Artificial Intelligence–Enabled ECG for Diastolic Dysfunction in Congenital Heart Disease

Q: What does this research mean for the field?

An AI-enabled ECG model for diastolic dysfunction grading correlates with echocardiographic and invasive filling pressures and independently predicts mortality in adults with congenital heart disease. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Donnchadh O’Sullivan; Malini Madhavan; Sahar Samimi; William R. Miranda; Zachi I. Attia; Heidi M. Connolly; Katia Bravo‐Jaimes; C. Charles Jain; Paul A Friedman; Christopher Aye Egbe; Francisco López-Jiménez; Jae K. Oh; Luke J. Burchill

doi:10.1016/j.jacadv.2025.102413

December 16, 2025JACC AdvancesOpen Access

Artificial Intelligence–Enabled ECG for Diastolic Dysfunction in Congenital Heart Disease

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Why the study?

Echocardiography-based assessment of diastolic function in adult congenital heart disease is challenging due to complex anatomy and heterogenous physiology.

Does an AI-enabled ECG model accurately grade diastolic dysfunction and predict mortality in patients with adult congenital heart disease?

Structured PICO

Does an AI-enabled ECG model accurately grade diastolic dysfunction and predict mortality in patients with adult congenital heart disease?

P

Population

6,741 patients with adult congenital heart disease (ACHD) from the Mayo Clinic ACHD Registry, median age 37 years, 49% female.

I

Intervention

Artificial intelligence (AI)-enabled electrocardiogram (ECG) model assigning a diastolic grade (0-3) to the earliest ECG within 12 months of the index visit.

O

Outcome

Correlation of AI-ECG diastolic grade with echocardiographic measures, invasive hemodynamics, and mortalitysurrogate

An AI-enabled ECG model can noninvasively grade diastolic dysfunction, correlating with invasive hemodynamics and independently predicting mortality in adult congenital heart disease.

Implication

AI-ECG may aid diastolic grading in ACHD; hypothesis-generating and requires prospective validation before adoption.

Abstract

BACKGROUND: Echocardiography-based assessment of diastolic function in adult congenital heart disease (ACHD) is challenging owing to complex anatomy and heterogenous physiology. OBJECTIVES: The objectives of the study was to evaluate an artificial intelligence (AI)-enabled electrocardiogram (ECG) (AI-ECG) model for grading diastolic dysfunction in patients with ACHD and assess its correlation with echocardiographic, invasive hemodynamic, and clinical outcomes. METHODS: In this single-center retrospective study, we analyzed 6,741 patients from the Mayo Clinic ACHD Registry (median age 37 years, 49% female) followed from 2000 to 2023. The median follow-up was 10 (5-15) years. Using a validated deep neural network (trained on 98,736 ECG-echocardiogram pairs), we assigned an AI-ECG diastolic grade (0-3) to the earliest ECG within 12 months of the index visit. We evaluated associations with echocardiography, hemodynamics, and mortality using nonparametric tests, correlation, Kaplan-Meier curves, Cox regression, and model performance for detecting elevated pulmonary artery wedge pressure (PAWP). RESULTS: The AI-ECG classified diastolic function as grade 0 in 65.8%, grade 1 in 4.0%, grade 2 in 19.7%, and grade 3 in 10.5%. Higher grades were associated with older age, greater CHD complexity, and more comorbidities including heart failure (6.7% vs 24.8%), diabetes, and cirrhosis (all P < 0.001). N-terminal pro-B-type natriuretic peptide rose with each grade (129 [60-304] to 763 [311-1,915] pg/mL; P < 0.001). AI-ECG pressure estimates correlated with left atrial strain (ρ = -0.52) and right ventricular strain (ρ = -0.50). Invasive hemodynamics followed similar patterns; right atrial pressure rose from 8 (6-11) to 13 (10-18) mm Hg, and wedge pressure from 11 (8-14) to 16 (12-21) mm Hg (P < 0.001). Survival differed by grade (log-rank P < 0.0001); grades 2 and 3 independently predicted mortality (HR: 1.38; 95% CI: 1.09-1.75; HR: 1.63; 95% CI: 1.27-2.08). The model discriminated pulmonary artery wedge pressure ≥20 mm Hg with an area under the receiver operating characteristic curve of 0.74 (95% CI: 0.70-0.78). CONCLUSIONS: AI-ECG diastolic grading correlates with echocardiographic and invasive measures of cardiac filling pressures and independently predicts mortality in ACHD. These findings support its utility as a scalable, noninvasive risk stratification tool.