Assessment of software-derived predictive algorithms for platelet yield and blood cell count after apheresis.

Introduction: Algorithms have been developed to predict the platelet yield after apheresis from the donor's data, as well as the effect on the blood cell count, to extract an acceptable platelet number without affecting the donor. However, the evaluation of these algorithms has not been widely reported. This study aimed to assess the accuracy of the predictive algorithms of the Trima Accel v. 6 blood collection system.

Methods: Platelet concentrates (PCs) obtained by apheresis were analyzed. Platelet count and hematocrit were compared pre- and post-apheresis. Calculated post-apheresis platelet count (CPAPC), hematocrit (CPAH), and platelet yield (CPY), and their actual values were correlated. The bias of the algorithms was assessed with Bland-Altman plots, and the prediction of the extraction of single or double platelet products was evaluated.

Results: Two hundred and seventy-nine PCs were analyzed. Post-apheresis platelet count (PAPC) and hematocrit were decreased. A moderate correlation was observed between CPY and the actual yield, with a negative bias, and a trend to increase alongside the magnitude of the measurements. CPAPC and CPAH were strongly correlated with their actual values without bias. Prediction of single or double platelet product extraction showed a significant agreement with the actual outcomes.

Conclusions: The predictive algorithm for the platelet yield showed bias, and a trend to underestimate the actual platelet yields when they are higher. The algorithms for the prediction of the PAPC and hematocrit did not show bias, proving their accuracy. Prediction of a single or double platelet product extraction has a strong agreement with the APY.