Development and validation of a diagnostic model for migraine without aura in inpatients
ObjectivesThis study aimed to develop and validate a robust predictive model for accurately identifying migraine without aura (MWoA) individuals from migraine patients.MethodsWe recruited 637 migraine patients, randomizing them into training and validation cohorts. Participant’s medical data were co...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1511252/full |
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| author | Zhu-Hong Chen Zhu-Hong Chen Guan Yang Chi Zhang Chi Zhang Dan Su Dan Su Yu-Ting Li Yu-Xuan Shang Wei Zhang Wen Wang |
| author_facet | Zhu-Hong Chen Zhu-Hong Chen Guan Yang Chi Zhang Chi Zhang Dan Su Dan Su Yu-Ting Li Yu-Xuan Shang Wei Zhang Wen Wang |
| author_sort | Zhu-Hong Chen |
| collection | DOAJ |
| description | ObjectivesThis study aimed to develop and validate a robust predictive model for accurately identifying migraine without aura (MWoA) individuals from migraine patients.MethodsWe recruited 637 migraine patients, randomizing them into training and validation cohorts. Participant’s medical data were collected such as demographic data (age, gender, self-reported headache characteristics) and clinical details including symptoms, triggers, and comorbidities. The model stability, which was developed using multivariable logistic regression, was tested by the internal validation cohort. Model efficacy was evaluated using the area under the receiver operating characteristic curve (AUC), alongside with nomogram, calibration curve, and decision curve analysis (DCA).ResultsThe study included 477 females (average age 46.62 ± 15.64) and 160 males (average age 39.78 ± 19.53). A total of 397 individuals met the criteria for MWoA. Key predictors in the regression model included patent foramen ovale (PFO) (OR = 2.30, p = 0.01), blurred vision (OR = 0.40, p = 0.001), dizziness (OR = 0.16, p < 0.01), and anxiety/depression (OR = 0.41, p = 0.02). Common symptoms like nausea (OR = 0.79, p = 0.43) and vomiting (OR = 0.64, p = 0.17) were not statistically significant predictors for MWoA. The AUC values were 79.1% and 82.8% in the training and validation cohorts, respectively, with good calibration in both.ConclusionThe predictive model developed and validated in this study demonstrates significant efficacy in identifying MWoA. Our findings highlight PFO as a potential key risk factor, underscoring its importance for early prevention, screening, and diagnosis of MWoA. |
| format | Article |
| id | doaj-art-09238f9ca57343ec90db501606a17f11 |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-09238f9ca57343ec90db501606a17f112025-01-21T05:43:15ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-01-011610.3389/fneur.2025.15112521511252Development and validation of a diagnostic model for migraine without aura in inpatientsZhu-Hong Chen0Zhu-Hong Chen1Guan Yang2Chi Zhang3Chi Zhang4Dan Su5Dan Su6Yu-Ting Li7Yu-Xuan Shang8Wei Zhang9Wen Wang10Functional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Medical Imaging, Gansu Corps Hospital of Chinese Armed Police Force, Lanzhou, Gansu, ChinaFunctional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaFunctional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Medical and Technology, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, ChinaFunctional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Medical and Technology, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, ChinaFunctional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaFunctional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaDepartment of Neurology, Tangdu Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaFunctional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi’an, Shaanxi, ChinaObjectivesThis study aimed to develop and validate a robust predictive model for accurately identifying migraine without aura (MWoA) individuals from migraine patients.MethodsWe recruited 637 migraine patients, randomizing them into training and validation cohorts. Participant’s medical data were collected such as demographic data (age, gender, self-reported headache characteristics) and clinical details including symptoms, triggers, and comorbidities. The model stability, which was developed using multivariable logistic regression, was tested by the internal validation cohort. Model efficacy was evaluated using the area under the receiver operating characteristic curve (AUC), alongside with nomogram, calibration curve, and decision curve analysis (DCA).ResultsThe study included 477 females (average age 46.62 ± 15.64) and 160 males (average age 39.78 ± 19.53). A total of 397 individuals met the criteria for MWoA. Key predictors in the regression model included patent foramen ovale (PFO) (OR = 2.30, p = 0.01), blurred vision (OR = 0.40, p = 0.001), dizziness (OR = 0.16, p < 0.01), and anxiety/depression (OR = 0.41, p = 0.02). Common symptoms like nausea (OR = 0.79, p = 0.43) and vomiting (OR = 0.64, p = 0.17) were not statistically significant predictors for MWoA. The AUC values were 79.1% and 82.8% in the training and validation cohorts, respectively, with good calibration in both.ConclusionThe predictive model developed and validated in this study demonstrates significant efficacy in identifying MWoA. Our findings highlight PFO as a potential key risk factor, underscoring its importance for early prevention, screening, and diagnosis of MWoA.https://www.frontiersin.org/articles/10.3389/fneur.2025.1511252/fullmigrainemigraine without auralogistic regression modeldiagnosispatent foramen ovale |
| spellingShingle | Zhu-Hong Chen Zhu-Hong Chen Guan Yang Chi Zhang Chi Zhang Dan Su Dan Su Yu-Ting Li Yu-Xuan Shang Wei Zhang Wen Wang Development and validation of a diagnostic model for migraine without aura in inpatients Frontiers in Neurology migraine migraine without aura logistic regression model diagnosis patent foramen ovale |
| title | Development and validation of a diagnostic model for migraine without aura in inpatients |
| title_full | Development and validation of a diagnostic model for migraine without aura in inpatients |
| title_fullStr | Development and validation of a diagnostic model for migraine without aura in inpatients |
| title_full_unstemmed | Development and validation of a diagnostic model for migraine without aura in inpatients |
| title_short | Development and validation of a diagnostic model for migraine without aura in inpatients |
| title_sort | development and validation of a diagnostic model for migraine without aura in inpatients |
| topic | migraine migraine without aura logistic regression model diagnosis patent foramen ovale |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1511252/full |
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