Key result
AI-guided ECG screening links high-risk status to ~5-fold higher detection of incident AF.
Why the study?
Previous atrial fibrillation screening trials highlighted the need for more targeted approaches to identify previously unrecognised atrial fibrillation.
Does an AI-guided targeted screening approach using sinus rhythm ECGs combined with continuous ambulatory monitoring increase the detection of previously unrecognized atrial fibrillation in patients with stroke risk factors?
Does an AI-guided targeted screening approach using sinus rhythm ECGs combined with continuous ambulatory monitoring increase the detection of previously unrecognized atrial fibrillation in patients with stroke risk factors?
An AI algorithm applied to routine sinus rhythm ECGs effectively stratifies patients and significantly increases the diagnostic yield of continuous ambulatory monitoring for detecting unrecognized atrial fibrillation compared to usual care.
Answers are grounded in this paper and cite their sources.
AI-guided sinus rhythm ECG screening may increase AF detection; leaves open whether it improves outcomes in randomized trials.
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 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 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.
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Noseworthy et al. (2022) studied Patients with stroke risk factors and no known atrial fibrillation (n=1,003). AI algorithm-guided targeted screening (high-risk ECG group) with continuous ambulatory heart rhythm monitoring vs. AI algorithm low-risk ECG group (primary analysis) and propensity-score matched real-world usual care controls (secondary analysis) was evaluated on Newly diagnosed atrial fibrillation (OR 4.98, 95% CI 2.11-11.75, p=0.0002).
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