).

Safety and tolerability were not reported. Limitations include the observational nature of the study and the need for external validation in diverse clinical settings. The model potentially offers a scalable alternative for assessing diastolic function, but further research is needed before clinical implementation.

Study Details

Study typeCohort

Sample sizen = 5,450

EvidenceLevel 3

PublishedApr 2026

View Original Abstract ↓

Backgound: Accurate assessment of diastolic function and left ventricular (LV) filling pressure is central to heart failure diagnosis and risk stratification. Contemporary guideline algorithms rely on complex parameters that are not consistently available in routine clinical practice. Objective: To compare the diagnostic and prognostic performance of the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) and 2025 ASE guidelines with a deep learning model based on routinely acquired echocardiographic variables. Methods: This study evaluated the guideline-based algorithms and a deep learning model in participants from the Atherosclerosis Risk in Communities (ARIC) cohort (n=5450) for prognostication and two invasive hemodynamic validation cohorts from the United States (n=83) and Japan (n=130) for detection of elevated left ventricular filling pressure. Results: In the ARIC cohort, the deep learning model demonstrated superior prognostic performance compared with the 2016 and 2025 guidelines (C-index: 0.676 vs. 0.638 and 0.602, respectively; both p<0.001). Similar findings were observed among participants with preserved ejection fraction (C-index: 0.660 vs. 0.628 and 0.590; both p<0.001), with improved performance compared with the H2FPEF score (C-index: 0.660 vs. 0.607; p<0.001). In the US hemodynamic validation cohort, the deep learning model showed higher diagnostic performance than the 2025 guidelines (AUC: 0.879 vs. 0.822; p=0.041) and similar performance compared with the 2016 guidelines (AUC: 0.879 vs. 0.812; p=0.138). In the Japanese hemodynamic validation cohort, the deep learning model outperformed both guidelines (AUC: 0.816 vs. 0.634 and 0.694; both p<0.05). Conclusions: A deep learning model leveraging routinely available echocardiographic parameters demonstrated improved diagnostic and prognostic performance compared with contemporary guideline-based approaches, potentially offering a scalable alternative for assessing diastolic function and left ventricular filling pressures.