Investigating Predictors of Increased Length of Stay After Resection of Vestibular Schwannoma Using Machine Learning.
To evaluate the predictors of prolonged length of stay (LOS) after vestibular schwannoma resection.Retrospective chart review.Tertiary referral center.Patients who underwent vestibular schwannoma resection between 2008 and 2019.Variables of interest included age, body mass index, comorbidities, symptoms, previous intervention, microsurgical approach, extent of resection, operative time, preoperative tumor volume, and postoperative complications. Predictive modeling was done through multivariable linear regression and random forest models with 80% of patients used for model training and the remaining 20% used for performance testing.LOS was evaluated as the number of days from surgery to discharge.Four hundred one cases from 2008 to 2019 were included with a mean LOS of 3.0 (IQR = 3.0-4.0). Postoperatively, 14 (3.5%) of patients had LOS greater than two standard deviations from the mean (11 days). In a multivariate linear regression model (adjusted R2 = 0.22; p