ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation

Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. We trained...

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Bibliographic Details
Published in:	Circulation (New York, N.Y.) Vol. 145; no. 2; p. 122
Main Authors:	Khurshid, Shaan, Friedman, Samuel, Reeder, Christopher, Di Achille, Paolo, Diamant, Nathaniel, Singh, Pulkit, Harrington, Lia X, Wang, Xin, Al-Alusi, Mostafa A, Sarma, Gopal, Foulkes, Andrea S, Ellinor, Patrick T, Anderson, Christopher D, Ho, Jennifer E, Philippakis, Anthony A, Batra, Puneet, Lubitz, Steven A
Format:	Journal Article
Language:	English
Published:	United States 11.01.2022
Subjects:	Atrial Fibrillation - diagnosis Atrial Fibrillation - pathology Deep Learning - standards Electrocardiography - methods Female Humans Male Middle Aged Risk Factors deep learning atrial fibrillation electronic health records
ISSN:	1524-4539, 1524-4539
Online Access:	Get more information
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Abstract	Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. We trained a convolutional neural network (ECG-AI) to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit 3 Cox proportional hazards models, composed of ECG-AI 5-year AF probability, CHARGE-AF clinical risk score (Cohorts for Heart and Aging in Genomic Epidemiology-Atrial Fibrillation), and terms for both ECG-AI and CHARGE-AF (CH-AI), respectively. We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve) and calibration in an internal test set and 2 external test sets (Brigham and Women's Hospital [BWH] and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors. The training set comprised 45 770 individuals (age 55±17 years, 53% women, 2171 AF events) and the test sets comprised 83 162 individuals (age 59±13 years, 56% women, 2424 AF events). Area under the receiver operating characteristic curve was comparable using CHARGE-AF (MGH, 0.802 [95% CI, 0.767-0.836]; BWH, 0.752 [95% CI, 0.741-0.763]; UK Biobank, 0.732 [95% CI, 0.704-0.759]) and ECG-AI (MGH, 0.823 [95% CI, 0.790-0.856]; BWH, 0.747 [95% CI, 0.736-0.759]; UK Biobank, 0.705 [95% CI, 0.673-0.737]). Area under the receiver operating characteristic curve was highest using CH-AI (MGH, 0.838 [95% CI, 0.807 to 0.869]; BWH, 0.777 [95% CI, 0.766 to 0.788]; UK Biobank, 0.746 [95% CI, 0.716 to 0.776]). Calibration error was low using ECG-AI (MGH, 0.0212; BWH, 0.0129; UK Biobank, 0.0035) and CH-AI (MGH, 0.012; BWH, 0.0108; UK Biobank, 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson : MGH, 0.61; BWH, 0.66; UK Biobank, 0.41). AI-based analysis of 12-lead ECGs has similar predictive usefulness to a clinical risk factor model for incident AF and the approaches are complementary. ECG-AI may enable efficient quantification of future AF risk.
AbstractList	Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF.BACKGROUNDArtificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF.We trained a convolutional neural network (ECG-AI) to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit 3 Cox proportional hazards models, composed of ECG-AI 5-year AF probability, CHARGE-AF clinical risk score (Cohorts for Heart and Aging in Genomic Epidemiology-Atrial Fibrillation), and terms for both ECG-AI and CHARGE-AF (CH-AI), respectively. We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve) and calibration in an internal test set and 2 external test sets (Brigham and Women's Hospital [BWH] and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors.METHODSWe trained a convolutional neural network (ECG-AI) to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit 3 Cox proportional hazards models, composed of ECG-AI 5-year AF probability, CHARGE-AF clinical risk score (Cohorts for Heart and Aging in Genomic Epidemiology-Atrial Fibrillation), and terms for both ECG-AI and CHARGE-AF (CH-AI), respectively. We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve) and calibration in an internal test set and 2 external test sets (Brigham and Women's Hospital [BWH] and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors.The