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Artificial Intelligence Application in Graves Disease: Atrial Fibrillation, Heart Failure and Menstrual Changes.
Naser, Jwan A; Attia, Zachi I; Pislaru, Sorin V; Stan, Marius N; Pellikka, Patricia A; Noseworthy, Peter A; Friedman, Paul A; Lin, Grace.
Afiliação
  • Naser JA; Department of Internal Medicine, Mayo Clinic, Rochester, MN; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
  • Attia ZI; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
  • Pislaru SV; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
  • Stan MN; Department of Endocrinology and Metabolism, Mayo Clinic, Rochester, MN.
  • Pellikka PA; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
  • Noseworthy PA; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
  • Friedman PA; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
  • Lin G; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN. Electronic address: Lin.Grace@mayo.edu.
Mayo Clin Proc ; 97(4): 730-737, 2022 04.
Article em En | MEDLINE | ID: mdl-35078654
ABSTRACT

OBJECTIVE:

To 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

METHODS:

Patients 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).

RESULTS:

Among 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].

CONCLUSION:

AI-enabled ECG identifies patients at risk for GD-related AF and HFrEF and was associated with menstrual changes in women with GD.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fibrilação Atrial / Doença de Graves / Insuficiência Cardíaca Tipo de estudo: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Middle aged Idioma: En Revista: Mayo Clin Proc Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Mongólia

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fibrilação Atrial / Doença de Graves / Insuficiência Cardíaca Tipo de estudo: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Middle aged Idioma: En Revista: Mayo Clin Proc Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Mongólia