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Isolation of Precursor B-cell Subsets from Umbilical Cord Blood
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Predictive analytics and cord blood banking: toward utilization-based unit selection.

Liam A Wynn1, Alejandro Madrigal2

  • 1Anthony Nolan Cell Therapy Centre, Nottingham, UK.

Cytotherapy
|March 19, 2021
PubMed
Summary

A new multi-parameter linear regression (MLR) model accurately identifies cord blood units (CBUs) likely to process poorly, improving graft selection for hematopoietic stem cell transplantation (HSCT). This enhances banked graft inventory quality without extra cost.

Keywords:
SepaxTNC recoverycord blood bankingmanufacturingutilization

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Area of Science:

  • Hematopoietic Stem Cell Transplantation
  • Cord Blood Banking
  • Cell Therapy

Background:

  • Total nucleated cell (TNC) and CD34+ cell doses are critical for cord blood unit (CBU) suitability in allogeneic hematopoietic stem cell transplantation (HSCT).
  • Current selection methods based on pre-process TNC content often result in CBUs failing to meet post-process quality criteria due to cell loss during processing.
  • This leads to reduced graft selection by transplant centers and inefficient use of resources.

Purpose of the Study:

  • To develop and validate a multi-parameter linear regression (MLR) model for predicting poor-processing CBUs.
  • To improve the selection efficacy of CBUs for cryopreservation.
  • To enhance the quality of banked graft inventory for HSCT.

Main Methods:

  • A multi-parameter linear regression (MLR) model was trained using eight pre-process predictors, including red blood cell (RBC) content, CBU volume, CBU age, and TNC constituent cell subsets.
  • The model's selection efficacy was compared to conventional methods using receiver operating characteristic (ROC) analysis and mean inventory quality analysis.
  • Historically processed CBUs were graded (A+ to D) to examine utilization rates of each grade category.

Main Results:

  • The MLR model significantly improved the pre-process identification of 'D' grade CBUs compared to CD34+ and TNC content thresholds (P < 0.0001).
  • At a comparable financial investment, the MLR model resulted in a banked graft inventory with significantly higher quality (mean increase of +8.8% for CD34+ and +9.9% for TNC, P < 0.010).
  • Poor-processing 'D' grade CBUs represented 37% of processing expenditures but provided only 11% of grafts shipped for HSCT.

Conclusions:

  • Predictive modeling for pre-process CBU selection is an effective strategy to enhance graft inventory quality.
  • This approach can potentially increase future graft utilization for HSCT.
  • The MLR model offers a simple and cost-effective means to improve CBU selection without additional financial investment.