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Determinants of Use of Long-term Continuous Electrocardiographic Monitoring for Acute Ischemic Stroke Patients without Atrial Fibrillation at Baseline.
Lee, Jiann-Der; Huang, Yen-Chu; Lee, Meng; Lee, Tsong-Hai; Kuo, Ya-Wen; Hu, Ya-Han; Ovbiagele, Bruce.
Afiliação
  • Lee JD; Department of Neurology, Chang Gung Memorial Hospital, Chiayi, Taiwan.
  • Huang YC; Department of Neurology, Chang Gung Memorial Hospital, Chiayi, Taiwan.
  • Lee M; Department of Neurology, Chang Gung Memorial Hospital, Chiayi, Taiwan.
  • Lee TH; Department of Neurology, Chang Gung Memorial Hospital, Chiayi, Taiwan.
  • Kuo YW; Department of Nursing, Chang Gung University of Science and Technology, Chiayi Campus, Chiayi, Taiwan.
  • Hu YH; Department of Information Management, National Central University, Taoyuan City, Taiwan.
  • Ovbiagele B; Department of Neurology, University of California, San Francisco, CA, United States.
Curr Neurovasc Res ; 17(3): 224-231, 2020.
Article em En | MEDLINE | ID: mdl-32324514
ABSTRACT

BACKGROUND:

Atrial fibrillation (AF) is the most common cardiac rhythm disorder associated with stroke. Increased risk of stroke is the same regardless of whether the AF is permanent or paroxysmal. However, detecting paroxysmal AF is challenging and resource intensive. We aimed to develop a predictive model for AF in patients with acute ischemic stroke, which could improve the detection rate of paroxysmal AF.

METHODS:

We analyzed 10,034 adult patients with acute ischemic stroke. Differences in clinical characteristics between the patients with and without AF were analyzed in order to develop a predictive model of AF. The associated factors for AF were analyzed using multivariate logistic regression and classification and regression tree (CART) analyses. We used another dataset, which enrolled 860 acute ischemic stroke patients without AF at baseline, to test whether the developed model could improve the detection rate of paroxysmal AF. Among the study population, 1,658 patients (16.5%) had AF.

RESULTS:

Multivariate logistic regression revealed that sex, age, body weight, hypertension, diabetes mellitus, hyperlipidemia, pulse rate at admission, respiratory rate at admission, systolic blood pressure at admission, diastolic blood pressure at admission, National Institute of Health Stroke Scale (NIHSS) score at admission, total cholesterol level, triglyceride level, aspartate transaminase level, and sodium level were major factors associated with AF. CART analysis identified NIHSS score at admission, age, triglyceride level, and aspartate transaminase level as important factors for AF to classify the patients into subgroups.

CONCLUSION:

When selecting the high-risk group of patients (with an NIHSS score >12 and age >64.5 years, or with an NIHSS score ≤12, age >71.5 years, and triglyceride level ≤61.5 mg/dL) according to the CART model, the detection rate of paroxysmal AF was approximately double in the acute ischemic stroke patients without AF at baseline.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Fibrilação Atrial / Isquemia Encefálica / Eletrocardiografia / AVC Isquêmico Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Fibrilação Atrial / Isquemia Encefálica / Eletrocardiografia / AVC Isquêmico Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2020 Tipo de documento: Article