BACKGROUND AIMS: The ability to predict how many CD34(+) cells a donor will collect on a given day is vital for efficient leukapheresis. METHODS: We validated a formula to predict daily CD34(+) cell collections by leukapheresis, calculated as follows: (peripheral blood CD34(+) cells/L) × (adjusted collection efficiency of 30%)/body weight (kg), multiplied by the number of liters processed. This validation was performed from 234 donors undergoing 30 L large volume leukapheresis (LVL) and 162 donors undergoing smaller collections (non-LVL). The LVL group consisted of 811 collection events (625 multiple myeloma, 186 non-myeloma). The non-LVL group consisted of 224 collection events (196 multiple myeloma, 28 non-myeloma). All predicted and observed CD34(+) cell collection numbers were plotted (predicted versus observed) and assessed using linear regression analyses. Linear correlation coefficients (r-values), slopes and intercepts of the regression lines were evaluated. RESULTS: Predicted versus observed data points across all quantities of CD34(+) cells/kg collected by both LVL and non-LVL had strong r-values of 0.947 and 0.913, respectively, demonstrating near perfect positive linear correlations. Data for LVL collections subgrouped by number of cells collected (poor, intermediate and good), mobilization regimen, collection day and diagnosis were analyzed the same way and showed consistent findings. CONCLUSIONS: We have validated a formula with a strong ability to predict collection of CD34(+) cells/kg that would allow for individualization of collection for any donor once the peripheral blood CD34(+) cell count and optimal goal of collection were known; to date this has not been published by other groups.
BACKGROUND AIMS: The ability to predict how many CD34(+) cells a donor will collect on a given day is vital for efficient leukapheresis. METHODS: We validated a formula to predict daily CD34(+) cell collections by leukapheresis, calculated as follows: (peripheral blood CD34(+) cells/L) × (adjusted collection efficiency of 30%)/body weight (kg), multiplied by the number of liters processed. This validation was performed from 234 donors undergoing 30 L large volume leukapheresis (LVL) and 162 donors undergoing smaller collections (non-LVL). The LVL group consisted of 811 collection events (625 multiple myeloma, 186 non-myeloma). The non-LVL group consisted of 224 collection events (196 multiple myeloma, 28 non-myeloma). All predicted and observed CD34(+) cell collection numbers were plotted (predicted versus observed) and assessed using linear regression analyses. Linear correlation coefficients (r-values), slopes and intercepts of the regression lines were evaluated. RESULTS: Predicted versus observed data points across all quantities of CD34(+) cells/kg collected by both LVL and non-LVL had strong r-values of 0.947 and 0.913, respectively, demonstrating near perfect positive linear correlations. Data for LVL collections subgrouped by number of cells collected (poor, intermediate and good), mobilization regimen, collection day and diagnosis were analyzed the same way and showed consistent findings. CONCLUSIONS: We have validated a formula with a strong ability to predict collection of CD34(+) cells/kg that would allow for individualization of collection for any donor once the peripheral blood CD34(+) cell count and optimal goal of collection were known; to date this has not been published by other groups.
Authors: Erica B Esrick; John P Manis; Heather Daley; Cristina Baricordi; Hélène Trébéden-Negre; Francis J Pierciey; Myriam Armant; Sarah Nikiforow; Matthew M Heeney; Wendy B London; Luca Biasco; Mohammed Asmal; David A Williams; Alessandra Biffi Journal: Blood Adv Date: 2018-10-09
Authors: X Papanikolaou; E R Rosenbaum; L N Tyler; J Sawyer; C J Heuck; B Barlogie; M Cottler-Fox Journal: Leukemia Date: 2013-07-15 Impact factor: 11.528
Authors: Marc-Andrea Baertsch; Katharina Kriegsmann; Petra Pavel; Thomas Bruckner; Michael Hundemer; Mark Kriegsmann; Anthony D Ho; Hartmut Goldschmidt; Patrick Wuchter Journal: Transfus Med Hemother Date: 2017-10-04 Impact factor: 3.747
Authors: Jeremy Pantin; Enkhtsetseg Purev; Xin Tian; Lisa Cook; Theresa Donohue-Jerussi; Elena Cho; Robert Reger; Matthew Hsieh; Hanh Khuu; Gary Calandra; Nancy L Geller; Richard W Childs Journal: Haematologica Date: 2016-10-20 Impact factor: 9.941