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

Autor:	Khurshid S; Division of Cardiology (S.K., M.A.A., J.E.H.), Massachusetts General Hospital, Boston.; Cardiovascular Research Center (S.K., L.X.H., X.W., M.A.A., P.T.E., J.E.H., S.A.L.), Massachusetts General Hospital, Boston.; 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., Friedman S; 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., Reeder C; 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., Di Achille P; 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., Diamant N; 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., Singh P; 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., Harrington LX; Cardiovascular Research Center (S.K., L.X.H., X.W., M.A.A., P.T.E., J.E.H., S.A.L.), Massachusetts General Hospital, Boston.; 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., Wang X; Cardiovascular Research Center (S.K., L.X.H., X.W., M.A.A., P.T.E., J.E.H., S.A.L.), Massachusetts General Hospital, Boston.; 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., Al-Alusi MA; Division of Cardiology (S.K., M.A.A., J.E.H.), Massachusetts General Hospital, Boston.; Cardiovascular Research Center (S.K., L.X.H., X.W., M.A.A., P.T.E., J.E.H., S.A.L.), Massachusetts General Hospital, Boston.; 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., Sarma G; 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., Foulkes AS; Biostatistics Center (A.S.F.), Massachusetts General Hospital, Boston., Ellinor PT; Cardiovascular Research Center (S.K., L.X.H., X.W., M.A.A., P.T.E., J.E.H., S.A.L.), Massachusetts General Hospital, Boston.; Cardiac Arrhythmia Service (P.T.E., C.D.A., S.A.L.), Massachusetts General Hospital, Boston.; 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., Anderson CD; Cardiac Arrhythmia Service (P.T.E., C.D.A., S.A.L.), Massachusetts General Hospital, Boston.; Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital, Boston.; Henry and Allison McCance Center for Brain Health (C.D.A.), Massachusetts General Hospital, Boston.; Harvard Medical School, Boston, MA (A.S.F., P.T.E., C.D.A., J.E.H., S.A.L.).; Department of Neurology, Brigham and Women's Hospital, Boston, MA (C.D.A.)., Ho JE; Division of Cardiology (S.K., M.A.A., J.E.H.), Massachusetts General Hospital, Boston.; Cardiovascular Research Center (S.K., L.X.H., X.W., M.A.A., P.T.E., J.E.H., S.A.L.), Massachusetts General Hospital, Boston.; 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.; Harvard Medical School, Boston, MA (A.S.F., P.T.E., C.D.A., J.E.H., S.A.L.)., Philippakis AA; 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.; Eric and Wendy Schmidt Center (A.A.P.), Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge., Batra P; 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., Lubitz SA; Cardiovascular Research Center (S.K., L.X.H., X.W., M.A.A., P.T.E., J.E.H., S.A.L.), Massachusetts General Hospital, Boston.; Cardiac Arrhythmia Service (P.T.E., C.D.A., S.A.L.), Massachusetts General Hospital, Boston.; 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.; 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.; Harvard Medical School, Boston, MA (A.S.F., P.T.E., C.D.A., J.E.H., S.A.L.).
Jazyk:	angličtina
Zdroj:	Circulation [Circulation] 2022 Jan 11; Vol. 145 (2), pp. 122-133. Date of Electronic Publication: 2021 Nov 08.
DOI:	10.1161/CIRCULATIONAHA.121.057480
Abstrakt:	Background: 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. Methods: 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. Results: 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). Conclusions: 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.
Databáze:	MEDLINE
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