The Lead Episode 40: A Discussion of Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation-JAMA Cardiology PDF

JAMA Cardiology PDF

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A study published in JAMA Cardiology investigated the use of deep learning models applied to electrocardiograms (ECGs) to predict atrial fibrillation (AF). The study analyzed ECGs from a diverse patient population and found that the deep learning model had good accuracy and an area under the curve (AUC) of 0.86 in predicting AF within 31 days. It also performed well in patients of different demographics and comorbidities. The findings suggest that deep learning of outpatient ECGs could be beneficial for identifying patients at high risk of AF and implementing monitoring programs to prevent adverse events. However, further research is needed to validate this approach and explore its potential in clinical practice.<br /><br />The study developed a deep learning model using convolutional neural networks to analyze ECGs and predict AF occurrence within 31 days. The model was trained and tested on a large population from multiple hospital networks. The model outperformed traditional clinical risk factors and showed robust performance across different patient subgroups, including women and Black patients. It was also able to predict new-onset AF within a longer 1-year time horizon.<br /><br />The researchers suggest that deep learning analysis of ECGs acquired during routine clinical practice could be a non-invasive method for identifying patients at high risk for AF. This could lead to further evaluation and monitoring using additional methods. Implementing a screening program using deep learning algorithms in a large population, such as the Veterans Affairs (VA) system, could be effective due to the population's high pretest probability of AF.<br /><br />However, the study has limitations as it was retrospective and the population may have a higher prevalence of AF, which could bias the results. Prospective studies are needed to confirm the performance of the model and its impact on patient outcomes.<br /><br />In conclusion, the study demonstrates that deep learning algorithms can effectively predict AF using 12-lead ECGs in patients with sinus rhythm. This approach could be implemented into existing workflows without requiring significant additional resources. However, further research and validation are needed to determine the clinical utility of this method in AF screening and prevention.

Keywords

deep learning

electrocardiograms

atrial fibrillation

accuracy

area under the curve

patient population

demographics

comorbidities

monitoring programs

convolutional neural networks