Department of Applied Clinical Sciences and Biotechnology, Section of Neurology, University of L'Aquila, L'Aquila, Italy.
Department of Health Sciences, Section of Clinical Pharmacology and Oncology, University of Florence, Florence, Italy.
J Headache Pain. 2019 Feb 13;20(1):15. doi: 10.1186/s10194-019-0966-3.
Case-finding tools, such as the Identify Chronic Migraine (ID-CM) questionnaire, can improve detection of CM and alleviate its significant societal burden. We aimed to develop and validate the Italian version of the ID-CM (ID-EC) in paper and as a smart app version in a headache clinic-based setting.
The study investigators translated and adapted to the Italian language the original ID-CM questionnaire (ID-EC) and further implemented it as a smart app. The ID-EC was tested in its paper and electronic version in consecutive patients referring to 9 Italian tertiary headache centers for their first in-person visit. The scoring algorithm of the ID-EC paper version was applied by the study investigators (case-finding) and by patients (self-diagnosis), while the smart app provided to patients automatically the diagnosis. Diagnostic accuracy of the ID-EC was assessed by matching the questionnaire results with the interview-based diagnoses performed by the headache specialists during the visit according to the criteria of International Classification of Headache Disorders, III edition, beta version.
We enrolled 531 patients in the test of the paper version of ID-EC and 427 in the validation study of the smart app. According to the clinical diagnosis 209 patients had CM in the paper version study and 202 had CM in the smart app study. 79.5% of patients returned valid paper questionnaires, while 100% of patients returned valid and complete smart app questionnaires. The paper questionnaire had a 81.5% sensitivity and a 81.1% specificity for case-finding and a 30.7% sensitivity and 90.7% specificity for self-diagnosis, while the smart app had a 64.9% sensitivity and 90.2% specificity.
Our data suggest that the ID-EC, developed and validated in tertiary headache centers, is a valid case-finding tool for CM, with sensitivity and specificity values above 80% in paper form, while the ID-EC smart app is more useful to exclude CM diagnosis in case of a negative result. Further studies are warranted to assess the diagnostic accuracy of the ID-EC in general practice and population-based settings.
病例发现工具，如识别慢性偏头痛（ID-CM）问卷，可以提高对 CM 的检测并减轻其对社会的巨大负担。我们旨在开发和验证意大利语版本的 ID-CM（ID-EC），并在头痛诊所环境中以纸质版和智能应用程序版本进行验证。
研究调查人员将原始 ID-CM 问卷（ID-EC）翻译成意大利语并进行了调整，并进一步将其开发为智能应用程序。ID-EC 的纸质版和电子版在连续的患者中进行了测试，这些患者因首次就诊而到意大利 9 家三级头痛中心就诊。研究调查人员应用 ID-EC 纸质版的评分算法进行病例发现，患者进行自我诊断，而智能应用程序则自动为患者提供诊断。根据国际头痛疾病分类，第三版，β版的标准，根据头痛专家在就诊期间进行的基于访谈的诊断，通过将问卷结果与 ID-EC 的诊断进行匹配来评估 ID-EC 的诊断准确性。
我们在 ID-EC 的纸质版测试中招募了 531 名患者，在智能应用程序的验证研究中招募了 427 名患者。根据临床诊断，在纸质版研究中 209 名患者患有 CM，在智能应用程序研究中 202 名患者患有 CM。79.5%的患者返回了有效的纸质问卷，而 100%的患者返回了有效的完整智能应用程序问卷。纸质问卷的病例发现敏感性为 81.5%，特异性为 81.1%，自我诊断的敏感性为 30.7%，特异性为 90.7%，而智能应用程序的敏感性为 64.9%，特异性为 90.2%。
我们的数据表明，在三级头痛中心开发和验证的 ID-EC 是一种有效的 CM 病例发现工具，其纸质形式的敏感性和特异性均高于 80%，而 ID-EC 智能应用程序在结果为阴性时更有助于排除 CM 诊断。需要进一步研究来评估 ID-EC 在一般实践和人群中的诊断准确性。
