The utility of a prognostic index for predicting time to first treatment in early chronic lymphocytic leukemia: the GIMEMA experience

Abstract

Background: A prognostic index based on widely available clinical and laboratory features was recently proposed to predict survival in patients with previously untreated chronic lymphocytic leukemia. We assessed the utility of this index for predicting time to first treatment in early chronic lymphocytic leukemia.

Design and methods: An observational database of the GIMEMA (Gruppo Italiano Malattie EMatologiche dell'Adulto), which included 310 patients with newly diagnosed Binet stage A chronic lymphocytic leukemia who were observed at different primary hematology centers during the period 1991 - 2000, was used for the purpose of this study.

Results: The new prognostic index enabled Binet stage A patients to be divided into two subgroups that differed with respect to time to first treatment (P=0.003). The original prognostic index was derived from a database that included cases observed at a reference academic center; these patients were younger (P<0.0001) and had more advanced disease (P<0.0001) than those in the current investigation, which studied community-based patients whose data were recorded at presentation. With this in mind, we used an optimal cut-off search to determine how best to split patients with Binet stage A disease into different prognostic groups. According to the recursive partitioning (RPART) model, a classification tree was built that identified three subsets of patients who scores were 0-2 (low risk), 3-4 (intermediate risk) and 5-7 (high risk). The probability of remaining free from therapy at 5 years was 100% in the low risk group, 81.2% in the intermediate risk group and 61.3% in the high risk group (P<0.0001).

Conclusions: The results of this study confirm the utility of a new prognostic index for predicting time to first treatment in a large sample series of community-based patients with early stage chronic lymphocytic leukemia at presentation. Our effort to develop a revised scoring method meets the need to separate Binet stage A patients into different prognostic groups in order to devise individualized and tailored follow-up during the treatment-free period.

Figure 1.

Kaplan-Meier estimate of the time to first treatment of 310 patients with Binet stage A CLL.

Figure 2.

Kaplan-Meier estimate of the time to first treatment of Binet stage A patients stratified according to the median value obtained by the M.D. Anderson Cancer Center nomogram score.

Figure 3.

Kaplan-Meier estimate of the time to first treatment by prognostic index category in patients with Binet stage A CLL.

Figure 4.

Classification tree built according to recursive partitioning (RPART). The number of patients who required therapy in each group is shown first and then the total number of patients. RR indicates the relative risk.

Figure 5.

Kaplan-Meier estimate of the time to first treatment of Binet stage A patients. According to recursive partitioning (RPART) analysis patients were segregated into three risk categories: low-risk (score, 0–2), intermediate-risk (score, 3–4) and high-risk (score, 5–7).