04-01-2025 | Glioblastoma | Research
Creating a predictive model and online calculator for high-value care outcomes following glioblastoma resection: incorporating neighborhood socioeconomic status index
Published in: Journal of Neuro-Oncology
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Purpose
Social determinants of health including neighborhood socioeconomic status, have been established to play a profound role in overall access to care and outcomes in numerous specialized disease entities. To provide glioblastoma multiforme (GBM) patients with high-quality care, it is crucial to identify predictors of hospital length of stay (LOS), discharge disposition, and access to postoperative adjuvant chemoradiation. In this study, we incorporate a novel neighborhood socioeconomic status index (NSES) and develop three predictive algorithms for assessing post-operative outcomes in GBM patients, offering a tool for preoperative risk stratification of GBM patients.
Methods
Adult GBM patients who underwent surgical resection from a single center were identified; NSES was identified via patient street address of residence, with lower scores representing disadvantaged neighborhoods. Multivariate logistic regression analysis was used to predict high value care outcomes. The Hosmer–Lemeshow test was used to assess model calibration.
Results
A total of 467 patients were included, with a mean age of 59.85 ± 13.21 years and 58.7% being male. The mean NSES for our cohort was 63.77 ± 14.91, indicating that the majority resided in neighborhoods with a higher socioeconomic status compared to the national average NSES of 50. One hundred nine (23.3%) patients had extended LOS, 28.9% had non-routine discharge, and 19.1% did not follow the Stupp protocol following surgery. On multivariate regression, worse NSES was significantly and independently associated with extended LOS (OR = 0.981, p = 0.026), non-routine discharge disposition (OR = 0.984, p = 0.033), and non-compliance with the Stupp protocol (OR = 0.977, p = 0.014). Our three models predicting high-value care outcomes had acceptable C-statistics > 0.70, and all models demonstrated adequate calibration (p > 0.05). Final models are accessible via online calculator.
Conclusion
NSES scores are readily available and may be utilized via our open-access calculators. After external validation, our predictive models have the potential to assist in providing patients with individualized risk estimates for post-operative outcomes following GBM resection.
