(1) Background: Patients with vestibular migraine (VM) often present with positional vertigo. A portion of these patients have features of benign paroxysmal positional vertigo (BPPV). It is a challenge to rapidly identify the BPPV component of VM associated with positional vertigo. (2) Methods: Retrospective data collected from 60 VM and 47 VM + BPPV patients were used to build a diagnostic model, and then prospective data from 47 patients were used for the external validation. All patients had VM manifesting as positional vertigo, with or without accompanying BPPV. The clinical manifestations and the results of vestibular function tests were comprehensively analyzed using logistic regression. (3) Results: The univariate and multivariate analyses showed that the age, symptom duration, tinnitus, ear fullness, nausea, head shaking nystagmus, the direction of the Dix–Hallpike and roll tests, and horizontal gain could help differentiate between the two groups. A nomogram and an online calculator were generated. The C-index was 0.870. The diagnostic model showed good discriminative power and calibration performance during internal and external validation. (4) Conclusions: This study provided a new perspective for diagnosing VM with positional vertigo by identifying the BPPV component and, for the first time, offers a prediction model integrating multiple predictors.

(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^{−(−2.269× X5 − 2.395× X13 + 2.141× X18 + 3.949 × X23 + 2.798× X27 − 4.275× X30(1) − 5.811× X30(2) + 0.873)}] (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.