Development of a Statistical Model for the Prediction of Common Vestibular Diagnoses.

importanceTreatment of patients with vestibular disorders can be complex, requires lengthy clinic visit time, and uses greater clinical resources for diagnosis. A pre-encounter intake questionnaire may predict the most common disorders, allowing for more efficient allocation of resources and use of clinicians.

objectiveTo develop a statistical model for predicting vestibular diagnoses, prior to clinical evaluation, from an intake questionnaire. DESIGN, SETTING, AND

participantsRetrospective review of 414 consecutive new vestibular patient intake questionnaires (September 2012 through January 2014) and associated medical records with performance of logistic regression analyses and development of predictive models (July 2013 through May 2015).

interventionsUse of a vestibular intake questionnaire for triaging of new patients with complaints of dizziness. MAIN OUTCOMES AND MEASURES: Predictors for the diagnosis of benign paroxysmal positional vertigo (BPPV), Ménière's disease, and vestibular migraine.

resultsOf the 414 questionnaires analyzed, 381 (92%) had clinician information necessary to define a final diagnosis. Patients were 34% male and had a mean (range) age of 57 (19-91) years. Of the diagnoses, 183 (48%) were ear related (including 103 BPPV and 49 Meniere's disease), 141 (37%) neurological (including 109 vestibular migraine), 36 (9%) medical, 8 (2%) of psychological origin, 46 (12%) of unknown etiology, and 33 (9%) other causes. The diagnosis of BPPV could be predicted from 4 variables with a sensitivity of 79% and specificity of 65%. The diagnosis of Ménière's disease could be predicted from 5 variables with a sensitivity of 81% and specificity of 85%. The diagnosis of vestibular migraine could be predicted from 4 variables with a sensitivity of 76% and specificity of 59%. CONCLUSIONS AND RELEVANCE: A pre-encounter history questionnaire can provide useful diagnostic information for common vestibular disorders. This can help direct appointment scheduling to improve clinical efficiency, time to intervention, and use of resources. Further refinement may enable the use of shorter questionnaires or screening algorithms.