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A Novel Diagnostic Prediction Model for Vestibular Migraine

Authors Zhou C, Zhang L, Jiang X, Shi S, Yu Q, Chen Q, Yao D, Pan Y

Received 27 March 2020

Accepted for publication 3 July 2020

Published 29 July 2020 Volume 2020:16 Pages 1845—1852


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Jun Chen

Chang Zhou, Lei Zhang, Xuemei Jiang, Shanshan Shi, Qiuhong Yu, Qihui Chen, Dan Yao, Yonghui Pan

Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150000, People’s Republic of China

Correspondence: Yonghui Pan
Department of Neurology, The First Affiliated Hospital of Harbin Medical University, No. 199 Dazhi Street, Nangang District, Harbin City, Heilongjiang 150000, People’s Republic of China
Tel +86 13945693065
Email [email protected]

Background: Increasing morbidity and misdiagnosis of vestibular migraine (VM) gravely affect the treatment of the disease as well as the patients’ quality of life. A powerful diagnostic prediction model is of great importance for management of the disease in the clinical setting.
Materials and Methods: Patients with a main complaint of dizziness were invited to join this prospective study. The diagnosis of VM was made according to the International Classification of Headache Disorders. Study variables were collected from a rigorous questionnaire survey, clinical evaluation, and laboratory tests for the development of a novel predictive diagnosis model for VM.
Results: A total of 235 patients were included in this study: 73 were diagnosed with VM and 162 were diagnosed with non-VM vertigo. Compared with non-VM vertigo patients, serum magnesium levels in VM patients were lower. Following the logistic regression analysis of risk factors, a predictive model was developed based on 6 variables: age, sex, autonomic symptoms, hypertension, cognitive impairment, and serum Mg2+ concentration. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was 0.856, which was better than some of the reported predictive models.
Conclusion: With high sensitivity and specificity, the proposed logistic model has a very good predictive capability for the diagnosis of VM. It can be used as a screening tool as well as a complementary diagnostic tool for primary care providers and other clinicians who are non-experts of VM.

Keywords: headache, dizziness, cognitive function, motion sickness, magnesium ion, predictive model

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