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Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial


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.


For this non-randomised interventional trial, we prospectively recruited patients
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, NCT04208971.


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).


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.


Mayo Clinic Robert D and Patricia E Kern Center for the Science of Health Care Delivery.

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