Predicting donor-related factors for high platelet yield donations by classification and regression tree analysis

INTRODUCTION: Collecting high-dose (HD) or double-dose (DD) apheresis platelets units from a single collection offers significant benefit by improving inventory logistics and minimizing the cost per unit produced. Platelet collection yield by apheresis is primarily influenced by donor factors, but the cell separator used also affects the collection yield. OBJECTIVES: To predict the cutoff in donor factors resulting in HD and DD platelet collections between Trima/Spectra Optia and MCS+ apheresis equipment using Classification and Regression Trees (CART) analysis. METHODS: High platelet yield collections (target ≥ 4.5 × 10(11) platelets) using MCS+, Trima Accel and Spectra Optia were included. Endpoints were ≥ 6 × 10(11) platelets for DD and ≥ 4.5 to < 6 × 10(11) for HD collections. The CART, a tree building technique, was used to predict the donor factors resulting in high-yield platelet collections in Trima/Spectra Optia and MCS+ equipment by R programming. RESULTS: Out of 1,102 donations, the DDs represented 60% and the HDs, 31%. The Trima/Spectra Optia predicted higher success rates when the donor platelet count was set at ≥ 205 × 103/µl and ≥ 237 × 103/µl for HD and DD collections. The MCS+ predicted better success when the donor platelet count was ≥ 286 × 103/µl for HD and ≥ 384 × 103/µl for DD collections. Increased donor weight helped counter the effects of lower donor platelet counts only for HD collections in both the equipment. CONCLUSIONS: The donor platelet count and weight formed the strongest criteria for predicting high platelet yield donations. Success rates for collecting DD and HD products were higher in the Trima/Spectra Optia, as they require lower donor platelet count and body weight than the MCS+.