Conditional median survival of patients with advanced carcinoma: surveillance, epidemiology, and end results data

Abstract

Background: Conditional survival is clinically useful, particularly for patients with malignant disease who have a poor prognosis. However, there are no published data on the conditional median survival of patients with advanced carcinoma on a population basis.

Methods: Data on 217,573 patients with breast, colorectal, lung, or prostate carcinoma who were newly diagnosed with distant disease between 1973 and 1995 and who were followed through the end of 1997 were extracted from the Surveillance, Epidemiology, and End Results (SEER) data base of the National Cancer Institute. The Kaplan-Meier method was employed to estimate conditional median survival and 95% confidence intervals at 0-5 years after the initial diagnosis.

Results: The conditional median survival increased as time elapsed after the initial diagnosis. The increase was slowest and almost leveled off among patients with prostate carcinoma. The median survival of patients with breast carcinoma increased relatively linearly with time, i.e., 5-6 months per year. Conversely, there was a rapid increase in the conditional median survival according to the amount of time since diagnosis for patients with lung and colorectal carcinoma. The trend was most pronounced for patients with colorectal carcinoma. At 5 years after the initial diagnosis, the remaining median survival was longest for patients with colorectal carcinoma, almost 6 years (71.5 months), followed by patients with lung carcinoma (52.5 months), breast carcinoma (42.5 months), and prostate carcinoma (34.5 months). Although race was a correlate with initial survival, gender and age had more impact on late conditional survival.

Conclusions: The conditional median survival provides useful and encouraging information for patients who survive with advanced disease and for healthcare professionals who treat these patients. However, the information should be used carefully, taking the limitations of these data into account.