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

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Automated counting of hematopoietic progenitor cells (HPCs) can predict successful stem cell harvests. A classification and regression tree (CART) model using HPC counts and patient factors improves prediction accuracy.

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Area of Science:

  • Hematology
  • Stem Cell Transplantation
  • Oncology

Background:

  • Automated enumeration of hematopoietic progenitor cells (HPCs) offers a rapid, cost-effective method for predicting successful peripheral blood stem cell (PBSC) harvests.
  • Current methods lack defined optimal HPC cutoff counts and predictive factors for improved accuracy.

Purpose of the Study:

  • To determine the optimal hematopoietic progenitor cell (HPC) cutoff count for predicting successful PBSC harvest.
  • To identify patient-specific risk factors that influence mobilization success.
  • To develop an improved predictive model for harvest success.

Main Methods:

  • Retrospective analysis of 189 patients undergoing PBSC harvesting between 2007 and 2012.
  • Identification of risk factors for failed harvest (defined as <2 × 10^6 CD34+ cells/kg) using multivariate logistic regression and correlation analysis.
  • Application of classification and regression tree (CART) analysis to integrate host risk factors and cell counts for predictive modeling.

Main Results:

  • PBSC harvests were successful in 81.5% of patients.
  • Independent predictors of poor mobilization included age ≥60 years, solid tumor diagnosis, ≥5 prior chemotherapy cycles, prior radiotherapy, and specific mobilization agents.
  • A CART model combining host risk factors with HPC (≥28 × 10^6/L) or mononuclear cell (MNC; ≥3.5 × 10^9/L) counts achieved a 92.3% success rate in good mobilizers, versus 30.3% in poor mobilizers.

Conclusions:

  • A CART algorithm integrating patient risk factors and HPC/MNC counts enhances prediction of successful PBSC harvests.
  • This approach may decrease the reliance on monitoring circulating CD34+ cells, streamlining the harvest prediction process.