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Cancer. 2006 Feb 1;106(3):623-30.

Prognosis in transplant-eligible patients with agnogenic myeloid metaplasia: a simple CBC-based scoring system.

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1
Division of Hematology, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA.

Abstract

BACKGROUND:

Allogeneic hematopoietic stem cell transplantation is potentially curative in agnogenic myeloid metaplasia (AMM) but is associated with substantial mortality and morbidity that necessitates accurate identification of patients in whom benefit outweighs risk. The current study describes the natural history of AMM in transplant-eligible patients and proposes a new prognostic scoring system that favorably compares with other established models.

METHODS:

Patients diagnosed with AMM before the age of 60 years and seen at Mayo Clinic were identified and the diagnosis confirmed. Relevant demographic, clinical, and laboratory characteristics were abstracted, and the impact of various parameters on overall survival (OS) was evaluated with univariate and multivariate analyses.

RESULTS:

A cohort of 160 patients with AMM is described. OS was 78 months. Multivariate analysis identified a hemoglobin level of <10 g/dL, white blood cell count of either <4 or >30x10(9)/L, platelet count of <100x10(9)/L, presence of constitutional symptoms, and hepatomegaly as independent predictors of inferior survival. The first 3 complete blood count-based parameters were combined into a new scoring system that resulted in median survivals of 155, 69, and 24 months in the presence of 0, 1, or >or=2 adverse features. The chi-square value for the new model was 80.6 compared with 51.4, 48.4, and 43.7 for the models by Dupriez, Cervantes, and Visani, respectively.

CONCLUSIONS:

A new scoring system based on blood count at the time of diagnosis can adequately stratify by risk transplant-eligible patients with AMM and can accurately identify high-risk as well as intermediate-risk disease. The new system displayed a stronger discriminative value, between risk categories, compared with currently existing prognostic models.

PMID:
16369987
DOI:
10.1002/cncr.21644
[Indexed for MEDLINE]
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