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Intra-apheresis CD34+ cell count: A dynamic approach to predicting peripheral blood stem cell collection yield.

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CD34+ cell yield among healthy donors: Large-scale model development and validation.

Abdullah Alswied1, David Daniel1, Leonard N Chen1

  • 1Department of Transfusion Medicine, National Institutes of Health (NIH) Clinical Center, NIH, Bethesda, Maryland, USA.

Journal of Clinical Apheresis
|June 26, 2024
PubMed
Summary

Accurate prediction of CD34+ cell yield is vital for hematopoietic stem cell transplantation success. New models, particularly logistic regression, offer improved accuracy for optimizing apheresis collection and patient outcomes.

Keywords:
CD34+ cell calculatorCD34+ cell preharvest prediction toolCD34+ cell yield optimizationhematopoietic progenitor cell apheresismachine learning in apheresis

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Area of Science:

  • Hematology
  • Transplantation Immunology
  • Biostatistics

Background:

  • Hematopoietic stem cell transplantation (HSCT) requires adequate CD34+ cell doses for successful engraftment.
  • Current CD34+ cell yield prediction models lack reproducibility, impacting apheresis optimization.
  • Developing reliable models is crucial for improving HSCT clinical outcomes.

Purpose of the Study:

  • To develop and validate advanced predictive models for CD34+ cell yield estimation.
  • To enhance the accuracy of predicting apheresis collection efficiency.
  • To optimize the HSCT collection process and improve patient outcomes.

Main Methods:

  • Secondary analysis of a large dataset (over 17,000 donors) from the Center for International Blood and Marrow Transplant Research database.
  • Utilized filgrastim-mobilized hematopoietic progenitor cell apheresis data.
  • Employed linear regression, gradient boosting regressor, and logistic regression classification models.

Main Results:

  • Key predictors for CD34+ cell yield include pre-apheresis count, weight, age, sex, and blood volume processed.
  • Logistic regression achieved high predictive accuracy (96% at 200 × 10^6 threshold) and AUC scores (0.90-0.93).
  • Gradient boosting and linear regression models showed strong correlations (r=0.82 and r=0.81, respectively).

Conclusions:

  • Advanced predictive models, especially logistic regression, significantly improve CD34+ cell yield estimation.
  • These models offer practical utility for optimizing apheresis collection in HSCT.
  • Enhanced prediction accuracy can lead to better HSCT success rates and clinical outcomes.