Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial
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 ( NCT04000...
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| Published in | Nature medicine Vol. 27; no. 5; pp. 815 - 819 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.05.2021
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1078-8956 1546-170X 1546-170X |
| DOI | 10.1038/s41591-021-01335-4 |
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| Abstract | 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.
In a pragmatic, cluster-randomized clinical trial, use of an AI algorithm for interpretation of electrocardiograms in primary care practices increased the frequency at which impaired heart function was diagnosed. |
|---|---|
| AbstractList | 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.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. 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.In a pragmatic, cluster-randomized clinical trial, use of an AI algorithm for interpretation of electrocardiograms in primary care practices increased the frequency at which impaired heart function was diagnosed. 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. In a pragmatic, cluster-randomized clinical trial, use of an AI algorithm for interpretation of electrocardiograms in primary care practices increased the frequency at which impaired heart function was diagnosed. 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 ([less than or equal to]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. 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 ([less than or equal to]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. In a pragmatic, cluster-randomized clinical trial, use of an AI algorithm for interpretation of electrocardiograms in primary care practices increased the frequency at which impaired heart function was diagnosed. 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. |
| Audience | Academic |
| Author | Molling, Paul E. Friedman, Paul A. Thacher, Thomas D. McCoy, Rozalina G. Bernard, Matthew E. Siontis, Konstantinos C. Barry, Barbara A. Attia, Zachi I. Misra, Artika Kapa, Suraj Rosas, Steven L. Lopez-Jimenez, Francisco Inselman, Jonathan W. Yao, Xiaoxi Behnken, Emma M. Akfaly, Abdulla Noseworthy, Peter A. Krien, Joseph S. Foss, Randy M. Rushlow, David R. Shah, Nilay D. Pellikka, Patricia A. |
| Author_xml | – sequence: 1 givenname: Xiaoxi orcidid: 0000-0001-9906-7106 surname: Yao fullname: Yao, Xiaoxi email: yao.xiaoxi@mayo.edu organization: Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Department of Cardiovascular Medicine, Mayo Clinic – sequence: 2 givenname: David R. surname: Rushlow fullname: Rushlow, David R. organization: Department of Family Medicine, Mayo Clinic – sequence: 3 givenname: Jonathan W. surname: Inselman fullname: Inselman, Jonathan W. organization: Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic – sequence: 4 givenname: Rozalina G. surname: McCoy fullname: McCoy, Rozalina G. organization: Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Division of Community Internal Medicine, Department of Medicine, Mayo Clinic – sequence: 5 givenname: Thomas D. orcidid: 0000-0002-7644-8173 surname: Thacher fullname: Thacher, Thomas D. organization: Department of Family Medicine, Mayo Clinic – sequence: 6 givenname: Emma M. surname: Behnken fullname: Behnken, Emma M. organization: Knowledge and Evaluation Research Unit, Mayo Clinic – sequence: 7 givenname: Matthew E. surname: Bernard fullname: Bernard, Matthew E. organization: Department of Family Medicine, Mayo Clinic – sequence: 8 givenname: Steven L. surname: Rosas fullname: Rosas, Steven L. organization: Department of Family Medicine, Mayo Clinic Health System – sequence: 9 givenname: Abdulla surname: Akfaly fullname: Akfaly, Abdulla organization: Department of Community Internal Medicine, Mayo Clinic Health System – sequence: 10 givenname: Artika surname: Misra fullname: Misra, Artika organization: Department of Family Medicine, Mayo Clinic Health System – sequence: 11 givenname: Paul E. surname: Molling fullname: Molling, Paul E. organization: