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Development and validation of a web-based headache diagnosis questionnaire.

ABSTRACT: Information technology advances may help in conducting epidemiological studies using web-based surveys. Questionnaire-based headache diagnosis should be validated against the doctor's diagnosis. This study aimed to develop and validate a web-based diagnostic questionnaire for migraine, probable migraine (PM), and tension-type headache (TTH). We constructed a seven-item questionnaire for diagnosing migraine, PM, and TTH. A web-based survey was conducted among adults aged 20-59 years; migraine, PM, and TTH were diagnosed based on the responses. Validation interview was performed via telephone by a neurologist within 1 month after the web-based interview. Finally, 256 participants completed both web-based survey and validation interview. Of them, 121 (47.3%), 65 (25.4%), 61 (23.8%), and 9 (3.5%) were diagnosed with migraine, PM, TTH, and unclassified headache (UH), respectively in the web-based survey, whereas 119 (46.5%), 60 (23.4%), 74 (28.9%), 2 (0.8%), and 1 (0.4%) were diagnosed with migraine, PM, TTH, UH, and primary stabbing headache, respectively in the validation interview. The best agreement was found in migraine (sensitivity: 92.6%; specificity: 94.8%; kappa coefficient: 0.875), followed by TTH (sensitivity: 78.4%; specificity: 98.4%; kappa coefficient: 0.809). PM showed the least agreement (sensitivity: 85.0%; specificity: 92.9%; kappa coefficient: 0.757). In conclusion, our questionnaire is valid in identifying these headache disorders.

SUBMITTER: Kim KM

PROVIDER: S-EPMC9052186 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Development and validation of a web-based headache diagnosis questionnaire.

Kim Kyung Min KM Kim A Ra AR Lee Wonwoo W Jang Bo Hyun BH Heo Kyoung K Chu Min Kyung MK

Scientific reports 20220429 1

Information technology advances may help in conducting epidemiological studies using web-based surveys. Questionnaire-based headache diagnosis should be validated against the doctor's diagnosis. This study aimed to develop and validate a web-based diagnostic questionnaire for migraine, probable migraine (PM), and tension-type headache (TTH). We constructed a seven-item questionnaire for diagnosing migraine, PM, and TTH. A web-based survey was conducted among adults aged 20-59 years; migraine, PM ...[more]

PMID: 35488015

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Project description:BackgroundQuestionnaires have been used in the past 2 decades to predict the diagnosis of vertigo and assist clinical decision-making. A questionnaire-based machine learning model is expected to improve the efficiency of diagnosis of vestibular disorders.ObjectiveThis study aims to develop and validate a questionnaire-based machine learning model that predicts the diagnosis of vertigo.MethodsIn this multicenter prospective study, patients presenting with vertigo entered a consecutive cohort at their first visit to the ENT and vertigo clinics of 7 tertiary referral centers from August 2019 to March 2021, with a follow-up period of 2 months. All participants completed a diagnostic questionnaire after eligibility screening. Patients who received only 1 final diagnosis by their treating specialists for their primary complaint were included in model development and validation. The data of patients enrolled before February 1, 2021 were used for modeling and cross-validation, while patients enrolled afterward entered external validation.ResultsA total of 1693 patients were enrolled, with a response rate of 96.2% (1693/1760). The median age was 51 (IQR 38-61) years, with 991 (58.5%) females; 1041 (61.5%) patients received the final diagnosis during the study period. Among them, 928 (54.8%) patients were included in model development and validation, and 113 (6.7%) patients who enrolled later were used as a test set for external validation. They were classified into 5 diagnostic categories. We compared 9 candidate machine learning methods, and the recalibrated model of light gradient boosting machine achieved the best performance, with an area under the curve of 0.937 (95% CI 0.917-0.962) in cross-validation and 0.954 (95% CI 0.944-0.967) in external validation.ConclusionsThe questionnaire-based light gradient boosting machine was able to predict common vestibular disorders and assist decision-making in ENT and vertigo clinics. Further studies with a larger sample size and the participation of neurologists will help assess the generalization and robustness of this machine learning method.

Dataset Information

Development and validation of a web-based headache diagnosis questionnaire.

Publications

Development and validation of a web-based headache diagnosis questionnaire.

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