ArticleJournal of clinical apheresis2024
CD34+ cell yield among healthy donors: Large-scale model development and validation.
Article in Journal of clinical apheresis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine Learning-Based Prediction of Mononuclear Cell Collection Efficiency for CAR-T Manufacturing.Current medical science · 2026Article
- Intra-apheresis CD34+ cell count: A dynamic approach to predicting peripheral blood stem cell collection yield.Transfusion · 2026Article
- Challenges and limitations of mobilization and stem cell collection for gene therapy of sickle cell disease.Blood advances · 2025Review
- Viable and Functional: Long-Term -80 °C Cryopreservation Sustains CD34Journal of clinical medicine · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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 (R
conclusionThis study introduces advanced predictive models for estimating CD34+ cell yield, with the logistic regression classification model demonstrating remarkable accuracy and practical utility.
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