Decision-tree algorithm for optimized hematopoietic progenitor cell-based predictions in peripheral blood stem cell mobilization.
Transfusion
; 56(8): 2042-51, 2016 08.
Article
em En
| MEDLINE
| ID: mdl-27232662
ABSTRACT
BACKGROUND:
Enumerating hematopoietic progenitor cells (HPCs) by using an automated hematology analyzer is a rapid, inexpensive, and simple method for predicting a successful harvest compared with enumerating circulating CD34+ cells. However, the optimal HPC cutoff count and the indicating factors to be considered for improved predicting have not yet been determined. STUDY DESIGN ANDMETHODS:
Between 2007 and 2012, a total of 189 consecutive patients who proceeded to peripheral blood stem cell (PBSC) harvesting were retrospectively recruited. Baseline characteristics were analyzed to identify the risk factors for a failed harvest, which were defined as less than 2 × 10(6) CD34+ cells/kg. Variables identified by multivariate logistic regression and correlation analysis for predicting a successful harvest were subjected to classification and regression tree (CART) analysis.RESULTS:
PBSCs were successfully harvested in 154 (81.5%) patients. An age of at least 60 years, a diagnosis of a solid tumor, at least five prior chemotherapy cycles, prior radiotherapy, and mobilization with granulocyte-colony-stimulating factor alone or high-dose cyclophosphamide were independent baseline predictors of poor mobilization. In CART analysis, patients with zero to two host risk factors and either higher HPC (≥28 × 10(6) /L) or mononuclear cell (MNC; ≥3.5 × 10(9) /L) counts were categorized as good mobilizers and their harvest success rate was 92.3%. By contrast, 30.3% of harvests were adequate in the patients with three to five host risk factors and lower HPC and MNC counts.CONCLUSION:
A CART algorithm incorporating host predictors and HPC and MNC counts improves predictions in a successful harvest and might reduce the necessity of monitoring peripheral CD34+ cells.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Árvores de Decisões
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Mobilização de Células-Tronco Hematopoéticas
Tipo de estudo:
Health_economic_evaluation
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2016
Tipo de documento:
Article