Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction
Objectives We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12‐lead electrocardiogram (ECG) in a large prospective cohort. Background Patients undergoing routine ECG may have undetected left ventricular (LV) dysfunct...
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| Vydáno v: | Journal of cardiovascular electrophysiology Ročník 30; číslo 5; s. 668 - 674 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Wiley Subscription Services, Inc
01.05.2019
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| ISSN: | 1045-3873, 1540-8167, 1540-8167 |
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| Abstract | Objectives
We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12‐lead electrocardiogram (ECG) in a large prospective cohort.
Background
Patients undergoing routine ECG may have undetected left ventricular (LV) dysfunction that warrants further echocardiographic assessment. However, identification of these patients can be challenging.
Methods
We applied the algorithm to all ECGs interpreted by the Mayo Clinic ECG laboratory in September 2018. The performance of the algorithm was tested among patients with recent echocardiographic assessments of LV function. We also applied the algorithm in patients with no recent echocardiographic assessments of LV function to determine the rate of new “positive screens.”
Results
Among 16 056 adult patients who underwent routine ECG, 8600 (age 67.1 ± 15.2 years, 45.6% male), had a transthoracic echocardiogram (TTE) and 3874 patients had a TTE and ECG less than 1 month apart. Among these patients, the algorithm was able to detect an EF less than or equal to 35% with 86.8% specificity, 82.5% sensitivity, and 86.5% accuracy, (area under the curve, 0.918). Among 474 “false‐positives screens,” 189 (39.8%) had an EF of 36% to 50%. Among patients with no prior TTE, the algorithm identified 3.5% of the patients with suspected EF less than or equal to 35%. Exploratory analysis suggests false positives could be reduced by assessing NT‐pro‐BNP after the initial “positive screen.”
Conclusions
A deep learning algorithm detected depressed LV function with good accuracy in routine practice. Further studies are needed to validate the algorithm in patients with no prior echocardiogram and to assess the impact on echocardiography utilization, cost, and clinical outcomes. |
|---|---|
| AbstractList | Objectives
We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12‐lead electrocardiogram (ECG) in a large prospective cohort.
Background
Patients undergoing routine ECG may have undetected left ventricular (LV) dysfunction that warrants further echocardiographic assessment. However, identification of these patients can be challenging.
Methods
We applied the algorithm to all ECGs interpreted by the Mayo Clinic ECG laboratory in September 2018. The performance of the algorithm was tested among patients with recent echocardiographic assessments of LV function. We also applied the algorithm in patients with no recent echocardiographic assessments of LV function to determine the rate of new “positive screens.”
Results
Among 16 056 adult patients who underwent routine ECG, 8600 (age 67.1 ± 15.2 years, 45.6% male), had a transthoracic echocardiogram (TTE) and 3874 patients had a TTE and ECG less than 1 month apart. Among these patients, the algorithm was able to detect an EF less than or equal to 35% with 86.8% specificity, 82.5% sensitivity, and 86.5% accuracy, (area under the curve, 0.918). Among 474 “false‐positives screens,” 189 (39.8%) had an EF of 36% to 50%. Among patients with no prior TTE, the algorithm identified 3.5% of the patients with suspected EF less than or equal to 35%. Exploratory analysis suggests false positives could be reduced by assessing NT‐pro‐BNP after the initial “positive screen.”
Conclusions
A deep learning algorithm detected depressed LV function with good accuracy in routine practice. Further studies are needed to validate the algorithm in patients with no prior echocardiogram and to assess the impact on echocardiography utilization, cost, and clinical outcomes. ObjectivesWe sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12‐lead electrocardiogram (ECG) in a large prospective cohort.BackgroundPatients undergoing routine ECG may have undetected left ventricular (LV) dysfunction that warrants further echocardiographic assessment. However, identification of these patients can be challenging.MethodsWe applied the algorithm to all ECGs interpreted by the Mayo Clinic ECG laboratory in September 2018. The performance of the algorithm was tested among patients with recent echocardiographic assessments of LV function. We also applied the algorithm in patients with no recent echocardiographic assessments of LV function to determine the rate of new “positive screens.”ResultsAmong 16 056 adult patients who underwent routine ECG, 8600 (age 67.1 ± 15.2 years, 45.6% male), had a transthoracic echocardiogram (TTE) and 3874 patients had a TTE and ECG less than 1 month apart. Among these patients, the algorithm was able to detect an EF less than or equal to 35% with 86.8% specificity, 82.5% sensitivity, and 86.5% accuracy, (area under the curve, 0.918). Among 474 “false‐positives screens,” 189 (39.8%) had an EF of 36% to 50%. Among patients