Validation of the graded prognostic assessment and recursive partitioning analysis as prognostic tools using a modern cohort of patients with brain metastases

Background: Prognostic indices for patients with brain metastases (BM) are needed to individualize treatment and stratify clinical trials. Two frequently used tools to estimate survival in patients with BM are the recursive partitioning analysis (RPA) and the diagnosis-specific graded prognostic assessment (DS-GPA). Given recent advances in therapies and improved survival for patients with BM, this study aims to validate and analyze these 2 models in a modern cohort.

Methods: Patients diagnosed with BM were identified via our institution's Tumor Board meetings. Data were retrospectively collected from the date of diagnosis with BM. The concordance of the RPA and GPA was calculated using Harrell's C index. A Cox proportional hazards model with backwards elimination was used to generate a parsimonious model predictive of survival.

Results: Our study consisted of 206 patients diagnosed with BM between 2010 and 2019. The RPA had a prediction performance characterized by Harrell's C index of 0.588. The DS-GPA demonstrated a Harrell's C index of 0.630. A Cox proportional hazards model assessing the effect of age, presence of lung, or liver metastases, and Eastern Cooperative Oncology Group (ECOG) performance status score of 3/4 on survival yielded a Harrell's C index of 0.616. Revising the analysis with an uncategorized ECOG demonstrated a C index of 0.648.

Conclusions: We found that the performance of the RPA remains unchanged from previous validation studies a decade earlier. The DS-GPA outperformed the RPA in predicting overall survival in our modern cohort. Analyzing variables shared by the RPA and DS-GPA produced a model that performed analogously to the DS-GPA.