Arrhythmias & electrophysiology
Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation
Authors' conclusion
Deep learning of outpatient sinus rhythm ECGs predicted AF within 31 days in populations with diverse demographics and comorbidities.
Study context
- Population
- Outpatients with 12-lead electrocardiograms (ECGs) in sinus rhythm, 6 US Veterans Affairs (VA) hospital networks and 1 non-VA academic medical center, 1987–2022
- Comparison
- VA sites vs Non-VA site
- VA sitesn = 907,858 ECGs
- Non-VA siten = 72,483 ECGs
- Outcome
- Prediction of atrial fibrillation (AF) by a deep learning model (convolutional neural network) applied to sinus rhythm ECGs
- Follow-up
- Within 31 days
Methodological detailsmulticentre, external validation
- Multicentre
- External validation
Performance
As reported in the abstract
AF within 31 days of a sinus rhythm ECG · 31 days
| Test or model | AUROC | Accuracy | F1 score |
|---|---|---|---|
| VA sites | 0.86 (95% CI, 0.85-0.86) | 0.78 (95% CI, 0.77-0.78) | 0.30 (95% CI, 0.30-0.31) |
| Non-VA site | 0.93 (95% CI, 0.93-0.94) | 0.87 (95% CI, 0.86-0.88) | 0.46 (95% CI, 0.44-0.48) |
AUROC
VA sites0.86 (95% CI, 0.85-0.86)
Non-VA site0.93 (95% CI, 0.93-0.94)
Accuracy
VA sites0.78 (95% CI, 0.77-0.78)
Non-VA site0.87 (95% CI, 0.86-0.88)
F1 score
VA sites0.30 (95% CI, 0.30-0.31)
Non-VA site0.46 (95% CI, 0.44-0.48)
- Well calibrated, Brier score across all sites
- 0.02
Further findings
Number needed to screen among individuals deemed high risk
- Testing sensitivity 25%
- 2.47
- Testing sensitivity 75%
- 11.48
Performance in subgroups (Black, female, age <65 years, CHA2DS2-VASc ≥2)
- Model performance similar in patients who were Black, female, younger than 65 years or with CHA2DS2-VASc scores of 2 or greater
Original publication
Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation. JAMA Cardiol. Published 1 December 2023. doi:10.
Summarised from the published abstract; every number is checked against it