training set comprised 45 770 individuals (age 55±17 years, 53% women, 2171 AF events) and the test sets comprised 83 162 individuals (age 59±13 years, 56% women, 2424 AF events). Area under the receiver operating characteristic curve was comparable using CHARGE-AF (MGH, 0.802 [95% CI, 0.767-0.836]; BWH, 0.752 [95% CI, 0.741-0.763]; UK Biobank, 0.732 [95% CI, 0.704-0.759]) and ECG-AI (MGH, 0.823 [95% CI, 0.790-0.856]; BWH, 0.747 [95% CI, 0.736-0.759]; UK Biobank, 0.705 [95% CI, 0.673-0.737]). Area under the receiver operating characteristic curve was highest using CH-AI (MGH, 0.838 [95% CI, 0.807 to 0.869]; BWH, 0.777 [95% CI, 0.766 to 0.788]; UK Biobank, 0.746 [95% CI, 0.716 to 0.776]). Calibration error was low using ECG-AI (MGH, 0.0212; BWH, 0.0129; UK Biobank, 0.0035) and CH-AI (MGH, 0.012; BWH, 0.0108; UK Biobank, 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson r: MGH, 0.61; BWH, 0.66; UK Biobank, 0.41).RESULTSThe training set comprised 45 770 individuals (age 55±17 years, 53% women, 2171 AF events) and the test sets comprised 83 162 individuals (age 59±13 years, 56% women, 2424 AF events). Area under the receiver operating characteristic curve was comparable using CHARGE-AF (MGH, 0.802 [95% CI, 0.767-0.836]; BWH, 0.752 [95% CI, 0.741-0.763]; UK Biobank, 0.732 [95% CI, 0.704-0.759]) and ECG-AI (MGH, 0.823 [95% CI, 0.790-0.856]; BWH, 0.747 [95% CI, 0.736-0.759]; UK Biobank, 0.705 [95% CI, 0.673-0.737]). Area under the receiver operating characteristic curve was highest using CH-AI (MGH, 0.838 [95% CI, 0.807 to 0.869]; BWH, 0.777 [95% CI, 0.766 to 0.788]; UK Biobank, 0.746 [95% CI, 0.716 to 0.776]). Calibration error was low using ECG-AI (MGH, 0.0212; BWH, 0.0129; UK Biobank, 0.0035) and CH-AI (MGH, 0.012; BWH, 0.0108; UK Biobank, 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson r: MGH, 0.61; BWH, 0.66; UK Biobank, 0.41).AI-based analysis of 12-lead ECGs has similar predictive usefulness to a clinical risk factor model for incident AF and the approaches are complementary. ECG-AI may enable efficient quantification of future AF risk.CONCLUSIONSAI-based analysis of 12-lead ECGs has similar predictive usefulness to a clinical risk factor model for incident AF and the approaches are complementary. ECG-AI may enable efficient quantification of future AF risk. Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. We trained a convolutional neural network (ECG-AI) to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit 3 Cox proportional hazards models, composed of ECG-AI 5-year AF probability, CHARGE-AF clinical risk score (Cohorts for Heart and Aging in Genomic Epidemiology-Atrial Fibrillation), and terms for both ECG-AI and CHARGE-AF (CH-AI), respectively. We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve) and calibration in an internal test set and 2 external test sets (Brigham and Women's Hospital [BWH] and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors. The training set comprised 45 770 individuals (age 55±17 years, 53% women, 2171 AF events) and the test sets comprised 83 162 individuals (age 59±13 years, 56% women, 2424 AF events). Area under the receiver operating characteristic curve was comparable using CHARGE-AF (MGH, 0.802 [95% CI, 0.767-0.836]; BWH, 0.752 [95% CI, 0.741-0.763]; UK Biobank, 0.732 [95% CI, 0.704-0.759]) and ECG-AI (MGH, 0.823 [95% CI, 0.790-0.856]; BWH, 0.747 [95% CI, 0.736-0.759]; UK Biobank, 0.705 [95% CI, 0.673-0.737]). Area under the receiver operating characteristic curve was highest using CH-AI (MGH, 0.838 [95% CI, 0.807 to 0.869]; BWH, 0.777 [95% CI, 0.766 to 0.788]; UK Biobank, 0.746 [95% CI, 0.716 to 0.776]). Calibration error was low using ECG-AI (MGH, 0.0212; BWH, 0.0129; UK Biobank, 0.0035) and CH-AI (MGH, 0.012; BWH, 0.0108; UK Biobank, 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson : MGH, 0.61; BWH, 0.66; UK Biobank, 0.41). AI-based analysis of 12-lead ECGs has similar predictive usefulness to a clinical risk factor model for incident AF and the approaches are complementary. ECG-AI may enable efficient quantification of future AF risk.
