Key result
The authors hypothesized that ECG-AI may be well-suited to discriminate cardioembolism at the time of stroke and provide a low-cost method for implantable loop recorder prioritization.
Why the study?
AF-related cardioembolic strokes are severe and treatable with oral anticoagulation, but optimal AF detection via implantable loop recorders is resource-intensive and risk scores have practical limitations.
Atrial fibrillation (AF) is the most common cause of cardioembolic stroke.Recognition is paramount since AF-related cardioembolic strokes are especially severe and oral anticoagulation (OAC) can prevent recurrent events. 1 Optimal AF detection requires implantable loop recorders (ILR), which are resource-intensive with limited uptake. 1 OAC after stroke without confirmed AF increases bleeding without improving outcomes.Efficient methods are needed to identify AF-related stroke, to guide intensive rhythm monitoring and OAC prescription.Validated AF risk scores, such as Cohorts for Heart and Aging Research in Genomic Epidemiology-AF (CHARGE-AF), 2 can discriminate cardioembolic stroke. 1 However, scores are rarely employed due to cumbersome calculation and potential misclassification.Overlapping risk factors between cardioembolic and non-cardioembolic stroke also limits discriminative value.ECG-AI is a validated deep learning model to estimate AF risk using the 12-lead electrocardiogram (ECG), 3 an inexpensive test routinely performed after stroke.Since ECG-AI provides predictive utility beyond clinical risk, 3 we hypothesized ECG-AI may be well-suited to discriminate cardioembolism at the time of stroke, and could provide a low-cost method for ILR prioritization.
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Khurshid et al. (2024) studied Cardioembolic stroke. ECG-AI was evaluated. The authors hypothesized that ECG-AI may be well-suited to discriminate cardioembolism at the time of stroke and provide a low-cost method for implantable loop recorder prioritization.
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