Heart failure & cardiomyopathies
Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling
Authors' conclusion
This externally validated algorithm shows promise to inexpensively screen for LV dysfunction and remodeling in children
Study context
- Population
- Patients ≤18 years without major congenital heart disease with paired ECG-echocardiograms (≤2 days apart)training 46,261 patients
- Participants
- 12,631 / 2,830 / 5,088
- Comparison
- ECG-based deep learning model for greater than mild left ventricular (LV) dysfunction, hypertrophy and dilation
- Internal testingn = 12,631
- Emergency departmentn = 2,830
- External validationn = 5,088
- Outcome
- AUROC and AUPRC for expert-classified greater than mild LV dysfunction, hypertrophy and dilation (individually and as a composite outcome)
Methodological detailsexternal validation, deep learning (convolutional neural network)
- External validation
- Deep learning (convolutional neural network)
Performance
As reported in the abstract
Model performance with quantitative outcome cutoffs
| Test or model | Composite AUROC | Composite area under the precision-recall curve (AUPRC) | LV dysfunction AUROC | LV dysfunction AUPRC | LV hypertrophy AUROC | LV hypertrophy AUPRC | LV dilation AUROC | LV dilation AUPRC | Composite NPV |
|---|---|---|---|---|---|---|---|---|---|
| Internal testing | 0.88 | 0.43 | 0.92 | 0.23 | 0.88 | 0.28 | 0.91 | 0.47 | 99.0% |
| External validation | 0.86 | 0.39 | 0.94 | 0.32 | 0.84 | 0.25 | 0.87 | 0.33 | 99.2% |
Composite AUROC
Internal testing0.88
External validation0.86
Composite area under the precision-recall curve (AUPRC)
Internal testing0.43
External validation0.39
LV dysfunction AUROC
Internal testing0.92
External validation0.94
LV dysfunction AUPRC
Internal testing0.23
External validation0.32
LV hypertrophy AUROC
Internal testing0.88
External validation0.84
LV hypertrophy AUPRC
Internal testing0.28
External validation0.25
LV dilation AUROC
Internal testing0.91
External validation0.87
LV dilation AUPRC
Internal testing0.47
External validation0.33
Composite NPV
Internal testing99.0%
External validation99.2%
Further findings
LV hypertrophy vs pediatric cardiologist expert benchmark
- Model predictions of LV hypertrophy outperformed the pediatric cardiologist expert benchmark
Adding age and sex to the model
- No benefit to model performance
Original publication
Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling. Circulation. Published 5 February 2024. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →