Liu Dan, Guo Zhaoqi, Wang Jun, Tian E, Chen Jingyu, Zhou Liuqing, Kong Weijia, Zhang Sulin
Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Institute of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
J Clin Med. 2022 Aug 14;11(16):4745. doi: 10.3390/jcm11164745.
(1) Background: Vestibular migraine (VM) and Meniere's disease (MD) share multiple features in terms of clinical presentations and auditory-vestibular dysfunctions, e.g., vertigo, hearing loss, and headache. Therefore, differentiation between VM and MD is of great significance. (2) Methods: We retrospectively analyzed the medical records of 110 patients with VM and 110 patients with MD. We at first established a regression equation by using logistic regression analysis. Furthermore, sensitivity, specificity, accuracy, positive predicted value (PV), and negative PV of screened parameters were assessed and intuitively displayed by receiver operating characteristic curve (ROC curve). Then, two visualization tools, i.e., nomograph and applet, were established for convenience of clinicians. Furthermore, other patients with VM or MD were recruited to validate the power of the equation by ROC curve and the Gruppo Italiano per la Valutazione degli Interventi in Terapia Intensiva (GiViTI) calibration belt. (3) Results: The clinical manifestations and auditory-vestibular functions could help differentiate VM from MD, including attack frequency (X5), phonophobia (X13), electrocochleogram (ECochG) (X18), head-shaking test (HST) (X23), ocular vestibular evoked myogenic potential (o-VEMP) (X27), and horizontal gain of vestibular autorotation test (VAT) (X30). On the basis of statistically significant parameters screened by Chi-square test and multivariable double logistic regression analysis, we established a regression equation: P = 1/[1 + e] (P, predictive value; e, natural logarithm). Nomographs and applets were used to visualize our result. After validation, the prediction model showed good discriminative power and calibrating power. (4) Conclusions: Our study suggested that a diagnostic algorithm based on available clinical features and an auditory-vestibular function regression equation is clinically effective and feasible as a differentiating tool and could improve the differential diagnosis between VM and MD.
(1) 背景：前庭性偏头痛（VM）和梅尼埃病（MD）在临床表现和听觉 - 前庭功能障碍方面有多个共同特征，如眩晕、听力损失和头痛。因此，VM与MD的鉴别具有重要意义。(2) 方法：我们回顾性分析了110例VM患者和110例MD患者的病历。首先，通过逻辑回归分析建立回归方程。此外，评估筛选参数的敏感性、特异性、准确性、阳性预测值（PV）和阴性PV，并通过受试者工作特征曲线（ROC曲线）直观显示。然后，为方便临床医生，建立了两种可视化工具，即列线图和小程序。此外，招募其他VM或MD患者，通过ROC曲线和意大利重症治疗评估小组（GiViTI）校准带验证方程的效能。(3) 结果：临床表现和听觉 - 前庭功能有助于区分VM和MD，包括发作频率（X5）、恐声症（X13）、耳蜗电图（ECochG）（X18）、摇头试验（HST）（X23）、眼前庭诱发肌源性电位（o - VEMP）（X27）和前庭自动旋转试验（VAT）的水平增益（X30）。基于通过卡方检验和多变量双逻辑回归分析筛选出的具有统计学意义的参数，我们建立了一个回归方程：P = 1/[1 + e]（P，预测值；e，自然对数）。使用列线图和小程序将我们的结果可视化。验证后，预测模型显示出良好的鉴别能力和校准能力。(4) 结论：我们的研究表明，基于可用临床特征和听觉 - 前庭功能回归方程的诊断算法作为一种鉴别工具在临床上是有效且可行的，并且可以改善VM和MD之间的鉴别诊断。
