Between-hospital variation in mortality and survival after glioblastoma surgery in the Dutch Quality Registry for Neuro Surgery

Abstract

Purpose: Standards for surgical decisions are unavailable, hence treatment decisions can be personalized, but also introduce variation in treatment and outcome. National registrations seek to monitor healthcare quality. The goal of the study is to measure between-hospital variation in risk-standardized survival outcome after glioblastoma surgery and to explore the association between survival and hospital characteristics in conjunction with patient-related risk factors.

Methods: Data of 2,409 adults with first-time glioblastoma surgery at 14 hospitals were obtained from a comprehensive, prospective population-based Quality Registry Neuro Surgery in The Netherlands between 2011 and 2014. We compared the observed survival with patient-specific risk-standardized expected early (30-day) mortality and late (2-year) survival, based on age, performance, and treatment year. We analyzed funnel plots, logistic regression and proportional hazards models.

Results: Overall 30-day mortality was 5.2% and overall 2-year survival was 13.5%. Median survival varied between 4.8 and 14.9 months among hospitals, and biopsy percentages ranged between 16 and 73%. One hospital had lower than expected early mortality, and four hospitals had lower than expected late survival. Higher case volume was related with lower early mortality (P = 0.031). Patient-related risk factors (lower age; better performance; more recent years of treatment) were significantly associated with longer overall survival. Of the hospital characteristics, longer overall survival was associated with lower biopsy percentage (HR 2.09, 1.34-3.26, P = 0.001), and not with academic setting, nor with case volume.

Conclusions: Hospitals vary more in late survival than early mortality after glioblastoma surgery. Widely varying biopsy percentages indicate treatment variation. Patient-related factors have a stronger association with overall survival than hospital-related factors.

Keywords: Glioblastoma; Mortality; Neurosurgical procedures; Outcome assessment; Quality of health care; Survival.

Fig. 1

Survival outcome over months as Kaplan–Meier curves per hospital in colors and overall survival function in black based on the Cox regression model with risk-standardization for age, performance, and treatment year. The hospital identifications correspond with Table 1

Fig. 2

Hospital characteristics versus survival outcome. Plots of a case volume in 4 years versus observed early mortality percentage, b volume versus observed late survival percentage, c biopsy percentage versus observed early mortality percentage, and d biopsy percentage versus observed late survival percentage. Black circles indicate hospitals with an academic setting. The overall outcome percentages are represented by dotted lines. Logistic regression lines are drawn, significant association estimates in black, non-significant estimates in grey

Fig. 3

Multipanel plot of expected numbers of early deaths versus risk-standardized mortality ratios (a), expected numbers of late survivors versus risk-standardized late survival ratios (b), and combination plot of risk-standardized early mortality versus late survival ratios on log scales (c). The solid funnels are 95% control limits, the dotted funnels 99% control limits. Black dots indicate hospitals with ratios outside the 95% control limits. Better than expected early mortality and late survival is shown in green, worse than expected is shown in red. The institutional identifications are printed in the circles; sizes correspond with the case volumes according to the legend. Note that hospital i has 0 observed late survivors and 0.89 expected late survivors, which therefore is outside the plot below the lower 99% control limit and in the red region