Integrated diagnostic algorithm for acute vertigo combining TiTrATE, STANDING, and HINTS: a validation study in the emergency department.

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Abstract
Accurate diagnosis of acute vertigo (AV) in emergency settings is crucial due to varied underlying causes. Challenges include differentiating non-life-threatening conditions, like vestibular migraine, from severe issues, such as stroke. The “TiTrATE - STANDING Adapted” algorithm was created to help non-specialist emergency physicians diagnose posterior circulation strokes in AV patients, overcoming the limitations of current practices that require specialized knowledge and equipment. This study involved a prospective validation and retrospective analysis of 67 patients at the National Hospital for Neurology and Neurosurgery and University College London Hospital. Patients underwent objective oculomotor assessments through video oculography and pure tone audiometry, conducted by an experienced audiologist in the acute stage. The accuracy of the “TiTrATE - STANDING Adapted” algorithm was compared to final diagnoses made by specialists, which included a comprehensive review of medical histories, objective test results, and imaging studies. The “TiTrATE - STANDING Adapted” algorithm demonstrated a sensitivity of 90%, with low specificity (57.9%), resulting in a high rate of false positives (24 out of 67) and a global accuracy of 62.7%. Conditions such as vestibular migraine and chronic vascular issues (e.g., orthostatic hypotension) were often misclassified, impacting the overall specificity. Integrating TiTrATE, HINTS Plus, and STANDING into a single diagnostic algorithm for acute vertigo in the ED could enhance accuracy and streamline decision-making. However, the combined model must perform at least as well as its individual components. Key improvements needed before implementation include adding vestibular migraine criteria, refining stroke exclusion guidelines, and ongoing validation to boost diagnostic precision and patient outcomes.
Funding Information
  • National Institute for Health and Care Research (NIHR302201)

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