A Novel Diagnostic Prediction Model for Vestibular Migraine.

ABSTRACT

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.