Author(s): Hasford J, Pfirrmann M, Hehlmann R, Allan NC, Baccarani M,
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Abstract BACKGROUND: Interferon alfa is a conservative and widely used alternative to bone marrow transplantation in treatment of patients with early chronic myeloid leukemia (CML). A meta-analysis was conducted to develop a reliable prognostic scoring system for estimation of survival of patients with CML treated with interferon alfa. METHODS: Patients treated in prospective studies, including major randomized trials, were separated into learning and validation samples. Cox regression analysis and the minimum P-value approach were used to identify prognostic factors for patient survival and to discover groups in the learning sample with the greatest differences in survival. These findings were then validated by applying the new scoring system to patients in the validation sample. RESULTS: We collected data on 1573 patients who were participants in 14 studies involving 12 institutions; 1303 patients (learning sample, n = 981; validation sample, n = 322) were eligible for inclusion in this analysis, and their median survival time was 69 months (range, 1-117 months). Because two previously described prognostic scoring systems failed to discriminate risk groups satisfactorily, we developed a new scoring system that utilizes the following covariates: age, spleen size, blast count, platelet count, eosinophil count, and basophil count. Among 908 patients with complete data in the learning sample, three distinct risk groups were identified (median survival times of 98 months [n = 369; 40.6\%], 65 months [n = 406; 44.7\%], or 42 months [n = 133;14.6\%]; two-sided logrank test, P< or =.0001). The ability of the new scoring system to discriminate these risk groups was confirmed by analysis of 285 patients with complete data in the validation sample (two-sided logrank test, P = .0002). CONCLUSIONS: A new prognostic scoring system for estimating survival of patients with CML treated with interferon alfa has been developed and validated through use of a large dataset.
This article was published in J Natl Cancer Inst
and referenced in Journal of Leukemia