Development and validation of non-invasive prediction models for migraine in Chinese adults
Abstract 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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | The Journal of Headache and Pain |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s10194-023-01675-1 |
| _version_ | 1797630106667057152 |
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| author | Shaojie Duan Hui Xia Tao Zheng Guanglu Li Zhiying Ren Wenyan Ding Ziyao Wang Zunjing Liu |
| author_facet | Shaojie Duan Hui Xia Tao Zheng Guanglu Li Zhiying Ren Wenyan Ding Ziyao Wang Zunjing Liu |
| author_sort | Shaojie Duan |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-11T11:02:15Z |
| format | Article |
| id | doaj.art-77ce84b3eeff45c78371124369adcb5a |
| institution | Directory Open Access Journal |
| issn | 1129-2377 |
| language | English |
| last_indexed | 2024-03-11T11:02:15Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | The Journal of Headache and Pain |
| spelling | doaj.art-77ce84b3eeff45c78371124369adcb5a2023-11-12T12:24:51ZengBMCThe Journal of Headache and Pain1129-23772023-11-0124111610.1186/s10194-023-01675-1Development and validation of non-invasive prediction models for migraine in Chinese adultsShaojie Duan0Hui Xia1Tao Zheng2Guanglu Li3Zhiying Ren4Wenyan Ding5Ziyao Wang6Zunjing Liu7Department of Geriatrics, Taizhou Central Hospital (Taizhou University Hospital)The Second Clinical College, Guangzhou University of Chinese MedicineDongfang Hospital, Beijing University of Chinese MedicineGraduate School of Beijing University of Chinese MedicineGraduate School of Beijing University of Chinese MedicineDepartment of Geriatrics, Taizhou Central Hospital (Taizhou University Hospital)Graduate School of Beijing University of Chinese MedicineDepartment of Neurology, Peking University People’s HospitalAbstract 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.https://doi.org/10.1186/s10194-023-01675-1MigrainePrediction modelReceiver operating characteristic curvePittsburgh sleep quality indexTraditional Chinese medicine constitution |
| spellingShingle | Shaojie Duan Hui Xia Tao Zheng Guanglu Li Zhiying Ren Wenyan Ding Ziyao Wang Zunjing Liu Development and validation of non-invasive prediction models for migraine in Chinese adults The Journal of Headache and Pain Migraine Prediction model Receiver operating characteristic curve Pittsburgh sleep quality index Traditional Chinese medicine constitution |
| title | Development and validation of non-invasive prediction models for migraine in Chinese adults |
| title_full | Development and validation of non-invasive prediction models for migraine in Chinese adults |
| title_fullStr | Development and validation of non-invasive prediction models for migraine in Chinese adults |
| title_full_unstemmed | Development and validation of non-invasive prediction models for migraine in Chinese adults |
| title_short | Development and validation of non-invasive prediction models for migraine in Chinese adults |
| title_sort | development and validation of non invasive prediction models for migraine in chinese adults |
| topic | Migraine Prediction model Receiver operating characteristic curve Pittsburgh sleep quality index Traditional Chinese medicine constitution |
| url | https://doi.org/10.1186/s10194-023-01675-1 |
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