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