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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.

Glucocorticoids (GCs) are a class of steroid hormones that regulate numerous physiological events in the human body. Clinically, glucocorticoids are used for antiinflammatory and immunosuppressive actions via binding with gluc ocorticoid receptors (GRs). Emerging evidence has also indicated that inappropriate GC and GR levels are detrimental for brain development and eventually lead to severe neurological diseases. However, the roles of GC/GR signaling in brain development are not fully understood. Here, we showed that stable GR expression levels were critical for brain development, because both GR knockdown and overexpression severely impaired neuronal migration. Further studies showed that the multipolar-bipolar transition a nd leading process development were interrupted in GR-knockdown and GR-overexpressing neurons. To elucidate the underlying mechanism, we screened the protein lev els of downstream molecules and identified RhoA as a factor negatively regulated by the GR. Restoration of the RhoA protein level partially rescued the neuronal migration defects in the GR-knockdown and GR-overexpressing neurons, indicating that RhoA played a major role in GR-mediated neuronal migration. These data suggest that an a ppropriate level of GC/GR signaling is essential for precise control of neuronal mi gration. [ABSTRACT FROM AUTHOR]