Development and validation of a prognostic model for recurrent glioblastoma patients treated with bevacizumab and irinotecan

Background Predictive markers and prognostic models are required in order to individualize treatment of recurrent glioblastoma (GBM) patients. Here, we sought to identify clinical factors able to predict response and survival in recurrent GBM patients treated with bevacizumab (BEV) and irinotecan. Material and methods A total of 219 recurrent GBM patients treated with BEV plus irinotecan according to a previously published treatment protocol were included in the initial population. Prognostic models were generated by means of multivariate logistic and Cox regression analysis. Results In multivariate analysis, corticosteroid use had a negative predictive impact on response at first evaluation (OR 0.45; 95% CI 0.22-0.93; p = 0.03) and at best response (OR 0.51; 95% CI 0.26-1.02; p = 0.056). Three significant (p < 0.05) prognostic factors associated with reduced progression-free survival and overall survival (OS) were identified. These factors were included in the final model for OS, namely corticosteroid use (HR 1.70; 95% CI 1.18-2.45; p = 0.004), neurocognitive deficit (HR 1.40; 95% CI 1.04-1.89; p = 0.03) and multifocal disease (HR 1.56; 95% CI 1.15-2.11; p < 0.0001). Based on these results a prognostic index able to calculate the probability for OS at 6 and 12 months for the individual patient was established. The predictive value of the model for OS was validated in a separate patient cohort of 85 patients. Discussion and conclusion A prognostic model for OS was established and validated. This model can be used by physicians to risk stratify the individual patient and together with the patient decide whether to initiate BEV relapse treatment.