| Abstract: |
Purpose: To identify prognostic factors and a model predictive for survival in patients with metastatic renal-cell carcinoma (RCC). Patients and Methods: The relationship between pretreatment clinical features and survival was studied in 670 patients with advanced RCC treated in 24 Memorial Sloan- Kettering Cancer Center clinical trials between 1975 and 1996. Clinical features were first examined univariately. A stepwise modeling approach based on Cox proportional hazards regression was then used to form a multivariate model. The predictive performance of the model was internally validated through a two-step nonparametric bootstrapping process. Results: The median survival time was 10 months (95% confidence interval [CI], 9 to 11 months). Fifty-seven of 670 patients remain alive, and the median follow-up time for survivors was 33 months. Pretreatment features associated with a shorter survival in the multivariate analysis were low Karnofsky performance status (<80%), high serum lactate dehydrogenase (> 1.5 times upper limit of normal), low hemoglobin (< lower limit of normal), high 'corrected' serum calcium (> 10 mg/dL), and absence of prior nephrectomy. These were used as risk factors to categorize patients into three different groups. The median time to death in the 25% of patients with zero risk factors (favorable-risk) was 20 months. Fifty-three percent of the patients had one or two risk factors (intermediate-risk), and the median survival time in this group was 10 months. Patients with three or more risk factors (poor-risk), who comprised 22% of the patients, had a median survival time of 4 months. Conclusions: Five prognostic factors for predicting survival were identified and used to categorize patients with metastatic RCC into three risk groups, for which the median survival times were separated by 6 months or more. These risk categories can be used in clinical trial design and interpretation and in patient management. The low long-term survival rate emphasizes the priority of clinical investigation to identify more effective therapy. |
