Cause-Specific Survival After Meningioma Surgery: A Nationwide Population-Based Competing Risk Study

Abstract

Survival after meningioma surgery often is reported with inadequate allowance for competing causes of death. We processed the French Système National des Données de Santé database using an algorithm combining the type of surgical procedure and the International Classification of Diseases to retrieve appropriate cases of meningiomas. The cumulative incidence of meningioma-related death was the primary end point. A competing risk analysis was performed to identify factors associated with meningioma-specific death of patients who underwent meningioma surgery. The risk of meningioma-related death at 1, 2, and 3 years respectively was 2.4%, 95% confidence interval [CI] 2-2.7; 3%, 95% CI 2.6-3.4; and 3.1%, 95% CI 2.7-3.6. In the adjusted Fine-Gray competing risk regression for meningioma cause-specific survival, age at surgery (subdistribution hazard ratio [SHR] 1.07, 95% CI 1.05-1.09, P < 0.001), mortality-related morbidity index (SHR 1.68, 95% CI 1.07-2.63, P= 0.025), expenditure-related morbidity index (SHR 1.06, 95% CI 1.03-1.09, P < 0.001), spinal location (SHR 0.2, 95% CI 0.08-0.47, P < 0.001), cerebrospinal fluid shunt (SHR 3.13, 95% CI 1.9-5.16, P < 0.001), grade (SHR 1.88, 95% CI 1.13-3.14, P= 0.015) redo surgery for recurrence (SHR 1.6, 95% CI 1.01-2.51, P= 0.043), and progressing meningioma (SHR 2.87, 95% CI 1.23-6.68, P= 0.015) were established as independent prognostic factors of meningioma-related death. Cause-specific survival after meningioma surgery is greater in younger, low-comorbidity adults with spinal and benign meningioma. Those with an intracranial, progressing malignant tumor requiring cerebrospinal fluid shunting and having a severe global health-state have a significant increased risk of meningioma-related death. Redo surgery failed to improve the outcome. We recommend the use of competing risk model in meningioma studies in which unrelated mortality may be substantial, as this approach results in more accurate estimates of disease risk and associated predictors.