Site-specific evaluation of prognostic factors on survival in Iranian colorectal cancer patients: a competing risks survival analysis
- PMID: 20104971
Site-specific evaluation of prognostic factors on survival in Iranian colorectal cancer patients: a competing risks survival analysis
Abstract
Background: Colorectal cancer (CRC) is one of the most malignant cancers, but prognosis varies in different parts of the world. Knowing the prognostic factors of the cancer is clinically important for prognosis and treatment application objectives. However, evaluation of these factors overall does not provide thorough understanding of the cancer. Therefore, this study aimed to evaluate prognostic factors of colon and rectal cancers site-specifically, via a competing risks survival analysis with colon and rectum as competing causes of death.
Methods: A total of 1,219 patients with CRC diagnosis according to the pathology reports of our cancer registry, from 1 January 2002 to 1 October 2007, were entered into the study. Demographic and clinicopathological factors with regard to survival of patients were analyzed using univariate and multivariate competing risks survival analysis, utilizing STATA statistical software.
Results: The results of univariate analysis showed that gender, body mass index (BMI), alcohol history, inflammatory bowel disease (IBD), tumor size, tumor grade and pathologic stage were significantly associated with colon cancer and BMI, personal history of cancer, pathologic stage and the kind of first treatment used were significantly related to rectal cancer. In the multivariate analysis, BMI, IBD, tumor grade and pathologic stage of the cancer were significant prognostic factors for colon cancer and BMI and the kind of first treatment used were significant prognostic factors of rectal cancer. Also 1, 2, 3, 4 and 5 year and overall adjusted survival of patients with rectal cancer was better than those of colon cancer.
Conclusion: Based on our findings, CRC is not a single entity and its sub-sites should be evaluated separately to reveal hidden associations which may not be revealed under general modeling.
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