TY - JOUR
T1 - Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm
T2 - a prospective non-randomised interventional trial
AU - Noseworthy, Peter A.
AU - Attia, Zachi I.
AU - Behnken, Emma M.
AU - Giblon, Rachel E.
AU - Bews, Katherine A.
AU - Liu, Sijia
AU - Gosse, Tara A.
AU - Linn, Zachery D.
AU - Deng, Yihong
AU - Yin, Jun
AU - Gersh, Bernard J.
AU - Graff-Radford, Jonathan
AU - Rabinstein, Alejandro A.
AU - Siontis, Konstantinos C.
AU - Friedman, Paul A.
AU - Yao, Xiaoxi
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/10/8
Y1 - 2022/10/8
N2 - 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.
AB - 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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U2 - 10.1016/S0140-6736(22)01637-3
DO - 10.1016/S0140-6736(22)01637-3
M3 - Article
C2 - 36179758
AN - SCOPUS:85139316732
SN - 0140-6736
VL - 400
SP - 1206
EP - 1212
JO - The Lancet
JF - The Lancet
IS - 10359
ER -