Department of Family Medicine, Mayo Clinic Health System – sequence: 12 givenname: Joseph S. surname: Krien fullname: Krien, Joseph S. organization: Department of Family Medicine, Mayo Clinic Health System – sequence: 13 givenname: Randy M. orcidid: 0000-0003-3898-7317 surname: Foss fullname: Foss, Randy M. organization: Department of Family Medicine, Mayo Clinic Health System – sequence: 14 givenname: Barbara A. surname: Barry fullname: Barry, Barbara A. organization: Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic – sequence: 15 givenname: Konstantinos C. surname: Siontis fullname: Siontis, Konstantinos C. organization: Department of Cardiovascular Medicine, Mayo Clinic – sequence: 16 givenname: Suraj surname: Kapa fullname: Kapa, Suraj organization: Department of Cardiovascular Medicine, Mayo Clinic – sequence: 17 givenname: Patricia A. orcidid: 0000-0001-6800-3521 surname: Pellikka fullname: Pellikka, Patricia A. organization: Department of Cardiovascular Medicine, Mayo Clinic – sequence: 18 givenname: Francisco surname: Lopez-Jimenez fullname: Lopez-Jimenez, Francisco organization: Department of Cardiovascular Medicine, Mayo Clinic – sequence: 19 givenname: Zachi I. orcidid: 0000-0002-9706-7900 surname: Attia fullname: Attia, Zachi I. organization: Department of Cardiovascular Medicine, Mayo Clinic – sequence: 20 givenname: Nilay D. surname: Shah fullname: Shah, Nilay D. organization: Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic – sequence: 21 givenname: Paul A. orcidid: 0000-0001-5052-2948 surname: Friedman fullname: Friedman, Paul A. organization: Department of Cardiovascular Medicine, Mayo Clinic – sequence: 22 givenname: Peter A. orcidid: 0000-0002-4308-0456 surname: Noseworthy fullname: Noseworthy, Peter A. organization: Department of Cardiovascular Medicine, Mayo Clinic |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33958795$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1056/NEJM199209033271003 10.1136/bmj.312.7025.222 10.1161/01.CIR.0000130845.38133.8F 10.1016/0002-9149(95)80023-L 10.1038/s41591-018-0240-2 10.1001/jama.289.2.194 10.1056/NEJMp1915448 10.1016/S0140-6736(97)03033-X 10.1136/bmj.318.7180.368 10.1016/j.jacc.2013.05.019 10.7326/0003-4819-138-11-200306030-00012 10.1136/heart.84.4.440 10.1136/bmj.320.7229.220 10.1016/j.dib.2019.104894 10.1093/eurheartj/ehl406 10.1111/j.1751-7133.2008.08283.x 10.1161/01.CIR.0000085166.44904.79 10.1016/S0140-6736(03)13501-5 10.1136/bmj.320.7240.985 10.1111/jce.13889 10.1038/s41746-019-0208-8 10.1038/s41591-020-1034-x 10.1016/j.ahj.2019.10.007 |
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| References_xml | – volume: 327 start-page: 685 year: 1992 end-page: 691 ident: CR7 article-title: Effect of enalapril on mortality and the development of heart failure in asymptomatic patients with reduced left ventricular ejection fractions publication-title: New Engl. J. Med. doi: 10.1056/NEJM199209033271003 – volume: 312 start-page: 222 year: 1996 ident: CR16 article-title: Value of the electrocardiogram in identifying heart failure due to left ventricular systolic dysfunction publication-title: Br. Med. J. doi: 10.1136/bmj.312.7025.222 – volume: 109 start-page: 3176 year: 2004 end-page: 3181 ident: CR13 article-title: Plasma brain natriuretic peptide to detect preclinical ventricular systolic or diastolic dysfunction: a community-based study publication-title: Circulation doi: 10.1161/01.CIR.0000130845.38133.8F – volume: 75 start-page: 220 year: 1995 end-page: 223 ident: CR15 article-title: The utility of clinical, electrocardiographic, and roentgenographic variables in the prediction of left ventricular function publication-title: Am. J. Cardiol. doi: 10.1016/0002-9149(95)80023-L – volume: 25 start-page: 70 year: 2019 end-page: 74 ident: CR17 article-title: Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram publication-title: Nat. Med. doi: 10.1038/s41591-018-0240-2 – volume: 289 start-page: 194 year: 2003 end-page: 202 ident: CR3 article-title: Burden of systolic and diastolic ventricular dysfunction in the community: appreciating the scope of the heart failure epidemic publication-title: JAMA doi: 