Cargando…
A Novel Diagnostic Prediction Model for Vestibular Migraine
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 METHO...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7398677/ https://www.ncbi.nlm.nih.gov/pubmed/32801719 http://dx.doi.org/10.2147/NDT.S255717 |
_version_ | 1783565999614722048 |
---|---|
author | Zhou, Chang Zhang, Lei Jiang, Xuemei Shi, Shanshan Yu, Qiuhong Chen, Qihui Yao, Dan Pan, Yonghui |
author_facet | Zhou, Chang Zhang, Lei Jiang, Xuemei Shi, Shanshan Yu, Qiuhong Chen, Qihui Yao, Dan Pan, Yonghui |
author_sort | Zhou, Chang |
collection | PubMed |
description | 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 Mg(2+) 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. |
format | Online Article Text |
id | pubmed-7398677 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-73986772020-08-13 A Novel Diagnostic Prediction Model for Vestibular Migraine Zhou, Chang Zhang, Lei Jiang, Xuemei Shi, Shanshan Yu, Qiuhong Chen, Qihui Yao, Dan Pan, Yonghui Neuropsychiatr Dis Treat Original Research 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 Mg(2+) 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. Dove 2020-07-29 /pmc/articles/PMC7398677/ /pubmed/32801719 http://dx.doi.org/10.2147/NDT.S255717 Text en © 2020 Zhou et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Zhou, Chang Zhang, Lei Jiang, Xuemei Shi, Shanshan Yu, Qiuhong Chen, Qihui Yao, Dan Pan, Yonghui A Novel Diagnostic Prediction Model for Vestibular Migraine |
title | A Novel Diagnostic Prediction Model for Vestibular Migraine |
title_full | A Novel Diagnostic Prediction Model for Vestibular Migraine |
title_fullStr | A Novel Diagnostic Prediction Model for Vestibular Migraine |
title_full_unstemmed | A Novel Diagnostic Prediction Model for Vestibular Migraine |
title_short | A Novel Diagnostic Prediction Model for Vestibular Migraine |
title_sort | novel diagnostic prediction model for vestibular migraine |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7398677/ https://www.ncbi.nlm.nih.gov/pubmed/32801719 http://dx.doi.org/10.2147/NDT.S255717 |
work_keys_str_mv | AT zhouchang anoveldiagnosticpredictionmodelforvestibularmigraine AT zhanglei anoveldiagnosticpredictionmodelforvestibularmigraine AT jiangxuemei anoveldiagnosticpredictionmodelforvestibularmigraine AT shishanshan anoveldiagnosticpredictionmodelforvestibularmigraine AT yuqiuhong anoveldiagnosticpredictionmodelforvestibularmigraine AT chenqihui anoveldiagnosticpredictionmodelforvestibularmigraine AT yaodan anoveldiagnosticpredictionmodelforvestibularmigraine AT panyonghui anoveldiagnosticpredictionmodelforvestibularmigraine AT zhouchang noveldiagnosticpredictionmodelforvestibularmigraine AT zhanglei noveldiagnosticpredictionmodelforvestibularmigraine AT jiangxuemei noveldiagnosticpredictionmodelforvestibularmigraine AT shishanshan noveldiagnosticpredictionmodelforvestibularmigraine AT yuqiuhong noveldiagnosticpredictionmodelforvestibularmigraine AT chenqihui noveldiagnosticpredictionmodelforvestibularmigraine AT yaodan noveldiagnosticpredictionmodelforvestibularmigraine AT panyonghui noveldiagnosticpredictionmodelforvestibularmigraine |