R E Buller1, S Vasilev, P J DiSaia. 1. Department of Obstetrics and Gynecology, University of Iowa College of Medicine, Iowa City, 52242, USA.
Abstract
OBJECTIVE: Our purpose was to determine the importance of the rate of decline of CA 125 relative to conventional prognosticators of ovarian cancer survival to develop a cost effective management algorithm that supports clinical trial research. STUDY DESIGN: By use of a retrospective chart review the slope of the CA 125 exponential regression curve was calculated for 126 women undergoing combination chemotherapy for epithelial ovarian cancer. Univariate and multivariate survival analyses evaluated conventional parameters including age, grade, stage, histologic features, time to initial chemotherapy, dose and treatment intensity, number of cycles to normal CA 125 levels, the intercept from the regression equation, and the slope of the exponential curve. RESULTS: The ideal CA 125 regression rate was calculated at 7.6 days (95% confidence interval 5.9 to 10.7). Univariate analysis determined slope of the CA 125 exponential regression curve (p = 0.0003), number of cycles to normal CA 125 levels (p = 0.0001), residual disease (p = 0.0006), and platinum treatment intensity (p = 0.0001) as the most important predictors of survival. Cox proportional-hazard regression analysis identified slope of the CA 125 exponential regression curve and number of cycles to normal CA 125 levels as the most significant factors for actuarial survival, replacing such conventional parameters as patient age, stage, grade, chemotherapy intensity, and residual disease. None of the factors investigated predicted treatment outcome for patients without residual disease. Multiple linear regression analysis of the slope of the CA 125 exponential regression curve identified intercept of the regression equation, stage, age, and time to initial chemotherapy as important determinants of the slope. CONCLUSION: The slope of the CA 125 exponential regression curve is the single most important prognosticator of actuarial survival for the patient with a CA 125-positive ovarian carcinoma. Treatment algorithms based on this slope may be helpful in developing novel cost-effective clinical trials.
OBJECTIVE: Our purpose was to determine the importance of the rate of decline of CA 125 relative to conventional prognosticators of ovarian cancer survival to develop a cost effective management algorithm that supports clinical trial research. STUDY DESIGN: By use of a retrospective chart review the slope of the CA 125 exponential regression curve was calculated for 126 women undergoing combination chemotherapy for epithelial ovarian cancer. Univariate and multivariate survival analyses evaluated conventional parameters including age, grade, stage, histologic features, time to initial chemotherapy, dose and treatment intensity, number of cycles to normal CA 125 levels, the intercept from the regression equation, and the slope of the exponential curve. RESULTS: The ideal CA 125 regression rate was calculated at 7.6 days (95% confidence interval 5.9 to 10.7). Univariate analysis determined slope of the CA 125 exponential regression curve (p = 0.0003), number of cycles to normal CA 125 levels (p = 0.0001), residual disease (p = 0.0006), and platinum treatment intensity (p = 0.0001) as the most important predictors of survival. Cox proportional-hazard regression analysis identified slope of the CA 125 exponential regression curve and number of cycles to normal CA 125 levels as the most significant factors for actuarial survival, replacing such conventional parameters as patient age, stage, grade, chemotherapy intensity, and residual disease. None of the factors investigated predicted treatment outcome for patients without residual disease. Multiple linear regression analysis of the slope of the CA 125 exponential regression curve identified intercept of the regression equation, stage, age, and time to initial chemotherapy as important determinants of the slope. CONCLUSION: The slope of the CA 125 exponential regression curve is the single most important prognosticator of actuarial survival for the patient with a CA 125-positive ovarian carcinoma. Treatment algorithms based on this slope may be helpful in developing novel cost-effective clinical trials.