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J Neurooncol ; 135(3): 529-534, 2017 Dec.
Article in English | MEDLINE | ID: mdl-28836140

ABSTRACT

The relation between hospital volume and outcomes for patients with glioblastoma is unknown. We undertook this study to determine the effect of hospital volume on treatment received and its effect on survival in patients with glioblastoma. We included patients from the National Cancer Database diagnosed with a glioblastoma from 2006 to 2013. Hospital volume was calculated by examining the treating facilities average number of cases per year and grouping them into tertiles: (low < 9.25, medium 9.26-23.88, and high ≥ 23.39). Treatment was defined as receiving any type of therapeutic surgery, radiation or chemotherapy. Using regression models we examined the relation between hospital volume to treatment received and survival with adjustment for clinical, socioeconomic and institutional factors. The study included 68,726 patients of which 91.8% received treatment. Among patients diagnosed at low volume facilities, 90.1% received treatment versus 94.2% in high volume facilities (p < 0.0001). Compared to low volume centers, the odds ratio of receiving any treatment was 1.01 (CI 95% CI: 0.95-1.09) and 1.43 (95% CI: 1.31-1.55) for medium volume and high volume facilities, respectively. On multivariate analysis for survival among those who received treatment, the hazard of mortality was decreased at high volume (HR 0.92, 95% CI 0.89-0.94) facilities compared to low volume facilities. Patients diagnosed with glioblastoma at a high volume facility (≥23.39 cases per year) have an increased likelihood of receiving treatment. Furthermore, glioblastoma patients may significantly improve their survival by choosing to receive care at a high-volume hospital.


Subject(s)
Brain Neoplasms/mortality , Brain Neoplasms/therapy , Glioblastoma/mortality , Glioblastoma/therapy , Hospitals, High-Volume , Hospitals, Low-Volume , Aged , Cohort Studies , Comorbidity , Female , Functional Laterality , Humans , Kaplan-Meier Estimate , Male , Middle Aged , Multivariate Analysis , Proportional Hazards Models , Registries , Regression Analysis , Socioeconomic Factors , Time Factors , United States
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