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

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.

Assessment of DPOAE test-retest difference curves via hierarchical Gaussian processes.

Distortion product otoacoustic emissions (DPOAE) testing is a promising alternative to behavioral hearing tests and auditory brainstem response testing of pediatric cancer patients. The central goal of this study is to assess whether significant changes in the DPOAE frequency/emissions curve (DP-gram) occur in pediatric patients in a test-retest scenario. This is accomplished through the construction of normal reference charts, or credible regions, that DP-gram differences lie in, as well as contour probabilities that measure how abnormal (or in a certain sense rare) a test-retest difference is. A challenge is that the data were collected over varying frequencies, at different time points from baseline, and on possibly one or both ears. A hierarchical structural equation Gaussian process model is proposed to handle the different sources of correlation in the emissions measurements, wherein both subject-specific random effects and variance components governing the smoothness and variability of each child's Gaussian process are coupled together.