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Updated: Mar 11, 2026
Purification of Platelets from Mouse Blood
Published on: May 7, 2019
Plateletpheresis efficiency and mathematical correction of software-derived platelet yield prediction: A linear
José Carlos Jaime-Pérez1, Raúl Alberto Jiménez-Castillo1, Karina Elizabeth Vázquez-Hernández1
1Department of Hematology, Dr. José Eleuterio González University Hospital of the School of Medicine of the Universidad Autónoma de Nuevo León, Monterrey, México.
Background:
Advances in automated cell separators have improved the efficiency of plateletpheresis and the possibility of obtaining double products (DP). We assessed cell processor accuracy of predicted platelet (PLT) yields with the goal of a better prediction of DP collections.
Study Design And Methods:
This retrospective proof-of-concept study included 302 plateletpheresis procedures performed on a Trima Accel v6.0 at the apheresis unit of a hematology department. Donor variables, software predicted yield and actual PLT yield were statistically evaluated. Software prediction was optimized by linear regression analysis and its optimal cut-off to obtain a DP assessed by receiver operating characteristic curve (ROC) modeling.
Results:
Three hundred and two plateletpheresis procedures were performed; in 271 (89.7%) occasions, donors were men and in 31 (10.3%) women. Pre-donation PLT count had the best direct correlation with actual PLT yield (r = 0.486. P < .001). Means of software machine-derived values differed significantly from actual PLT yield, 4.72 × 1011 vs.6.12 × 1011 , respectively, (P < .001). The following equation was developed to adjust these values: actual PLT yield= 0.221 + (1.254 × theoretical platelet yield). ROC curve model showed an optimal apheresis device software prediction cut-off of 4.65 × 1011 to obtain a DP, with a sensitivity of 82.2%, specificity of 93.3%, and an area under the curve (AUC) of 0.909.
Conclusion:
Trima Accel v6.0 software consistently underestimated PLT yields. Simple correction derived from linear regression analysis accurately corrected this underestimation and ROC analysis identified a precise cut-off to reliably predict a DP.