10.1001/jama.289.2.194 – volume: 8 start-page: 13 year: 2016 end-page: 22 ident: CR19 article-title: Toward a learning health-care system—knowledge delivery at the point of care empowered by big data and NLP publication-title: Biomed. Inf. Insights – volume: 382 start-page: 1488 year: 2020 end-page: 1491 ident: CR20 article-title: Evidence from pragmatic trials during routine care—slouching toward a learning health system publication-title: New Engl. J. Med. doi: 10.1056/NEJMp1915448 – volume: 350 start-page: 829 year: 1997 end-page: 833 ident: CR2 article-title: Symptomatic and asymptomatic left-ventricular systolic dysfunction in an urban population publication-title: Lancet doi: 10.1016/S0140-6736(97)03033-X – volume: 318 start-page: 368 year: 1999 end-page: 372 ident: CR5 article-title: Prevalence and clinical characteristics of left ventricular dysfunction among elderly patients in general practice setting: cross sectional survey publication-title: Br. Med. J. doi: 10.1136/bmj.318.7180.368 – volume: 62 start-page: e147 year: 2013 end-page: e239 ident: CR8 article-title: 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines publication-title: J. Am. Coll. Cardiol. doi: 10.1016/j.jacc.2013.05.019 – volume: 138 start-page: 907 year: 2003 end-page: 916 ident: CR9 article-title: The epidemiology of ‘asymptomatic’ left ventricular systolic dysfunction: implications for screening publication-title: Ann. Intern. Med. doi: 10.7326/0003-4819-138-11-200306030-00012 – volume: 84 start-page: 440 year: 2000 end-page: 441 ident: CR10 article-title: Failure of plasma brain natriuretic peptide to identify left ventricular systolic dysfunction in the community publication-title: Heart doi: 10.1136/heart.84.4.440 – volume: 320 start-page: 220 year: 2000 end-page: 224 ident: CR14 article-title: Risk assessment of left ventricular systolic dysfunction in primary care: cross sectional study evaluating a range of diagnostic tests publication-title: Br. Med. J. doi: 10.1136/bmj.320.7229.220 – volume: 28 start-page: 104894 year: 2019 ident: CR23 article-title: Clinical trial design data for electrocardiogram artificial intelligence-guided screening for low ejection fraction (EAGLE) publication-title: Data Brief doi: 10.1016/j.dib.2019.104894 – volume: 27 start-page: 3004 year: 2006 end-page: 3010 ident: CR12 article-title: Plasma pro-B-type natriuretic peptide in the general population: screening for left ventricular hypertrophy and systolic dysfunction publication-title: Eur. Heart J. doi: 10.1093/eurheartj/ehl406 – volume: 14 start-page: 5 year: 2008 end-page: 8 ident: CR4 article-title: Screening for asymptomatic left ventricular dysfunction using B-type natriuretic peptide publication-title: Congest. Heart Fail. doi: 10.1111/j.1751-7133.2008.08283.x – volume: 108 start-page: 977 year: 2003 end-page: 982 ident: CR1 article-title: Natural history of asymptomatic left ventricular systolic dysfunction in the community publication-title: Circulation doi: 10.1161/01.CIR.0000085166.44904.79 – volume: 361 start-page: 1843 year: 2003 end-page: 1848 ident: CR6 article-title: Effect of enalapril on 12-year survival and life expectancy in patients with left ventricular systolic dysfunction: a follow-up study publication-title: Lancet doi: 10.1016/S0140-6736(03)13501-5 – volume: 320 start-page: 985 year: 2000 end-page: 986 ident: CR11 article-title: Measuring brain natriuretic peptide in suspected left ventricular systolic dysfunction in general practice: cross-sectional study publication-title: Br. Med. J. doi: 10.1136/bmj.320.7240.985 – volume: 30 start-page: 668 year: 2019 end-page: 674 ident: CR18 article-title: Prospective validation of a deep learning ECG algorithm for the detection of left ventricular systolic dysfunction publication-title: J. Cardiovasc. Electrophysiol. doi: 10.1111/jce.13889 – volume: 2 year: 2019 ident: CR21 article-title: Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation publication-title: NPJ Digit. Med. doi: 10.1038/s41746-019-0208-8 – volume: 26 start-page: 1364 year: 2020 end-page: 1374 ident: CR24 article-title: Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension