Abstract Number: PB0522
Meeting: ISTH 2020 Congress
Background: On the conventional internal quality control (IQC) management approach, it is not easy to deal with two major practical issues that may jeopardize the analytic quality of the results: long-term revision of control chart parameters, and managing reagent batch change-over and the accompanying recalibration of control chart target values.
Aims: The present study seeks to demonstrate the reliability of using three Bayesian tools in long-term IQC management with respect to ISO 15189 standard requirements for optimal patient care. Alongside the predictive control chart (PCC), previously described, two other tools are used to detect any insidious persistent analytic trends during both preliminary and long-term IQC management phases.
Methods: These Bayesian tools utilize the CUSUM (cumulative sum), which, much like the EWMA (exponentially weighted moving average), is a powerful tool for detecting trends. The Location Predictive Residual CUSUM (Loc PRC) and the Scale Predictive Residual CUSUM (Scale PRC) are dedicated to detecting even small persistent shifts in systematic and random error, respectively.
Results: Figure 1 shows PCC functioning under Loc PRC control regarding shift in control chart target values and under Scale PRC control for shift in method variance. Figure 2 shows the same controls during reagent batch changeover.
Conclusions: The association of these 3 Bayesian tools enables efficient longitudinal management of IQC results, avoiding the need for a preliminary phase in IQC reagent batch changeover. Bayesian inference, which considers the target value as a random variable, also avoids having to revise parameters on the control chart.
To cite this abstract in AMA style:Sobas F, Bourazas K, Geay MO, Beghin M, Jousselme E, Nougier C, Tsiamyrtzis P. Long-Term Internal Quality Control Management: The Interest of Bayesian Inference [abstract]. Res Pract Thromb Haemost. 2020; 4 (Suppl 1). https://abstracts.isth.org/abstract/long-term-internal-quality-control-management-the-interest-of-bayesian-inference/. Accessed February 27, 2024.
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