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Published on: February 24, 2023
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An additive score optimized by a genetic learning algorithm predicts readmission risk after glioblastoma resection
Arka N Mallela1, Prateek Agarwal2, Nicholas J Goel2
1Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.
Summary
Thirty-day readmission after glioblastoma surgery is common. Factors like lower KPS and post-op complications increase risk, while MGMT methylation and radiation may decrease it, aiding risk stratification.
Area of Science:
- Neurosurgery
- Oncology
- Health Services Research
Background:
- Thirty-day readmission after glioblastoma (GBM) resection impacts survival and healthcare quality metrics.
- Identifying predictors of readmission is crucial for improving patient outcomes and managing healthcare resources.
Purpose of the Study:
- To identify factors associated with 30-day readmission following glioblastoma resection.
- To develop a simple risk stratification score for predicting glioblastoma readmission.
Main Methods:
- Retrospective analysis of 666 glioblastoma resections (2005-2016).
- Logistic regression and genetic learning algorithms to identify readmission predictors.
- Development of an additive risk score based on significant factors.
Main Results:
- The 30-day readmission rate was 20.3%.
- Factors increasing readmission risk included lower KPS, recurrent resection, surgical-site infection, VTE, VPS, and discharge to rehabilitation.
- MGMT methylation and chemoradiation were associated with decreased readmission risk.
Conclusions:
- A novel Glioblastoma Readmission Risk Score was developed, incorporating BMI, KPS, smoking, complications, MGMT methylation, and radiation.
- This score can stratify readmission risk, aiding clinical decision-making and outcome analysis for glioblastoma patients.
