Decision-tree algorithm for optimized hematopoietic progenitor cell-based predictions in peripheral blood stem cell mobilization.

Abstract

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. 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. 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. 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.