Background: Insufficient data exist to characterize factors associated with longer-Term survival of glioblastoma (GBM). A population-based analysis of GBM longer-Term survivors (LTS) in the United States was conducted to investigate the association between treatment, demographic, surgical factors, and longer-Term survival. Methods: From the National Cancer Database, GBM patients were identified using ICD-O-3 histology codes 9440-9442/3, 2005-2015 and were divided into routine (≤3 years) and longer-Term (>3 years) overall survival (OS) groups. Univariable and multivariable logistic regression analysis was used to assess factors associated with longer-Term survival. A subset analysis was performed to further investigate the association of extent of resection and treatment combinations on OS outcomes. Results: A total of 93 036 patients with GBM met study criteria. Among these patients, 8484 were LTS and 84 552 were routine survivors (RS). When comparing LTS (OS of >3 years) with RS (OS of ≤3 years), younger age, insured status, metro/urban residence, treatment at academic facility, and fewer comorbidities were associated with longer-Term survival. In addition, trimodality therapy (chemotherapy + radiation + surgery) was associated with having best odds of longer-Term survival (odds ratio = 4.89, 95% confidence interval [3.58, 6.68]); 74% of LTS received such therapy compared with 51% of RS. Subset analysis revealed that total resection is only associated with longer-Term survival status for those receiving trimodality therapy or surgery only. Conclusions: In a population-based analysis, standard of care surgery and chemo radiation connote a survival advantage in GBM. Among those receiving standard of care, having a total resection is most beneficial for longer-Term survival status.
CITATION STYLE
Malay, S., Somasundaram, E., Patil, N., Buerki, R., Sloan, A., & Barnholtz-Sloan, J. S. (2020). Treatment and surgical factors associated with longer-Term glioblastoma survival: A National Cancer Database study. Neuro-Oncology Advances, 2. https://doi.org/10.1093/noajnl/vdaa070
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