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Background: 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.Methods: 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.Results: 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.Conclusions: 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. [ABSTRACT FROM AUTHOR]

Objective. The aim of this study was to develop and validate an algorithm to assist the attribution of neuropsychiatric (NP) events to underlying disease in SLE patients. Methods. Phase 1 identified and categorized candidate items to be included in the algorithm for the attribution of an NP event to SLE and their relative weights through a literature-informed consensus-driven process. Using a retrospective training cohort of SLE, phase 2 validated items selected in phase 1 and refined weights through a data-driven process, fitting items as independent variables and expert evaluation (clinical judgement) as reference standard in logistic models. Phase 3 consisted of a validation process using an external multicentre retrospective SLE cohort. Results. Phase 1 identified four different items: timing of the NP event, type of event, confounding factors and favouring factors. The training and validating cohorts included 228 and 221 patients, respectively. Each patient experienced at least one NP event characterized using the ACR case definition. In these samples, items selected in phase 1 showed good performance in discriminating patients with NPSLE: the area under the receiver operating characteristic curve using dichotomous outcomes was 0.87 in the training set and 0.82 in the validating set. Relevant cut-offs of the validated score identify events with a positive predictive value of 100% (95% CI 93.2, 100) and 86.3% (95% CI 76.2, 93.2) in the training and validating cohorts, respectively. Conclusion. A new algorithm based on a probability score was developed and validated to determine the relationship between NP events and SLE. [ABSTRACT FROM AUTHOR]