with no prior TTE, the algorithm identified 3.5% of the patients with suspected EF less than or equal to 35%. Exploratory analysis suggests false positives could be reduced by assessing NT‐pro‐BNP after the initial “positive screen.”ConclusionsA deep learning algorithm detected depressed LV function with good accuracy in routine practice. Further studies are needed to validate the algorithm in patients with no prior echocardiogram and to assess the impact on echocardiography utilization, cost, and clinical outcomes. We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12-lead electrocardiogram (ECG) in a large prospective cohort.OBJECTIVESWe sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12-lead electrocardiogram (ECG) in a large prospective cohort.Patients undergoing routine ECG may have undetected left ventricular (LV) dysfunction that warrants further echocardiographic assessment. However, identification of these patients can be challenging.BACKGROUNDPatients undergoing routine ECG may have undetected left ventricular (LV) dysfunction that warrants further echocardiographic assessment. However, identification of these patients can be challenging.We applied the algorithm to all ECGs interpreted by the Mayo Clinic ECG laboratory in September 2018. The performance of the algorithm was tested among patients with recent echocardiographic assessments of LV function. We also applied the algorithm in patients with no recent echocardiographic assessments of LV function to determine the rate of new "positive screens."METHODSWe applied the algorithm to all ECGs interpreted by the Mayo Clinic ECG laboratory in September 2018. The performance of the algorithm was tested among patients with recent echocardiographic assessments of LV function. We also applied the algorithm in patients with no recent echocardiographic assessments of LV function to determine the rate of new "positive screens."Among 16 056 adult patients who underwent routine ECG, 8600 (age 67.1 ± 15.2 years, 45.6% male), had a transthoracic echocardiogram (TTE) and 3874 patients had a TTE and ECG less than 1 month apart. Among these patients, the algorithm was able to detect an EF less than or equal to 35% with 86.8% specificity, 82.5% sensitivity, and 86.5% accuracy, (area under the curve, 0.918). Among 474 "false-positives screens," 189 (39.8%) had an EF of 36% to 50%. Among patients with no prior TTE, the algorithm identified 3.5% of the patients with suspected EF less than or equal to 35%. Exploratory analysis suggests false positives could be reduced by assessing NT-pro-BNP after the initial "positive screen."RESULTSAmong 16 056 adult patients who underwent routine ECG, 8600 (age 67.1 ± 15.2 years, 45.6% male), had a transthoracic echocardiogram (TTE) and 3874 patients had a TTE and ECG less than 1 month apart. Among these patients, the algorithm was able to detect an EF less than or equal to 35% with 86.8% specificity, 82.5% sensitivity, and 86.5% accuracy, (area under the curve, 0.918). Among 474 "false-positives screens," 189 (39.8%) had an EF of 36% to 50%. Among patients with no prior TTE, the algorithm identified 3.5% of the patients with suspected EF less than or equal to 35%. Exploratory analysis suggests false positives could be reduced by assessing NT-pro-BNP after the initial "positive screen."A deep learning algorithm detected depressed LV function with good accuracy in routine practice. Further studies are needed to validate the algorithm in patients with no prior echocardiogram and to assess the impact on echocardiography utilization, cost, and clinical outcomes.CONCLUSIONSA deep learning algorithm detected depressed LV function with good accuracy in routine practice. Further studies are needed to validate the algorithm in patients with no prior echocardiogram and to assess the impact on echocardiography utilization, cost, and clinical outcomes. We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12-lead electrocardiogram (ECG) in a large prospective cohort. Patients undergoing routine ECG may have undetected left ventricular (LV) dysfunction that warrants further echocardiographic assessment. However, identification of these patients can be challenging. We applied the algorithm to all ECGs interpreted by the Mayo Clinic ECG laboratory in September 2018. The performance of the algorithm was tested among patients with recent echocardiographic assessments of LV function. We also applied the algorithm in patients with no recent echocardiographic assessments of LV function to determine the rate of new "positive screens." Among 16 056 adult patients who underwent routine ECG, 8600 (age 67.1 ± 15.2 years, 45.6% male), had a transthoracic echocardiogram (TTE) and 3874 patients had a TTE and ECG less than 1 month apart. Among these patients, the algorithm was able to detect an EF less than or equal to 35% with 86.8% specificity, 82.5% sensitivity, and 86.5% accuracy, (area under the curve, 0.918). Among 474 "false-positives screens," 189 (39.8%) had an EF of 36% to 50%. Among patients with no prior TTE, the algorithm identified 3.5% of the patients with suspected EF less than or equal to 35%. Exploratory analysis suggests false positives could be reduced by assessing NT-pro-BNP after the initial "positive screen." A deep learning algorithm detected depressed LV function with good accuracy in routine practice. Further studies are needed to validate the algorithm in patients with no prior echocardiogram and to assess the impact on echocardiography utilization, cost, and clinical outcomes. |
