An international prognostic index for chronic lymphocytic leukemia (CLL-IPI) that combines genetic, biochemical, and clinical parameters into a prognostic model may be able to discriminate among patients into four subgroups, according to a study published in The Lancet Oncology.
Researchers from The International CLL-IPI working group consortium looked at 3,472 treatment-naïve patients with CLL through a systematic search of the Cochrane Hematological Malignancies Group of MEDLINE, Embase, and Central databases of prospective, clinical phase 2 and 3 trials that were published between January 1950 and December 2010.
They performed a univariate analysis and multivariate analyses using 27 baseline factors, with overall survival as an endpoint. They assessed the discriminatory value using C-statistics and also the validity and reproducibility of the CLL-IPI through subgroup analysis.
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Furthermore, they used two additional datasets from the Mayo Clinic (MAYO cohort) as well as the SCALE Scandinavian population-based case-control study (SCAN cohort) as external-validation datasets.
Among the observed patients, 2,308 were randomly segregated into a training dataset and 1,164 into the internal-validation dataset. Within the training dataset, 5 prognostic factors were identified: TP53 status, IGHV mutational status, serum β2-microglobulin concentration, clinical stage, and age.
Through a weighted grading of the independent factors, the researchers created a prognostic index that identified 4 risk groups (low, intermediate, high, and very high risk) within the training dataset with a significantly different overall survival at 5 years. They confirmed these risk groups in the internal-validation and external-validation datasets.
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“The CLL-IPI will allow a more targeted management of patients with CLL in clinical practice and in trials testing novel drugs,” the authors concluded.
Reference
- The International CLL-IPI working group. An international prognostic index for patients with chronic lymphocytic leukaemia (CLL-IPI): a meta-analysis of individual patient data. [published online ahead of print May 13, 2016.] doi: http://dx.doi.org/10.1016/S1470-2045(16)30029-8.
