ArticleThe Laryngoscope2023
Development of a Predictive Model for Persistent Dizziness Following Vestibular Schwannoma Surgery.
Article in The Laryngoscope, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Vestibular dose predicts toxicity in stereotactic radiosurgery for vestibular schwannomas.Clinical and translational radiation oncology · 2026Article
- [Skull base surgery-where do we stand?]HNO · 2025Review
- Development and application of explainable artificial intelligence using machine learning classification for long-term facial nerve function after vestibular schwannoma surgery.Journal of neuro-oncology · 2025Article
- A Scoping Review on Vestibulopathy After Microsurgical Resection of Vestibular Schwannoma-The Forgotten Symptom.Neurosurgery practice · 2024Review
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Authors and funding
6 authors.
Funding
Abstract
objectiveIn 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.
methodsRetrospective 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.
resultsA 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.
conclusionWe 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. LEVEL OF EVIDENCE: 4 Laryngoscope, 133:3534-3539, 2023.
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