BACKGROUND: Mobilization and homing of PBPCs are still poorly understood. Thus, a sufficient algorithm for the prediction of PBPC yield in apheresis procedures does not yet exist. STUDY DESIGN AND METHODS: The decline of CD34+ cells in the peripheral blood during apheresis and their simultaneous increase in the collection bag were determined in a prospective study of 18 consecutive apheresis procedures. A cell-kinetic, four-compartment model describing these changes was developed. Retrospective data from 136 apheresis procedures served to further improve this model. A predictive algorithm for the yield was developed that considered the sex, weight, and height of the patient, the number of CD34+ cells in peripheral blood before apheresis, the inlet flow, and the duration of the apheresis. The accuracy of this algorithm was evaluated by comparison of the predicted and the observed yields of CD34+ cells in 105 prospective autologous and 148 retrospective allogeneic apheresis procedures. RESULTS: The correlation between predicted and observed yields was good for the autologous and allogeneic groups with a correlation coefficient (r) of 0.8979 and 0.8311 (p<0.0001), respectively. The regression is described by the equations log (measured value [m]) = 1.0118 + 0.8595 x log (predicted value [p]) for the autologous and log (m) = 2.226 + 0.7559 x log (p) for the allogeneic group. The respective equations for the zero-point regression are log (m) = 1.014 x log (p) and log (m) = 1.026 x log (p). The probability that the measured value was 90 percent or more of the predicted value was 83.8 percent for the autologous and 90.5 percent for the allogeneic apheresis procedures. CONCLUSION: The predictive accuracy of the algorithm and the slope of the zero-point regression curve were higher for allogeneic than autologous PBPC collections. The predictive algorithm may be a useful tool in PBPC harvest, enabling the adaptation of the size of the apheresis to the needs of each patient.
BACKGROUND: Mobilization and homing of PBPCs are still poorly understood. Thus, a sufficient algorithm for the prediction of PBPC yield in apheresis procedures does not yet exist. STUDY DESIGN AND METHODS: The decline of CD34+ cells in the peripheral blood during apheresis and their simultaneous increase in the collection bag were determined in a prospective study of 18 consecutive apheresis procedures. A cell-kinetic, four-compartment model describing these changes was developed. Retrospective data from 136 apheresis procedures served to further improve this model. A predictive algorithm for the yield was developed that considered the sex, weight, and height of the patient, the number of CD34+ cells in peripheral blood before apheresis, the inlet flow, and the duration of the apheresis. The accuracy of this algorithm was evaluated by comparison of the predicted and the observed yields of CD34+ cells in 105 prospective autologous and 148 retrospective allogeneic apheresis procedures. RESULTS: The correlation between predicted and observed yields was good for the autologous and allogeneic groups with a correlation coefficient (r) of 0.8979 and 0.8311 (p<0.0001), respectively. The regression is described by the equations log (measured value [m]) = 1.0118 + 0.8595 x log (predicted value [p]) for the autologous and log (m) = 2.226 + 0.7559 x log (p) for the allogeneic group. The respective equations for the zero-point regression are log (m) = 1.014 x log (p) and log (m) = 1.026 x log (p). The probability that the measured value was 90 percent or more of the predicted value was 83.8 percent for the autologous and 90.5 percent for the allogeneic apheresis procedures. CONCLUSION: The predictive accuracy of the algorithm and the slope of the zero-point regression curve were higher for allogeneic than autologous PBPC collections. The predictive algorithm may be a useful tool in PBPC harvest, enabling the adaptation of the size of the apheresis to the needs of each patient.
Authors: Andreas Humpe; Ute Buwitt-Beckmann; Natalie Schub; Martin Gramatzki; Andreas Günther Journal: Transfus Med Hemother Date: 2013-07-25 Impact factor: 3.747
Authors: Jack W Hsu; Bronwen E Shaw; Soyoung Kim; Brent R Logan; Jennifer A Sees; Dennis L Confer; Michael A Pulsipher; Nirali Shah; Galen E Switzer; Muneer H Abidi; Ibrahim A Ahmed; Paulo N Anderlini; Christopher Bredeson; Saurabh Chhabra; Christopher E Dandoy; Miguel Angel Diaz; Nosha Farhadfar; Siddhartha Ganguly; Usama Gergis; Gregory A Hale; Peiman Hematti; Rammurti T Kamble; Kimberly A Kasow; Hillard M Lazarus; Jane L Liesveld; Hemant S Murthy; Richard F Olsson; Bipin N Savani; Raquel Schears; Sachiko Seo; Melhern Solh; Thomas Spitzer; Amir Steinberg; Michele Sugrue; Phyllis Warkentin; John R Wingard Journal: Biol Blood Marrow Transplant Date: 2020-02-20 Impact factor: 5.742