1. Predicting Stage Progression in Binet Stage a Chronic Lymphocytic Leukemia.
- Author
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Alshemmari SH, Almazyad M, Alsarraf A, Kunhikrishnan A, Isaac AM, and Kaempf A
- Subjects
- Humans, Middle Aged, Retrospective Studies, Mutation, Prognosis, Leukemia, Lymphocytic, Chronic, B-Cell therapy
- Abstract
Introduction: The variable clinical course of chronic lymphocytic leukemia (CLL) and the lack of consensus on follow-up and treatment strategies have necessitated a prognostic model for identifying high-risk patients at the time of diagnosis., Methods: We involved a retrospective analysis of demographic and clinical characteristics of 212 patients diagnosed with Binet stage A CLL and thus eligible for risk stratification by both the International Prognostic Score for Early-stage CLL (IPS-E) and the alternative IPS-E (AIPS-E). We evaluated the applicability of these prognostic indices in our young, Middle Eastern cohort (median age 59 at diagnosis)., Results: During the study period with a median follow-up of 3.5 years, 67 patients (32 %) experienced progression to first treatment and cumulative incidence of treatment was 13 % at 1 year and 28 % at 3 years after diagnosis. Sixty-nine (51 % of the 136 with a known value) patients harbored an unmutated immunoglobulin heavy chain gene (IGHV) and 21 (10 %) an 11q or 17p deletion with 11 % lacking FISH results. For each early-stage CLL prognostic index, more patients were identified as high-risk for disease progression (51 % of 124 patients evaluable for IPS-E; 42 % of 109 patients evaluable for AIPS-E) than intermediate-risk and low-risk. Multivariable models involving the IPS-E and AIPS-E components revealed that unmutated IGHV and elevated absolute lymphocyte count were significant predictors of earlier treatment requirement. Both prognostic scores were discriminative of time to first treatment (log-rank p < 0.001; c-statistics of 0.74 for IPS-E and 0.69 for AIPS-E)., Conclusion: Although clarity on clinical behavior with regard to initiation of treatment remains elusive, IPS-E and AIPS-E are valuable tools for identifying high-risk patients.
- Published
- 2024
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