Prediction Model for Etiologic Differentiation of Isolated Vestibular Syndrome in Emergency Settings

This study aimed to develop and validate a predictive model for differentiating central from peripheral etiologies in patients with isolated vestibular syndrome (VS).

In this multicenter retrospective cohort study, 506 patients with isolated VS from five hospitals were divided into derivation (n = 301) and validation (n = 205) cohorts. Multivariable logistic regression was performed to determine independent predictors of central VS. These predictors were assigned weights to construct the SAV₃E score. The performance of the SAV₃E was assessed using the area under the curve (AUC), calibration, and decision curve analysis (DCA) and compared with that of the TriAGe+ and STANDING models.

The SAV₃E score incorporated five predictors: absence of vestibular vagal symptoms (OR, 0.233; 95% CI, 0.084-0.648; p = 0.005), prior stroke (OR, 15.204; 95% CI, 4.455-51.884; p < 0.001), ABCD₂ score of 4-7 (OR, 1.903; 95% CI, 1.206-3.004; p = 0.006), central video oculography nystagmus (OR, 38.377; 95% CI, 8.631-170.644; p < 0.001), and positive video head impulse test (OR, 0.078; 95% CI, 0.033-0.188; p < 0.001). It displayed good discriminative performance with AUCs 0.910 and 0.886 in the derivation and validation cohorts, respectively. It outperformed TriAGe+ (AUC: 0.706) and STANDING (AUC: 0.779) models. Furthermore, calibration analysis revealed good model fit across cohorts (Hosmer-Lemeshow test results: derivation cohort, p = 0.899; validation cohort, p = 0.789). DCA confirmed good clinical utility across a wide range of probability thresholds (derivation cohort: 0.01-0.86, validation cohort: 0.01-1.00).

The SAV₃E score is a validated tool aimed at differentiating central versus peripheral VS, with the potential to improve diagnostic accuracy for urgent etiologies such as stroke.