A machine-learning model that incorporates CD45 surface expression predicts hematopoietic progenitor cell recovery after freeze–thaw

Sufficient doses of viable CD34+ (vCD34) hematopoietic progenitor cells (HPCs) are crucial for engraftment. Additional-day apheresis collections can compensate for potential loss during cryopreservation but incur high cost and additional risk. To aid predicting such losses for clinical decision supp...

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Bibliographic Details
Published in:	Cytotherapy (Oxford, England) Vol. 25; no. 10; pp. 1048 - 1056
Main Authors:	Al-Riyami, Arwa Z., Maryamchik, Elena, Hanna, Richard S., Pashmineh Azar, Amir Reza, Zheng, Xingwu, Choudhari, Shilpa, Finn, Colleen, Giacobbe, Nicholas, Machietto, Rene, Rieser, Robert, Ghasemi Tahrir, Farzaneh, Zhang, Xiaoyong, Kadauke, Stephan, Wang, Yongping
Format:	Journal Article
Language:	English
Published:	England Elsevier Inc 01-10-2023
Subjects:	CD34 engraftment hematopoietic progenitor cells machine learning predictive modeling engraftment hematopoietic progenitor cells machine learning CD34 predictive modeling
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Summary:	Sufficient doses of viable CD34+ (vCD34) hematopoietic progenitor cells (HPCs) are crucial for engraftment. Additional-day apheresis collections can compensate for potential loss during cryopreservation but incur high cost and additional risk. To aid predicting such losses for clinical decision support, we developed a machine-learning model using variables obtainable on the day of collection. In total, 370 consecutive autologous HPCs, apheresis-collected since 2014 at the Children's Hospital of Philadelphia, were retrospectively reviewed. Flow cytometry was used to assess vCD34% on fresh products and thawed quality control vials. The ratio of vCD34% thawed to fresh, which we call “post-thaw index,” was used as an outcome measure, with a “poor” post-thaw index defined as <70%. HPC CD45 normalized mean fluorescence intensity (MFI) was calculated by dividing CD45 MFI of HPCs to the CD45 MFI of lymphocytes in the same sample. We trained XGBoost, k-nearest neighbor and random forest models for the prediction and calibrated the best model to minimize falsely-reassuring predictions. In total, 63 of 370 (17%) products had a poor post-thaw index. The best model was XGBoost, with an area under the receiver operator curve of 0.83 evaluated on an independent test data set. The most important predictor for a poor post-thaw index was the HPC CD45 normalized MFI. Transplants after 2015, based on the lower of the two vCD34% values, showed faster engraftment than older transplants, which were based on fresh vCD34% only (average 10.6 vs 11.7 days, P = 0.0006). Transplants taking into account post-thaw vCD34% improved engraftment time in our patients; however, it came at the cost of unnecessary multi-day collections. The results from applying our predictive algorithm retrospectively to our data suggest that more than one-third of additional-day collections could have been avoided. Our investigation also identified CD45 nMFI as a novel marker for assessing HPC health post-thaw.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1465-3249 1477-2566
DOI:	10.1016/j.jcyt.2023.05.007