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. 2025 Oct 24.
doi: 10.1002/acn3.70213. Online ahead of print.

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

Guo Wenting  1 , Du Liping  2 , Yang Yi  3 , Hu Jin  4 , Ye Bijun  5 , Wang Jianer  6 , Liu Shuangsi  6 , Guo Shunyuan  1 , Cai Xiaofeng  1 , Wang Huiyuan  1 , Wu Bin  7 , Yu Xiang  8 , Shi Tianming  1
Affiliations
Free article

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

Guo Wenting et al. Ann Clin Transl Neurol. .
Free article

Abstract

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

Methods: 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.

Results: 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).

Conclusion: 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.

Keywords: acute vestibular syndrome; etiology; predictive model.

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References

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