The utility of a prognostic index for predicting time to first treatment in early XXXX : the GIMEMA experience.

@entity1 @entity1072
BACKGROUND: A prognostic index based on widely available clinical and laboratory features was recently proposed to predict survival in @entity1 with previously untreated @entity1072 . We assessed the utility of this index for predicting time to first treatment in early @entity1072 . DESIGN AND METHODS: An observational database of the GIMEMA (Gruppo Italiano Malattie EMatologiche dell'Adulto), which included 310 @entity1 with newly diagnosed Binet stage A @entity1072 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 @entity1 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 @entity1 were younger (P<0.0001) and had more advanced disease (P<0.0001) than those in the current investigation, which studied community-based @entity1 whose data were recorded at presentation. With this in mind, we used an optimal cut-off search to determine how best to split @entity1 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 @entity1 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 @entity1 with early stage @entity1072 at presentation. Our effort to develop a revised scoring method meets the need to separate Binet stage A @entity1 into different prognostic groups in order to devise individualized and tailored follow-up during the treatment-free period.
