A pooled analysis was performed to examine the impact of pretreatment factors on overall survival (OS) and time to progression (TTP) in patients with advanced-stage nonsmall cell lung cancer (NSCLC) and to construct a prediction equation for OS using pretreatment factors.METHODS.
A pooled data set of 1053 patients from 9 North Central Cancer Treatment Group trials was used. Age, gender, Eastern Cooperative Oncology Group performance status (PS), tumor stage (Stage IIIB vs. Stage IV), body mass index (BMI), creatinine level, hemoglobin (Hgb) level, white blood cell (WBC) count, and platelet count were evaluated for their prognostic significance in both univariate and multivariate analyses by using a Cox proportional-hazards model.RESULTS.
Patients who had high WBC counts, low Hgb levels, PS >0, BMI <18.5 kg/m2, and TNM Stage IV disease had significantly worse TTP and OS. Patients who had Stage IV disease with a high WBC count had a particularly poor prognosis. An equation to predict the OS of patients with Stage IV NSCLC based on pretreatment PS, BMI, Hgb level, and WBC count was constructed.CONCLUSIONS.
In addition to the widely accepted prognostic factors of PS, BMI, and disease stage, both of the readily available laboratory parameters of Hgb level and WBC count were found to be significant prognostic factors for OS and TTP in patients with advanced-stage NSCLC. The authors' prediction equation can be used to evaluate the benefit of a treatment in Phase II trials by comparing the observed survival of a cohort with its expected survival by using the patients' own prognostic factors in place of comparisons with historic data that may have substantially different baseline patient characteristics.CONCLUSIONS.
In addition to the widely accepted prognostic factors of performance status, body mass index, and stage, pretreatment blood counts were significant prognostic factors for overall survival and time to progression in patients with advanced-stage nonsmall cell lung cancer. The authors developed a prediction equation based on the patients' own prognostic factors that may be used to evaluate the benefit of a treatment in Phase II trials by comparing the observed survival to the expected survival of a cohort in place of historic comparisons, which may have substantially different baseline patient characteristics. The usefulness of this work to clinical research and clinical practice is compelling in this population, in which advances in treatment have been slow, producing only modest improvements in survival.