Applying a novel statistical process control model to platelet quality monitoring

• Published in: Transfusion (Philadelphia, Pa.) Pa.), 2002-08, Vol.42 (8), p.1059-1066
• Main Authors: Perrotta, P.L., Ozcan, C., Whitbread, J.A., Finch, S.J.
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Blood Platelets; Hematology; Humans; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Models, Statistical; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Quality Assurance, Health Care - methods; Quality Control
• ISSN: 0041-1132; 1537-2995
• DOI: 10.1046/j.1537-2995.2002.00168.x

BACKGROUND : Many countries are implementing universal WBC reduction of blood components Thus, manufacturing procedures must include QC techniques to detect units that fail to meet established standards. STUDY DESIGN AND METHODS : A statistical process control model, based on the exponentially weighted moving average of the cumulative distribution function (CDF‐EWMA), was developed to detect shifts in a mean and/or variance of a process. The model's parameters (weights) were optimized to maximize detection of an out‐of‐control process while minimizing sensitivity to autocorrelation. Validation was performed using a retrospective set of WBC‐reduction data obtained from a blood bank. The WBC‐reduction process was considered in control when there was 95‐percent confidence that more than 95 percent of platelet concentrates would contain less than 1 × 10 6 WBCs (6.0 log WBC) as required by European standards. A sentry setting of 5.7 log WBCs was used to allow earlier detection of an out‐of‐control process. RESULTS : Graphic output of the CDF‐EWMA model provided a continuous update of the probability that a WBC‐reduction process was in control. Using the validation data, the model showed that the process was in control until Observation 332, at which point residual WBCs per unit increased. However, the first platelet concentrate to exceed specified criteria (Observation 346) occurred after the model detected that the process was out of control, demonstrating the forecasting value of this model. This deviation corresponded to an equipment failure in a single apheresis instrument. The Shewhart and EWMA techniques were similarly able to detect when the process was out of control using the test data. CONCLUSION : As a statistical process control model, the CDF‐EWMA provides real‐time estimation of the fraction of components meeting a regulatory limit. It is capable of detecting developing QC problems before units fail to meet regulatory requirements and is a potential alternative to other QC techniques for monitoring WBC reduction of blood components.