Development and validation of non-invasive prediction models for migraine in Chinese adults
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Le résumé fourni par la source
BACKGROUND: Migraine is a common disabling neurological disorder with severe physical and psychological damage, but there is a lack of convenient and effective non-invasive early prediction methods. This study aimed to develop a new series of non-invasive prediction models for migraine with external validation. METHODS: A total of 188 and 94 subjects were included in the training and validation sets, respectively. A standardized professional questionnaire was used to collect the subjects' 9-item traditional Chinese medicine constitution (TCMC) scores, Pittsburgh Sleep Quality Index (PSQI) score, Zung's Self-rating Anxiety Scale and Self-rating Depression Scale scores. Logistic regression was used to analyze the risk predictors of migraine, and a series of prediction models for migraine were developed. Receiver operating characteristic (ROC) curve and calibration curve were used to assess the discrimination and calibration of the models. The predictive performance of the models were further validated using external datasets and subgroup analyses were conducted. RESULTS: PSQI score and Qi-depression score were significantly and positively associated with the risk of migraine, with the area of the ROC curves (AUCs) predicting migraine of 0.83 (95% CI:0.77-0.89) and 0.76 (95% CI:0.68-0.84), respectively. Eight non-invasive predictive models for migraine containing one to eight variables were developed using logistic regression, with AUCs ranging from 0.83 (95% CI: 0.77-0.89) to 0.92 (95% CI: 0.89-0.96) for the training set and from 0.76 (95% CI: 0.66-0.85) to 0.83 (95% CI: 0.75-0.91) for the validation set. Subgroup analyses showed that the AUCs of the eight prediction models for predicting migraine in the training and validation sets of different gender and age subgroups ranged from 0.80 (95% CI: 0.63-0.97) to 0.95 (95% CI: 0.91-1.00) and 0.73 (95% CI: 0.64-0.84) to 0.93 (95% CI: 0.82-1.00), respectively. CONCLUSIONS: This study developed and validated a series of convenient and novel non-invasive prediction models for migraine, which have good predictive ability for migraine in Chinese adults of different genders and ages. It is of great significance for the early prevention, screening, and diagnosis of migraine.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Development and validation of non-invasive prediction models for migraine in Chinese adults
- Date Crossref
- 06/11/2023
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Taizhou Central Hospital Department of Geriatrics pays non établi dans la noticeÉtablissement de santé
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Taizhou University pays non établi dans la noticeUniversité ou école supérieure
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Guangzhou University of Chinese Medicine pays non établi dans la noticeUniversité ou école supérieure
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Beijing University of Chinese Medicine Dongfang Hospital pays non établi dans la noticeUniversité ou école supérieure
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China-Japan Friendship Hospital Department of Neurology pays non établi dans la noticeÉtablissement de santé
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Peking University Department of Neurology pays non établi dans la noticeUniversité ou école supérieure
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Peking University People's Hospital pays non établi dans la noticeÉtablissement de santé
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The Second Clinical College pays non établi dans la noticeUniversité ou école supérieure
Department of Geriatrics — Taizhou Central Hospital, Taizhou University et Guangzhou University of Chinese Medicine, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.