RESUMO
BACKGROUND: Metastatic castrate sensitive prostate cancer (mCSPC) is a heterogeneous disease state with variable prognosis. Although several life-prolonging systemic agents are available, there is no robust multivariable model to predict prognosis and improve risk stratification in mCSPC. The objective of this study was to build and validate a multivariable prognostic model to predict overall survival (OS) in mCSPC. METHODS: We used data from LATITUDE, a phase III randomized controlled trial in which men with de novo mCSPC were randomly allocated to either ADT plus abiraterone or ADT with placebo. Patients with non-missing data (n = 1,058) were randomly split in a 70:30 ratio to training (n = 743) and testing (n = 315) sets. Elastic net regression was used for variable selection. A multivariable Cox regression model for OS was then fitted using the selected variables. The predictive accuracy of the model was assessed on the testing set using the time-dependent area under curve (tAUC) with bootstrapped confidence intervals [CI] primarily for OS and secondarily for radiographic progression-free survival (rPFS). RESULTS: The 11 prognostic variables in the final model were performance status, number of skeletal metastases, Gleason score, presence of liver metastasis, worst pain score, albumin, lactate dehydrogenase, prostate-specific antigen, hemoglobin, and treatment regimen. The tAUC for predicting OS at 2- and 3-years was 0.74 (95% CI, 0.67-0.80) and 0.72 (95% CI, 0.65-0.77), respectively. The tAUC for rPFS at 2- and 3-years was 0.72 (95% CI, 0.65-0.77) and 0.77 (95% CI, 0.70-0.82), respectively. CONCLUSIONS: A prognostic model for men with de novo mCSPC was developed and validated in an independent testing set. Our model had high accuracy for predicting OS and rPFS. The model includes commonly used clinical and laboratory parameters and can guide risk stratification of these patients for participation in future trials.