German Center for Vertigo and Balance Disorders, University Hospital, Ludwig-Maximilians-Universität München (LMU), Munich, Germany.
Department of Neurology, University Hospital, LMU, Munich, Germany.
Eur J Neurol. 2022 Oct;29(10):3092-3101. doi: 10.1111/ene.15448. Epub 2022 Jun 29.
Vertigo and dizziness are common complaints in emergency departments and primary care, and pose major diagnostic challenges due to their various underlying etiologies. Most supportive diagnostic algorithms concentrate on either identifying cerebrovascular events (CVEs) or diagnosing specific vestibular disorders or are restricted to specific patient subgroups. The aim of the present study was to develop and validate a comprehenisve algorithm for identifying patients with CVE and classifying the most common vestibular disorders.
The study was conducted within the scope of the "PoiSe" project (Prevention, Online feedback, and Interdisciplinary Therapy of Acute Vestibular Syndromes by e-health). A three-level algorithm was developed according to international guidelines and scientific evidence, addressing both the detection of CVEs and the classification of non-vascular vestibular disorders (unilateral vestibulopathy, benign paroxysmal positional vertigo, vestibular paroxysmia, Menière's disease, vestibular migraine, functional dizziness). The algorithm was validated in a prospectively collected dataset of 407 patients with acute vertigo and dizziness presenting to the Emergency Department at the Ludwig-Maximilian University of Munich.
The algorithm assigned 287 of 407 patients to the correct diagnosis, corresponding to an overall accuracy of 71%. CVEs were identified with high sensitivity of 94%. The six most common vestibular disorders were classified with high specificity, above 95%. Random forest identified presence of a paresis, sensory loss, central ocular motor and vestibular signs (HINTS [head impulse test, nystagmus assessment, and test of skew deviation]), and older age as the most important variables indicating a cerebrovascular event.
The proposed diagnostic algorithm can correctly classify the most common vestibular disorders based on a comprehensive set of key questions and clinical examinations. It is easily applied, not limited to subgroups, and might therefore be transferred to broad clinical settings such as primary healthcare.
眩晕和头晕是急诊科和初级保健中常见的主诉，由于其各种潜在病因，诊断具有很大挑战性。大多数支持性诊断算法主要集中于识别脑血管事件（CVE）或诊断特定的前庭疾病，或者仅限于特定的患者亚组。本研究旨在开发和验证一种全面的算法，用于识别 CVE 患者并对最常见的前庭疾病进行分类。
本研究是在“PoiSe”项目（通过电子健康预防、在线反馈和急性前庭综合征的跨学科治疗）的范围内进行的。根据国际指南和科学证据制定了一个三级算法，用于检测 CVE 和分类非血管性前庭疾病（单侧前庭病、良性阵发性位置性眩晕、前庭阵发性疾病、梅尼埃病、前庭性偏头痛、功能性头晕）。该算法在慕尼黑路德维希-马克西米利安大学急诊科就诊的 407 例急性眩晕和头晕患者的前瞻性采集数据集中进行了验证。
该算法将 407 例患者中的 287 例分配到正确的诊断，总体准确率为 71%。CVE 的识别具有 94%的高灵敏度。六种最常见的前庭疾病的分类具有 95%以上的高特异性。随机森林确定存在无力、感觉丧失、中枢性眼球运动和前庭体征（HINTS[头脉冲试验、眼震评估和偏斜试验]）以及年龄较大是提示 CVE 的最重要变量。
所提出的诊断算法可以根据一组全面的关键问题和临床检查正确分类最常见的前庭疾病。它易于应用，不受亚组限制，因此可以转移到广泛的临床环境，如初级保健。
