Assessment of a Statistical Algorithm for the Prediction of Benign Paroxysmal Positional Vertigo.

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Assessment of a Statistical Algorithm for the Prediction of Benign Paroxysmal Positional Vertigo.

JAMA Otolaryngol Head Neck Surg. 2018 Aug 30;:

Authors: Britt CJ, Ward BK, Owusu Y, Friedland D, Russell JO, Weinreich HM

Abstract
Importance: Benign paroxysmal positional vertigo (BPPV) is an otologic pathologic condition defined as a sensation of spinning triggered by changes in head position relative to gravity and caused by an entrapment of fragmented endolymph debris most commonly in the posterior semicircular canal. Confirmation of diagnosis requires experience with procedures that are poorly known by those other than practitioners with advanced otologic training. The complexity in the diagnosis of BPPV inspired the design of a questionnaire-based algorithm that would be useful for determining a vestibular diagnosis and treatment options.
Objective: To assess a statistical algorithm for the diagnosis of BPPV in a busy tertiary care setting, with the long-term goal of implementing a clinical pathway to efficiently diagnose and treat patients with dizziness.
Design, Setting, and Participants: In this retrospective case series, 200 patients who visited the Department of Otolaryngology-Head and Neck Surgery at Johns Hopkins University School of Medicine for their initial vertigo symptoms from September 1, 2016, to December 31, 2016, were assessed.
Interventions: Use of a validated patient questionnaire as a tool to differentiate patients with dizziness in an electronic medical record review.
Main Outcomes and Measures: Linear predictor (LP) value based on the questionnaire for the diagnosis of BPPV.
Results: Of the 200 patient visits reviewed (132 [66%] female), 106 (53.0%; 68 [64%] female) had the information necessary to calculate the LP value and had a confirmed final diagnosis. On the basis of an LP value of 0.2 or greater, the sensitivity for a diagnosis of BPPV was 0.75 and the specificity was 1.0. The positive predictive value was 1.0, whereas the negative predictive value was 0.96. Patients with BPPV had a statistically significantly different LP value (odds ratio, 5.92; 95% CI, 2.73-12.83) than did patients without BPPV.
Conclusions and Relevance: The findings of this study suggest that the algorithm is efficient for the diagnosis of BPPV in a clinical care setting.

PMID: 30178063 [PubMed - as supplied by publisher]

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