TY - JOUR
T1 - Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction
T2 - a pragmatic, randomized clinical trial
AU - Yao, Xiaoxi
AU - Rushlow, David R.
AU - Inselman, Jonathan W.
AU - McCoy, Rozalina G.
AU - Thacher, Thomas D.
AU - Behnken, Emma M.
AU - Bernard, Matthew E.
AU - Rosas, Steven L.
AU - Akfaly, Abdulla
AU - Misra, Artika
AU - Molling, Paul E.
AU - Krien, Joseph S.
AU - Foss, Randy M.
AU - Barry, Barbara A.
AU - Siontis, Konstantinos C.
AU - Kapa, Suraj
AU - Pellikka, Patricia A.
AU - Lopez-Jimenez, Francisco
AU - Attia, Zachi I.
AU - Shah, Nilay D.
AU - Friedman, Paul A.
AU - Noseworthy, Peter A.
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2021/5
Y1 - 2021/5
N2 - We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial (NCT04000087), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults (N = 11,573 intervention; N = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01–1.61), P = 0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08–1.91), P = 0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P = 0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention, P < 0.001). These results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care.
AB - We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial (NCT04000087), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults (N = 11,573 intervention; N = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01–1.61), P = 0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08–1.91), P = 0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P = 0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention, P < 0.001). These results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care.
UR - http://www.scopus.com/inward/record.url?scp=85105166108&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85105166108&partnerID=8YFLogxK
U2 - 10.1038/s41591-021-01335-4
DO - 10.1038/s41591-021-01335-4
M3 - Article
C2 - 33958795
AN - SCOPUS:85105166108
SN - 1078-8956
VL - 27
SP - 815
EP - 819
JO - Nature Medicine
JF - Nature Medicine
IS - 5
ER -