| Author | Attia, Zachi I. Friedman, Paul A. Kapa, Suraj Carter, Rickey E. Lopez‐Jimenez, Francisco Yao, Xiaoxi Noseworthy, Peter A. Mohan, Tarun L. Shah, Nilay D. Pellikka, Patricia A. |
| Author_xml | – sequence: 1 givenname: Zachi I. surname: Attia fullname: Attia, Zachi I. organization: Mayo Clinic – sequence: 2 givenname: Suraj orcidid: 0000-0003-2283-4340 surname: Kapa fullname: Kapa, Suraj organization: Mayo Clinic – sequence: 3 givenname: Xiaoxi surname: Yao fullname: Yao, Xiaoxi organization: Mayo Clinic – sequence: 4 givenname: Francisco surname: Lopez‐Jimenez fullname: Lopez‐Jimenez, Francisco organization: Mayo Clinic – sequence: 5 givenname: Tarun L. surname: Mohan fullname: Mohan, Tarun L. organization: Mayo Clinic – sequence: 6 givenname: Patricia A. surname: Pellikka fullname: Pellikka, Patricia A. organization: Mayo Clinic – sequence: 7 givenname: Rickey E. surname: Carter fullname: Carter, Rickey E. organization: Mayo Clinic College of Medicine – sequence: 8 givenname: Nilay D. surname: Shah fullname: Shah, Nilay D. organization: Mayo Clinic – sequence: 9 givenname: Paul A. orcidid: 0000-0001-5052-2948 surname: Friedman fullname: Friedman, Paul A. organization: Mayo Clinic – sequence: 10 givenname: Peter A. orcidid: 0000-0002-4308-0456 surname: Noseworthy fullname: Noseworthy, Peter A. email: Noseworthy.peter@mayo.edu organization: Mayo Clinic |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30821035$$D View this record in MEDLINE/PubMed |
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| Notes | Disclosures In the past 36 months, Dr. Shah has received research support through Mayo Clinic from the Food and Drug Administration to establish Yale‐Mayo Clinic Center for Excellence in Regulatory Science and Innovation (CERSI) program (U01FD005938), from the Centers of Medicare and Medicaid Innovation under the Transforming Clinical Practice Initiative (TCPI), from the Agency for Healthcare Research and Quality (R01HS025164; R01HS025402; 1U19HS024075; and R03HS025517), from the National Heart, Lung and Blood Institute of the National Institutes of Health (NIH) (R56HL130496 and R01HL131535), National Science Foundation, and from the Patient Centered Outcomes Research Institute (PCORI) to develop a Clinical Data Research Network (LHSNet). XY, TLM, PAP, REC, NDS, and PAN report no relevant relationships with industry. Mayo Clinic has licensed use of the low ejection fraction detection algorithm to and electronic stethoscope maker (Eko, Berkeley, CA). Several of the Mayo clinic authors are coinventors in the technology (PAF, SK, FLJ, and ZIA); however, Mayo Clinic and Mayo inventors will not receive financial benefit from the use of the technology at Mayo Clinic. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Undefined-3 |
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| References_xml | – ident: e_1_2_5_1_7_1 doi: 10.1016/j.echo.2014.10.003 – volume: 138 start-page: e210 issue: 13 year: 2018 ident: e_1_2_5_1_12_1 article-title: AHA/ACC/HRS Guideline for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society publication-title: Circulation – ident: e_1_2_5_1_6_1 doi: 10.1038/s41591-018-0240-2 – ident: e_1_2_5_1_17_1 doi: 10.1161/JAHA.117.006023 – volume: 2015 start-page: 2793 issue: 36 ident: e_1_2_5_1_5_1 article-title: 2015 ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC). Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC) publication-title: Eur Heart J – volume-title: Python Tutorial, Technical Report CS‐R9526 year: 1995 ident: e_1_2_5_1_8_1 – volume-title: An Introduction to Support Vector Machines year: 2000 ident: e_1_2_5_1_11_1 – volume-title: Principles and practice of screening for disease year: 1968 ident: e_1_2_5_1_15_1 – ident: e_1_2_5_1_2_1 doi: 10.1111/j.1751-7133.2008.tb00002.x – ident: e_1_2_5_1_4_1 doi: 10.1056/NEJM199209033271001 – ident: e_1_2_5_1_3_1 doi: 10.1016/S0140-6736(00)04560-8 – volume: 37 start-page: 448 year: 2015 ident: e_1_2_5_1_9_1 article-title: Batch normalization: accelerating deep network training by reducing internal covariate shift International Conference on Machine Learning publication-title: Proceedings of the 32nd International Conference on Machine Learning, PMLR, Lille France – ident: e_1_2_5_1_13_1 doi: 10.1016/j.jacc.2013.05.019 – ident: e_1_2_5_1_14_1 doi: 10.1161/01.CIR.0000130845.38133.8F – ident: e_1_2_5_1_16_1 doi: 10.1016/j.jelectrocard.2017.06.008 – ident: e_1_2_5_1_10_1 doi: 10.1109/ICSIPA.2011.6144164 |
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We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12‐lead... We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12-lead electrocardiogram... ObjectivesWe sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12‐lead... |
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| SubjectTerms | Algorithms artificial intelligence Deep learning Echocardiography ejection fraction EKG electrocardiogram Electrocardiography Heart Ultrasonic imaging Ventricle |
| Title | Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction |
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