JOURNAL ARTICLE

Clinical features and outcome predictors in benign paroxysmal positional vertigo and its variant: Perspective in a primary care neurology clinic.

  • Published In: Journal of Vestibular Research: Equilibrium & Orientation, 2026, v. 36, n. 1. P. 57 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Kim, Min-Ku; Kim, Hyo-Jung; Choi, Jeong-Yoon; Kim, Ji-Soo 3 of 3

Abstract

This article focuses on the clinical characteristics and treatment outcomes of benign paroxysmal positional vertigo (BPPV) and its variant, light cupula syndrome, in a primary care neurology setting. Among 1126 patients presenting with dizziness or vertigo, 27.4% were diagnosed with BPPV or its variant, with the posterior canal (PC) subtype being most common (59.4%), followed by geotropic and apogeotropic horizontal canal (HC) types. The study found that the proportion of PC subtype increased with longer symptom duration, while patients exhibiting persistent positional nystagmus (lasting ≥1 minute) required significantly more canalith repositioning procedures (CRPs) and were more refractory to treatment than those with transient nystagmus. These findings suggest that in primary care, symptom duration and nystagmus persistence are important factors influencing BPPV subtype distribution and treatment responsiveness, highlighting the need for routine screening and tailored management strategies.

Additional Information

  • Source:Journal of Vestibular Research: Equilibrium & Orientation. 2026/01, Vol. 36, Issue 1, p57
  • Document Type:Case Study
  • Subject Area:Health and Medicine
  • Publication Date:2026
  • ISSN:0957-4271
  • DOI:10.1177/09574271251347261
  • Accession Number:189687730
  • Copyright Statement:Copyright of Journal of Vestibular Research: Equilibrium & Orientation is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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