ArticleJACC. Advances2026
Artificial Intelligence-Enabled ECG for Diastolic Dysfunction in Congenital Heart Disease: A Novel Risk Stratification Tool.
Article in JACC. Advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEchocardiography-based assessment of diastolic function in adult congenital heart disease (ACHD) is challenging owing to complex anatomy and heterogenous physiology.
objectivesThe 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.
methodsIn 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).
resultsThe 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).
conclusionsAI-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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.