Development of a Predictive Model for Persistent Dizziness Following Vestibular Schwannoma Surgery

Objective: In an era of vestibular schwannoma (VS) surgery where functional preservation is increasingly emphasized, persistent postoperative dizziness is a relatively understudied functional outcome. The primary objective was to develop a predictive model to identify patients at risk for developing persistent postoperative dizziness after VS resection. Methods: Retrospective review of patients who underwent VS surgery at our institution with a minimum of 12 months of postoperative follow-up. Demographic, tumor-specific, preoperative, and immediate postoperative features were collected as predictors. The primary outcome was self-reported dizziness at 3-, 6-, and 12-month follow-up. Binary and multiclass machine learning classification models were developed using these features. Results: A total of 1,137 cases were used for modeling. The median age was 67 years, and 54% were female. Median tumor size was 2 cm, and the most common approach was suboccipital (85%). Overall, 63% of patients did not report postoperative dizziness at any timepoint; 11% at 3- month follow-up; 9% at 6-months; and 17% at 12-months. Both binary and multiclass models achieved high performance with AUCs of 0.89 and 0.86 respectively. Features important to model predictions were preoperative headache, need for physical therapy on discharge, vitamin D deficiency, and systemic comorbidities. Conclusion: We demonstrate the feasibility of a machine learning approach to predict persistent dizziness following vestibular schwannoma surgery with high accuracy. These models could be used to provide quantitative estimates of risk, helping counsel patients on what to expect after surgery and manage patients proactively in the postoperative setting.