Artificial Intelligence Application in Graves Disease: Atrial Fibrillation,Heart Failure and Menstrual Changes |
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| Institution: | 1. Department of Internal Medicine, Mayo Clinic, Rochester, MN;2. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN;3. Department of Endocrinology and Metabolism, Mayo Clinic, Rochester, MN;1. Division of Vascular and Endovascular Surgery;2. Division of Biomedical Statistics, Mayo Clinic, Rochester, MN. J.J.N. is currently at the Charles George VA Medical Center, Asheville, NC. M.C. is currently at Instituto Vascular, Passo Fundo, Brazil. P.G.R. is currently in the Department of Cardiovascular Surgery, Mayo Clinic, Rochester, MN;1. Department of Preventive Medicine and Population Health, University of Texas Medical Branch, Galveston;2. Division of Geriatrics and Palliative Medicine, Department of Internal Medicine, University of Texas Medical Branch, Galveston;3. Department of Preventive Medicine and Population Health, Office of Biostatistics, University of Texas Medical Branch, Galveston;4. Division of Geriatrics and Palliative Medicine, Department of Internal Medicine, University of Texas Health Science Center, Houston;1. Department of Pharmacy, Mayo Clinic, Rochester, MN;2. Department of Community Internal Medicine, Mayo Clinic, Rochester, MN;3. Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN;4. Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN;5. Department of Family Medicine, Mayo Clinic, Rochester, MN;1. Department of Epidemiology, Beijing Anzhen Hospital, Capital Medical University, the Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing, China;2. Vanke School of Public Health, Tsinghua University;3. Department of Gastroenterology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China;4. Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China;5. Division of Cardiology, University of North Carolina, Chapel Hill;6. Department of Cardiology, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China;7. Department of Cardiology, Peking University First Hospital, Beijing, China;8. Division of Cardiology, Geffen School of Medicine at University of California, Los Angeles;9. International Quality Improvement Department, American Heart Association, Dallas, TX;10. Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, Liaoning, China |
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| Abstract: | ObjectiveTo study the utility of artificial intelligence (AI)–enabled electrocardiograms (ECGs) in patients with Graves disease (GD) in identifying patients at high risk of atrial fibrillation (AF) and heart failure with reduced ejection fraction (HFrEF), and to study whether AI-ECG can reflect hormonal changes and the resulting menstrual changes in GD.Patients and MethodsPatients diagnosed with GD between January 1, 2009, and December 31, 2019, were included. We considered AF diagnosed at 30 days or fewer before or any time after GD and de novo HFrEF not explained by ischemia, valve disorder, or other cardiomyopathy at/after GD diagnosis. Electrocardiograms at/after index condition were excluded. A subset analysis included females younger than 45 years of age to study the association between ECG-derived female probability and menstrual changes (shorter, lighter, or newly irregular cycles).ResultsAmong 430 patients (mean age, 50±17 years; 337 (78.4%) female), independent risk factors for AF included ECG probability of AF (hazard ratio HR], 1.5; 95% CI, 1.2 to 1.6 per 10%; P<.001), older age (HR, 1.05; 95% CI, 1.03 to 1.07 per year; P<.001), and overt hyperthyroidism (HR, 3.9; 95% CI, 1.2 to 12.7; P=.03). The C-statistic was 0.85 for the combined model. Among 495 patients (mean age, 52±17 years; 374 (75.6%) female), independent risk factors for HFrEF were ECG probability of low ejection fraction (HR, 1.4; 95% CI, 1.1 to 1.6 per 10%; P=.001) and presence of AF (HR, 8.3; 95% CI, 2.2 to 30.9; P=.002), and a C-statistic of 0.89 for the combined model. Lastly, of 72 females younger than 45 years, 30 had menstrual changes at time of GD and had a significantly lower AI ECG–derived female probability median 77.3; (IQR 57.9 to 94.4)% vs. median 97.7 (IQR 92.4 to 99.5)%, P<.001].ConclusionAI-enabled ECG identifies patients at risk for GD-related AF and HFrEF and was associated with menstrual changes in women with GD. |
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