A machine-learning model that incorporates CD45 surface expression predicts hematopoietic progenitor cell recovery after freeze–thaw Journal Article
| Authors: | Al-Riyami, A. Z.; Maryamchik, E.; Hanna, R. S.; Pashmineh Azar, A. R.; Zheng, X.; Choudhari, S.; Finn, C.; Giacobbe, N.; Machietto, R.; Rieser, R.; Ghasemi Tahrir, F.; Zhang, X.; Kadauke, S.; Wang, Y. |
| Article Title: | A machine-learning model that incorporates CD45 surface expression predicts hematopoietic progenitor cell recovery after freeze–thaw |
| Abstract: | Background aims: 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. Methods: 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. Results: 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). Conclusions: 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. © 2023 International Society for Cell & Gene Therapy |
| Keywords: | engraftment; predictive modeling; hematopoietic progenitor cells; cd34; machine learning |
| Journal Title: | Cytotherapy |
| Volume: | 25 |
| Issue: | 10 |
| ISSN: | 1465-3249 |
| Publisher: | Elsevier Science Ltd. |
| Date Published: | 2023-10-01 |
| Start Page: | 1048 |
| End Page: | 1056 |
| Language: | English |
| DOI: | 10.1016/j.jcyt.2023.05.007 |
| PUBMED: | 37318396 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Article -- Source: Scopus |
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