publication-title: Nat. Med. doi: 10.1038/s41591-020-1034-x – volume: 219 start-page: 31 year: 2019 end-page: 36 ident: CR22 article-title: ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): rationale and design of a pragmatic cluster randomized trial publication-title: Am. Heart J. doi: 10.1016/j.ahj.2019.10.007 – volume: 320 start-page: 985 year: 2000 ident: 1335_CR11 publication-title: Br. Med. J. doi: 10.1136/bmj.320.7240.985 – volume: 312 start-page: 222 year: 1996 ident: 1335_CR16 publication-title: Br. Med. J. doi: 10.1136/bmj.312.7025.222 – volume: 62 start-page: e147 year: 2013 ident: 1335_CR8 publication-title: J. Am. Coll. Cardiol. doi: 10.1016/j.jacc.2013.05.019 – volume: 219 start-page: 31 year: 2019 ident: 1335_CR22 publication-title: Am. Heart J. doi: 10.1016/j.ahj.2019.10.007 – volume: 26 start-page: 1364 year: 2020 ident: 1335_CR24 publication-title: Nat. Med. doi: 10.1038/s41591-020-1034-x – volume: 318 start-page: 368 year: 1999 ident: 1335_CR5 publication-title: Br. Med. J. doi: 10.1136/bmj.318.7180.368 – volume: 361 start-page: 1843 year: 2003 ident: 1335_CR6 publication-title: Lancet doi: 10.1016/S0140-6736(03)13501-5 – volume: 138 start-page: 907 year: 2003 ident: 1335_CR9 publication-title: Ann. Intern. Med. doi: 10.7326/0003-4819-138-11-200306030-00012 – volume: 382 start-page: 1488 year: 2020 ident: 1335_CR20 publication-title: New Engl. J. Med. doi: 10.1056/NEJMp1915448 – volume: 28 start-page: 104894 year: 2019 ident: 1335_CR23 publication-title: Data Brief doi: 10.1016/j.dib.2019.104894 – volume: 30 start-page: 668 year: 2019 ident: 1335_CR18 publication-title: J. Cardiovasc. Electrophysiol. doi: 10.1111/jce.13889 – volume: 320 start-page: 220 year: 2000 ident: 1335_CR14 publication-title: Br. Med. J. doi: 10.1136/bmj.320.7229.220 – volume: 8 start-page: 13 year: 2016 ident: 1335_CR19 publication-title: Biomed. Inf. Insights – volume: 27 start-page: 3004 year: 2006 ident: 1335_CR12 publication-title: Eur. Heart J. doi: 10.1093/eurheartj/ehl406 – volume: 75 start-page: 220 year: 1995 ident: 1335_CR15 publication-title: Am. J. Cardiol. doi: 10.1016/0002-9149(95)80023-L – volume: 14 start-page: 5 year: 2008 ident: 1335_CR4 publication-title: Congest. Heart Fail. doi: 10.1111/j.1751-7133.2008.08283.x – volume: 109 start-page: 3176 year: 2004 ident: 1335_CR13 publication-title: Circulation doi: 10.1161/01.CIR.0000130845.38133.8F – volume: 25 start-page: 70 year: 2019 ident: 1335_CR17 publication-title: Nat. Med. doi: 10.1038/s41591-018-0240-2 – volume: 2 year: 2019 ident: 1335_CR21 publication-title: NPJ Digit. Med. doi: 10.1038/s41746-019-0208-8 – volume: 84 start-page: 440 year: 2000 ident: 1335_CR10 publication-title: Heart doi: 10.1136/heart.84.4.440 – volume: 108 start-page: 977 year: 2003 ident: 1335_CR1 publication-title: Circulation doi: 10.1161/01.CIR.0000085166.44904.79 – volume: 350 start-page: 829 year: 1997 ident: 1335_CR2 publication-title: Lancet doi: 10.1016/S0140-6736(97)03033-X – volume: 327 start-page: 685 year: 1992 ident: 1335_CR7 publication-title: New Engl. J. Med. doi: 10.1056/NEJM199209033271003 – volume: 289 start-page: 194 year: 2003 ident: 1335_CR3 publication-title: JAMA doi: 10.1001/jama.289.2.194 – reference: 35590216 - Med (N Y). 2021 Jul 9;2(7):791-793 |
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| SubjectTerms | 692/699/75/230 692/700/228 Adolescent Adult Aged Algorithms Artificial Intelligence Biomedical and Life Sciences Biomedicine Cancer Research Clinical trials Clusters Congestive heart failure Decision support systems Decision Support Systems, Clinical - instrumentation Diagnosis Early Diagnosis Echocardiography Echocardiography - methods Ejection fraction EKG Electrocardiogram Electrocardiography Electrocardiography - methods Female Health care Heart failure Heart Failure - diagnosis Heart function Heart function tests Humans Infectious Diseases Intervention Male Metabolic Diseases Methods Middle Aged Molecular Medicine Neurosciences Patients Primary care Stroke Volume - physiology Young Adult |
| Title | Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial |
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