ArticleNeuropsychiatric disease and treatment2020
A Novel Diagnostic Prediction Model for Vestibular Migraine.
Article in Neuropsychiatric disease and treatment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Diagnostic accuracy and usability of the EMBalance decision support system for vestibular disorders in primary care: proof of concept randomised controlled study results.Journal of neurology · 2022Trial
- Identifying tests to evaluate in a diagnostic accuracy study for patients with vertigo in general practice: a Delphi study.BMC primary care · 2025Article
- Exploring vestibulocerebellum-vestibular nuclei-spinal trigeminal nucleus causals communication and TRPV2 ion channel in a mouse model of vestibular migraine.The journal of headache and pain · 2025Article
- Focused Update on Migraine and Vertigo Comorbidity.Current pain and headache reports · 2024Review
- Study of clinical correlation of motion sickness in patients with vestibular migraine.Frontiers in neuroscience · 2022Article
- Nutraceuticals for Peripheral Vestibular Pathology: Properties, Usefulness, Future Perspectives and Medico-Legal Aspects.Nutrients · 2021Review
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIncreasing 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
methodsPatients 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.
resultsA 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 Mg
conclusionWith 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.
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