Predicting High-Value Care Outcomes Following Surgery for Skull Base Meningiomas

Abstract Background Though various predictors of adverse postoperative outcomes among meningioma patients have been established, research has yet to develop a method for consolidating these findings to allow for predictions of adverse healthcare outcomes for patient diagnosed with skull base meningiomas. The objective of the present study was to develop three predictive algorithms that can be used to estimate an individual patient’s probability of extended length of hospital stay (LOS), experiencing a nonroutine discharge disposition, or incurring high hospital charges following surgical resection of a skull base meningioma. Methods The present study utilized data from patients who underwent surgical resection for skull base meningiomas at a single academic institution between 2017-2019. Multivariate logistic regression analysis was used to predict extended LOS, nonroutine discharge, and high hospital charges, and 2000 bootstrapped samples were used to calculate an optimism-corrected c-statistic. The Hosmer-Lemeshow test was used to assess model calibration, and p Results A total of 245 patients were included in our analysis. Our cohort was majority female (77.6%) and Caucasian (62.4%). Our models predicting extended LOS, nonroutine discharge, and high hospital charges had optimism-corrected c-statistics of 0.768, 0.784, and 0.783, respectively. All models demonstrated adequate calibration (p>0.05), and were deployed an open-access, online calculator: https://neurooncsurgery3.shinyapps.io/high_value_skull_base_calc/ . Conclusion Following external validation, our predictive models have the potential to aid clinicians in providing patients with individualized risk-estimation for healthcare outcomes following meningioma surgery.