Author	Reeder, Christopher Diamant, Nathaniel Harrington, Lia X Singh, Pulkit Friedman, Samuel Wang, Xin Philippakis, Anthony A Ellinor, Patrick T Di Achille, Paolo Khurshid, Shaan Sarma, Gopal Foulkes, Andrea S Ho, Jennifer E Lubitz, Steven A Anderson, Christopher D Al-Alusi, Mostafa A Batra, Puneet
Author_xml	– sequence: 1 givenname: Shaan orcidid: 0000-0002-2840-4539 surname: Khurshid fullname: Khurshid, Shaan organization: Cardiovascular Disease Initiative (S.K., L.X.H., X.W., M.A.A., P.T.E., J.E.H., S.A.L.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge – sequence: 2 givenname: Samuel surname: Friedman fullname: Friedman, Samuel organization: Data Sciences Platform (S.F., C.R., P.D.A., N.D., P.S., G.S., A.A.P., P.B.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge – sequence: 3 givenname: Christopher orcidid: 0000-0002-3893-2423 surname: Reeder fullname: Reeder, Christopher organization: Data Sciences Platform (S.F., C.R., P.D.A., N.D., P.S., G.S., A.A.P., P.B.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge – sequence: 4 givenname: Paolo orcidid: 0000-0001-9256-0678 surname: Di Achille fullname: Di Achille, Paolo organization: Data Sciences Platform (S.F., C.R., P.D.A., N.D., P.S., G.S., A.A.P., P.B.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge – sequence: 5 givenname: Nathaniel surname: Diamant fullname: Diamant, Nathaniel organization: Data Sciences Platform (S.F., C.R., P.D.A., N.D., P.S., G.S., A.A.P., P.B.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge – sequence: 6 givenname: Pulkit surname: Singh fullname: Singh, Pulkit organization: Data Sciences Platform (S.F., C.R., P.D.A., N.D., P.S., G.S., A.A.P., P.B.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge – sequence: 7 givenname: Lia X surname: Harrington fullname: Harrington, Lia X organization: Cardiovascular Disease Initiative (S.K., L.X.H., X.W., M.A.A., P.T.E., J.E.H., S.A.L.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge – sequence: 8 givenname: Xin surname: Wang fullname: Wang, Xin organization: Cardiovascular Disease Initiative (S.K., L.X.H., X.W., M.A.A., P.T.E., J.E.H., S.A.L.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge – sequence: 9 givenname: Mostafa A surname: Al-Alusi fullname: Al-Alusi, Mostafa A organization: Data Sciences Platform (S.F., C.R., P.D.A., N.D., P.S., G.S., A.A.P., P.B.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge – sequence: 10 givenname: Gopal surname: Sarma fullname: Sarma, Gopal organization: Data Sciences Platform (S.F., C.R., P.D.A., N.D., P.S., G.S., A.A.P., P.B.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge – sequence: 11 givenname: Andrea S orcidid: 0000-0002-9520-0501 surname: Foulkes fullname: Foulkes, Andrea S organization: Biostatistics Center (A.S.F.), Massachusetts General Hospital, Boston – sequence: 12 givenname: Patrick T orcidid: 0000-0002-2067-0533 surname: Ellinor fullname: Ellinor, Patrick T organization: Cardiovascular Disease Initiative (S.K., L.X.H., X.W., M.A.A., P.T.E., J.E.H., S.A.L.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge – sequence: 13 givenname: Christopher D orcidid: 0000-0002-0053-2002 surname: Anderson fullname: Anderson, Christopher D organization: Department of Neurology, Brigham and Women's Hospital, Boston, MA (C.D.A.) – sequence: 14 givenname: Jennifer E orcidid: 0000-0002-7987-4768 surname: Ho fullname: Ho, Jennifer E organization: Harvard Medical School, Boston, MA (A.S.F., P.T.E., C.D.A., J.E.H., S.A.L.) – sequence: 15 givenname: Anthony A surname: Philippakis fullname: Philippakis, Anthony A organization: Eric and Wendy Schmidt Center (A.A.P.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge – sequence: 16 givenname: Puneet orcidid: 0000-0001-6822-0593 surname: Batra fullname: Batra, Puneet organization: Data Sciences Platform (S.F., C.R., P.D.A., N.D., P.S., G.S., A.A.P., P.B.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge – sequence: 17 givenname: Steven A orcidid: 0000-0002-9599-4866 surname: Lubitz fullname: Lubitz, Steven A organization: Harvard Medical School, Boston, MA (A.S.F., P.T.E., C.D.A., J.E.H., S.A.L.)
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PublicationTitle	Circulation (New York, N.Y.)
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Snippet	Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it...
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SubjectTerms	Atrial Fibrillation - diagnosis Atrial Fibrillation - pathology Deep Learning - standards Electrocardiography - methods Female Humans Male Middle Aged Risk Factors
Title	ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation
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