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Published on: July 20, 2022
ECG-based machine learning model for AF identification in patients with first ischemic stroke
Chih-Chieh Yu1,2, Yu-Qi Peng3, Chen Lin3
1Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei City.
Insights
A new convolutional neural network (CNN) model can identify atrial fibrillation (AF) in stroke patients using electrocardiograms (ECG) and predict future AF events, aiding early treatment to reduce stroke recurrence.
Area of Science:
- Cardiology
- Neurology
- Artificial Intelligence in Medicine
Background:
- Atrial fibrillation (AF) increases stroke recurrence risk, often requiring oral anticoagulants.
- Detecting AF in stroke patients is challenging.
- Previous studies lack clear benefits of universal anticoagulation for embolic stroke of undetermined source.
Purpose of the Study:
- Develop a convolutional neural network (CNN) model to detect AF from 12-lead sinus-rhythm ECGs in stroke patients.
- Evaluate the model's ability to predict future AF occurrence.
Main Methods:
- Trained a CNN model using ECG data from Taipei Veterans General Hospital.
- Performed external validation on ischemic stroke patients from National Taiwan University Hospital.
- Assessed model performance for AF detection and future AF prediction.
Main Results:
- Achieved AUC of 0.91 (internal) and 0.69 (external) for AF detection.
- Demonstrated 97% sensitivity and negative predictive value for AF detection.
- Identified a high-risk group with a 4.06-fold increased risk of future AF incidence.
Conclusions:
- The CNN model effectively identifies AF in stroke patients via ECG.
- The model predicts future AF events, enabling early anticoagulation.
- This approach may reduce recurrent stroke risk; prospective studies are needed.
Background:
The recurrence rate of strokes associated with atrial fibrillation (AF) can be substantially reduced through the administration of oral anticoagulants. However, previous studies have not demonstrated a clear benefit from the universal application of oral anticoagulants in patients with embolic stroke of undetermined source. Timely detection of AF remains a challenge in patients with stroke.
Aim:
This study aims to develop a convolutional neural network (CNN) model to accurately identify patients with AF using a 12-lead sinus-rhythm electrocardiogram (ECG) recorded around the time of the first ischemic stroke. In addition, this study also evaluates the model's ability to predict future occurrence of AF.
Methods:
A CNN model was trained with ECG data from patients at Taipei Veterans General Hospital. External validation was performed on ischemic stroke patients from National Taiwan University Hospital. The model's performance was assessed for detecting AF at the stroke event and predicting future AF occurrences.
Results:
The model demonstrated an area under curve (AUC) of 0.91 for internal validation and 0.69 for external validation in identifying AF at the stroke event, with sensitivity and negative predictive value both achieving 97%. Kaplan-Meier survival analysis of patients without a prior diagnosis of AF revealed a significant increase in future AF incidence among the high-risk group identified by the model (adjusted hazard ratio: 4.06; 95% confidence interval: 2.74-6.00).
Conclusions:
The CNN model effectively identifies AF in stroke patients using 12-lead ECGs and predicts future AF events, facilitating early anticoagulation therapy and potentially reducing recurrent stroke risk. Further prospective studies are warranted to confirm these findings.
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