Congenital heart diseaseDiagnostic or prognostic study
Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease
- Diagnostic accuracy study
- model development and external validation
Authors' conclusion
AI-ECG shows promise to predict biventricular dysfunction/dilation, which may help inform CMR timing in CHD.
Study context
- Population
- Patients with and without congenital heart disease (CHD) with paired ECG-CMRs (≤30 days apart)
- Participants
- 4,941 vs 746
- Comparison
- Internal testing vs External validation
- Internal testingn = 4,941
- External validationn = 746
- Outcome
- AI-ECG detection of cardiovascular magnetic resonance (CMR)-defined LV dysfunction (EF ≤40%), RV dysfunction (EF ≤35%), LV and RV dilation (end-diastolic volume z-score ≥4), by area under receiver-operating curve (AUROC)
Performance
As reported in the abstract
AUROC
| Test or model | Left ventricular (LV) dysfunction AUC | LV dilation AUC | RV dysfunction AUC | RV dilation AUC |
|---|---|---|---|---|
| Internal testing | 0.87 | 0.86 | 0.88 | 0.81 |
| External validation | 0.89 | 0.83 | 0.82 | 0.80 |
Further findings
Performance in functionally single ventricle (subgroup)
- Model performance lowest in functionally single ventricle patients
Survival in tetralogy of Fallot predicted high risk of ventricular dysfunction
- Lower survival in high-risk-predicted tetralogy of Fallot patients
- P < 0.001
Saliency mapping
- Lateral precordial leads influence all outcome predictions; QRS widening and T-wave inversions high-risk features for RV dysfunction/dilation
Original publication
Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease. J Am Coll Cardiol. Published 27 August 2024. doi:10.1016/j.jacc.2024.05.062
Summarised from the published abstract; every number is checked against it