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Predicting High-Value Care Outcomes After Surgery for Skull Base Meningiomas
Adrian E Jimenez1, Adham M Khalafallah1, Shravika Lam1
1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
World Neurosurgery
|February 10, 2021
Summary
New predictive models estimate risks for extended hospital stays, nonroutine discharge, and high costs after skull base meningioma surgery. These tools aid clinicians in personalized patient outcome predictions.
Area of Science:
- Neurosurgery
- Oncology
- Health Services Research
Background:
- Predicting adverse outcomes after meningioma surgery is crucial, especially for skull base tumors.
- Existing research lacks consolidated methods for predicting health care outcomes in skull base meningioma patients.
Purpose of the Study:
- To develop three predictive algorithms for extended length of stay (LOS), nonroutine discharge, and high hospital charges.
- To provide individualized risk estimation for patients undergoing surgical resection of skull base meningiomas.
Main Methods:
- Utilized data from 245 patients who underwent surgical resection for skull base meningiomas (2017-2019).
- Employed multivariate logistic regression and bootstrapping for model development and validation.
- Assessed model calibration using the Hosmer-Lemeshow test.
Main Results:
- Developed models for extended LOS, nonroutine discharge, and high hospital charges with optimism-corrected C-statistics of 0.768, 0.784, and 0.783, respectively.
- All models demonstrated adequate calibration (P>0.05).
- Models are accessible via an open-access online calculator.
Conclusions:
- The developed predictive models can assist clinicians in providing individualized risk assessments.
- These tools have the potential to improve patient care and resource management after meningioma surgery.
- External validation is recommended to further confirm the utility of these predictive algorithms.
