AI-Guided ECG Screening Increases Atrial Fibrillation Detection Fivefold in High-Risk Patients
In this prospective interventional trial, an AI algorithm applied to standard sinus-rhythm ECGs stratified 1,003 patients into risk groups for unrecognized atrial fibrillation. Over 30 days of continuous monitoring, atrial fibrillation was detected in 7.6% of AI-identified high-risk patients versus 1.6% of low-risk patients. Compared with propensity-matched usual care controls, AI-guided screening tripled the detection rate in the high-risk group. The results demonstrate that AI-guided targeted screening using existing clinical data can substantially improve atrial fibrillation detection yield.
The original study
Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial.
- Authors
- Noseworthy PA, Attia ZI, Behnken EM, Giblon RE, Bews KA, Liu S, et al.
- Journal
- Lancet (London, England)
- Type
- Clinical Trial, Journal Article, Pragmatic Clinical Trial
- PMID
- 36179758
Original abstract
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. FUNDING: Mayo Clinic Robert D and Patricia E Kern Center for the Science of Health Care Delivery.