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

Abstract

BackgroundSuccessful engraftment in hematopoietic stem cell transplantation necessitates the collection of an adequate dose of CD34+ cells. Thus, the precise estimation of CD34+ cells harvested via apheresis is critical. Current CD34+ cell yield prediction models have limited reproducibility. This study aims to develop a more reliable and universally applicable model by utilizing a large dataset, enhancing yield predictions, optimizing the collection process, and improving clinical outcomes.Materials and MethodsA secondary analysis was conducted using the Center for International Blood and Marrow Transplant Research database, involving data from over 17 000 healthy donors who underwent filgrastim‐mobilized hematopoietic progenitor cell apheresis. Linear regression, gradient boosting regressor, and logistic regression classification models were employed to predict CD34+ cell yield.ResultsKey predictors identified include pre‐apheresis CD34+ cell count, weight, age, sex, and blood volume processed. The linear regression model achieved a coefficient of determination (R2) value of 0.66 and a correlation coefficient (r) of 0.81. The gradient boosting regressor model demonstrated marginally improved results with an R2 value of 0.67 and an r value of 0.82. The logistic regression classification model achieved a predictive accuracy of 96% at the 200 × 106 CD34+ cell count threshold. At thresholds of 400, 600, 800, and 1000 × 106 CD34+ cell count, the accuracies were 88%, 83%, 83%, and 88%, respectively. The model demonstrated a high area under the receiver operator curve scores ranging from 0.90 to 0.93.ConclusionThis study introduces advanced predictive models for estimating CD34+ cell yield, with the logistic regression classification model demonstrating remarkable accuracy and practical utility.