Clinical determinants of survival in patients with 5-fluorouracil-based treatment for metastatic colorectal cancer: results of a multivariate analysis of 3825 patients.
Ann Oncol 2002;
13:308-17. [PMID:
11886010 DOI:
10.1093/annonc/mdf034]
[Citation(s) in RCA: 369] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND
Patients with metastatic colorectal cancer are usually offered systemic chemotherapy as palliative treatment. A multivariate analysis was performed in order to identify predictors and their constellation that allow a valid prediction of the outcome in patients treated with 5-fluorouracil (5-FU)-based therapy.
PATIENTS AND METHODS
A total of 3825 patients treated with 5-FU within 19 prospective randomised and three phase II trials were separated into learning (n = 2549) and validation (n = 1276) samples. Data were analysed by tree analysis using the recursive partition and amalgamation method (RECPAM). A predictor could only enter the RECPAM analysis if the number of patients with missing values was < 33.3% within a node, and the minimal node size was set to 50 patients. Twenty-three potential predictors were grouped into subsets of laboratory variables (11 parameters), tumour-related variables (seven parameters) and clinical variables (five parameters). In the first step, tree analysis was performed separately for each predictor subset. The selected prognostic parameters of the resulting partial models (the 'winners') were entered into the general model. The classification rule from the data of the learning set was applied to the independent validation set.
RESULTS
Winners of the subgroup analysis for laboratory variables were: platelets > or = 400 x 10(9)/l, alkaline phosphatase > or = 300 U/l, white blood cell (WBC) count > or = 10 x 10(9)/l and haemoglobin < 11 x 10(9)/l, and all predicted a worse outcome. Negative predictors within the subgroup of tumour parameters were: number of tumour sites more than one or more than two, presence of liver metastases or peritoneal carcinomatosis, which predicted a worse outcome. Furthermore, presence of lung metastases, a primary rectal cancer and presence of lymph node metastases all predicted a better outcome in the multivariate setting. Among the clinical parameters only performance status of ECOG 0 or 1 predicted better outcome. In the final regression tree, three risk groups could be identified: low risk group (n = 1111) with a median survival of 15 months for patients with ECOG 0/1 and only one tumour site; intermediate risk group (n = 904) with a median survival of 10.7 months for patients with ECOG 0/1 and more than one tumour site and alkaline phosphatase < 300 U/l or patients with ECOG > 1, WBC count < 10 x 10(9)/l and only one tumour site; high risk group (n = 534) with a median survival of 6.1 months for patients with ECOG 0/1 and more than one tumour site and alkaline phosphatase of > or = 300 U/l or patients with ECOG > 1 and more than one tumour site or WBC count > 10 x 10(9)/l. The median survival times for the good, intermediate and high risk groups in the validation sample were 14.7, 10.5 and 6.4 months, respectively.
CONCLUSIONS
Patients can be divided into at least three risk groups depending on the four baseline clinical parameters: performance status, WBC count, alkaline phosphatase and number of metastatic sites. Any molecular or biological marker should be validated against these clinical parameters and decisions for more or less intensive treatments may be studied separately in these three risk groups. Also, clinical trials should be stratified according to the three risk groups.
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