Potential Applications of AI-ECG AF Model
AI-ECG model thresholds can be adjusted to be more specific for patients with low pretest probability such as healthy population. This could help make a more cost-effective strategy to rule in patients for further testing (Figure 1).
To rule out patients with AF, a higher sensitivity is needed such as in patients with cryptogenic stroke. Patients with a higher likelihood of AF could then undergo more extensive monitoring (eg., beyond the recommended 30-day monitoring period) (Figure 1).52
As part of a risk score to predict 5- or 10-year probability of incident AF, which may have utility in prevention trials and screening programs.53 Previously this has been attempted with various risk scores, such as the Framingham AF and CHARGE-AF risk scores.54,55 Although not developed for this purpose, the usefulness of the model as an independent predictor of future AF has been externally validated in a population-based Mayo Clinic study of Ageing.56 In the study, both CHARGE-AF score and AI-ECG AF model had similar performance (C statistic of 0.69 for both). Combining the two resulted in a slight increase in overall performance. Participants with an AI-ECG AF model output of >0.5 had a cumulative incidence of 21.5% at 2 years and 52.2% at 10 years.56
In another recent study, a methodology similar to AI-ECG AF model was used to create an ECG-based algorithm for AF prediction. This model was trained using sinus rhythm ECGs at baseline and a window of interest for ECG was at least 1-3 years before AF diagnosis (compared to within a 30-day window in the Mayo Clinic model). They evaluated the performance of this model using UK Biobank data and showed an overall comparable performance to the CHARGE-AF risk score. There was a modest improvement in model performance when the AI-ECG was added to CHARGE-AF. These studies demonstrate that the ECG holds value in predicting AF (not just detecting concomitant AF) and that the clinical features captured by CHARGE-AF likely explain a lot of the predictive poster of the AI-ECG model.57
As a tool to guide management, for instance, identification of a high-risk subset of patients with ESUS may help determine who may benefit from empirical anticoagulation. This utility is currently planned as a follow up study to the BEAGLE trial.34
It may have utility to guide management in difficult cases when AF is highly suspected but has eluded diagnosis (case report).58