Background
Previous atrial fibrillation screening trials have highlighted the need for more targeted
approaches. We did a pragmatic study to evaluate the effectiveness of an artificial
intelligence (AI) algorithm-guided targeted screening approach for identifying previously
unrecognised atrial fibrillation.
Methods
with stroke risk factors but with no known atrial fibrillation who had an electrocardiogram
(ECG) done in routine practice. Participants wore a continuous ambulatory heart rhythm
monitor for up to 30 days, with the data transmitted in near real time through a cellular
connection. The AI algorithm was applied to the ECGs to divide patients into high-risk
or low-risk groups. The primary outcome was newly diagnosed atrial fibrillation. In
a secondary analysis, trial participants were propensity-score matched (1:1) to individuals
from the eligible but unenrolled population who served as real-world controls. This
study is registered with ClinicalTrials.gov, NCT04208971.
Findings
1003 patients with a mean age of 74 years (SD 8·8) from 40 US states completed the
study. Over a mean 22·3 days of continuous monitoring, atrial fibrillation was detected
in six (1·6%) of 370 patients with low risk and 48 (7·6%) of 633 with high risk (odds
ratio 4·98, 95% CI 2·11–11·75, p=0·0002). Compared with usual care, AI-guided screening
was associated with increased detection of atrial fibrillation (high-risk group: 3·6%
[95% CI 2·3–5·4] with usual care vs 10·6% [8·3–13·2] with AI-guided screening, p<0·0001; low-risk group: 0·9% vs 2·4%, p=0·12) over a median follow-up of 9·9 months (IQR 7·1–11·0).
Interpretation
An AI-guided targeted screening approach that leverages existing clinical data increased
the yield for atrial fibrillation detection and could improve the effectiveness of
atrial fibrillation screening.
Funding
Mayo Clinic Robert D and Patricia E Kern Center for the Science of Health Care Delivery.
