Cited by 257Atrial Fibrillation Management and OutcomesECG Monitoring and AnalysisCardiac electrophysiology and arrhythmias

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

Noseworthy, Peter A, Attia, Zachi I, Behnken, Emma M, Giblon, Rachel E, Bews, Katherine A, Liu, Sijia, Gosse, Tara A, Linn, Zachery D +8 more·2022·The Lancet

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.

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Crossref · 10.1016/s0140-6736(22)01637-3

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References 26 · Cited by 257

Authors
Noseworthy, P. A., Attia, Z. I., Behnken, E. M., Giblon, R. E., Bews, K. A., Liu, S., Gosse, T. A., Linn, Z. D., Deng, Y., Yin, J., Gersh, B. J., Graff-Radford, J., Rabinstein, A. A., Siontis, K. C., Friedman, P. A., & Yao, X.
Affiliations
Mayo Clinic (US)
Corresponding author
Noseworthy, Peter A
Venue
The Lancet
Publisher
Elsevier BV
Published
2022-10
Volume
400
Issue
10359
Pages
1206-1212
ISSN
  • 0099-5355 (ISSN-L)
  • 0140-6736
OpenAlex
W4297231385
Cited by
257
References
26
Citations by year
2026: 65 · 2025: 70 · 2024: 71 · 2023: 46 · 2022: 5
FWCI
33.40
Type
journal article
Language
en
Open access
closed
Locations
  • The Lancet (publishedVersion)
  • PubMed (publishedVersion)
Funders
Mayo Clinic
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
Keywords
atrial fibrillation, artificial intelligence, targeted screening, electrocardiogram, sinus rhythm, stroke risk factors, newly diagnosed atrial fibrillation, risk stratification, atrial fibrillation detection
MeSH terms
Aged, Artificial Intelligence, Atrial Fibrillation, Electrocardiography, Humans, Mass Screening, Prospective Studies

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