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A machine-learning model that incorporates CD45 surface expression predicts hematopoietic progenitor cell recovery after freeze-thaw.
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.
Afiliação
  • Al-Riyami AZ; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Clinical Pathology & Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA; Department of Hematology,
  • Maryamchik E; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Clinical Pathology & Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA; Department of Laboratory
  • Hanna RS; Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Pashmineh Azar AR; Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Zheng X; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Choudhari S; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Finn C; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Giacobbe N; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Machietto R; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Rieser R; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Ghasemi Tahrir F; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Zhang X; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Kadauke S; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Clinical Pathology & Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Wang Y; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Clinical Pathology & Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA. Electronic address: WangY
Cytotherapy ; 25(10): 1048-1056, 2023 10.
Article em En | MEDLINE | ID: mdl-37318396
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Células-Tronco Hematopoéticas / Transplante de Células-Tronco Hematopoéticas Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Child / Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Células-Tronco Hematopoéticas / Transplante de Células-Tronco Hematopoéticas Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Child / Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article