Optimizing moving average control procedures for small-volume laboratories

Abstract

Introduction: Moving average (MA) means calculating the average value from a set of patient results and further using that value for analytical quality control purposes. The aim of this study was to examine whether the selection, optimization and validation of MA procedures can be performed using the already described bias detection simulation method and whether it is possible to select appropriate MA procedures for a laboratory with a small daily testing volume. Materials and methods: The study was done on four analytes: creatinine, potassium, sodium and albumin. All patient results of these tests processed during six months were taken from the laboratory information system. Using the MA Generator software, different MA procedures were analysed. Different inclusion criteria, calculation formulas, batch sizes and weighting factors were tested. Selection of optimal MA procedures was based on their ability to detect simulated biases of different sizes. After optimization, the validation of MA procedures was done. The results were presented by bias detection curves and MA validation charts. Results: Simple MA procedures for albumin and sodium without truncation limits were selected as optimal. Exponentially weighted MA procedures were found optimal for creatinine and potassium, with the upper truncation limits of 150 μmol/L and 6 mmol/L, respectively. Conclusions: It has been experimentally confirmed that it is possible to perform the selection, optimization and validation of MA procedures using the bias detection simulation method. Also, it is possible to define MA procedures optimal for a laboratory with a small daily testing volume.

A study of the moving rate of positive results for use in a patient‐based real‐time quality control program on a procalcitonin point‐of‐care testing analyzer

“…The MR program with simulated optimal parameters could consistently detect positive of 0.06 ng/ml and above, or negative of 0.4 ng/ml and above biases, at an average daily run size of 10. A comparison of the two PBRTQC programs revealed that the MA program rejected as many outliers as possible to narrow the CL and to improve the detection sensitivity, but the increased rejection rate of the data reduced the frequency of calculating patient results, leading to possible delays in rejection and the possibility of not detecting particularly large biases 19,20 . After converting the results into the binary state, the MR program had no TLs and excessive concentrations for judgment, but had higher data utilization than the MA program, so it is very suitable for analyses with small volumes of data such as that in this study.…”

“…A comparison of the two PBRTQC programs revealed that the MA program rejected as many outliers as possible to narrow the CL and to improve the detection sensitivity, but the increased rejection rate of the data reduced the frequency of calculating patient results, leading to possible delays in rejection and the possibility of not detecting particularly large biases. 19 , 20 After converting the results into the binary state, the MR program had no TLs and excessive concentrations for judgment, but had higher data utilization than the MA program, so it is very suitable for analyses with small volumes of data such as that in this study. Furthermore, the PCT results of the population showed a skewed distribution, leading to a reduction in the applicability of the MA method.…”

Moving average procedures as an additional tool for real-time analytical quality control

“…Selection of the optimal MA procedure for each of the 10 examined clinical chemistry analytes was previously done using the bias detection simulation method described by van Rossum ( 7 , 12 , 13 ). The bias detection simulation method comprises the examination of different combinations of MA procedure parameters for each analyte and the ability of each examined MA procedure to detect biases of different sizes through dedicated software.…”

“…The bias detection simulation method comprises the examination of different combinations of MA procedure parameters for each analyte and the ability of each examined MA procedure to detect biases of different sizes through dedicated software. We have previously described in detail the process of selecting, optimizing, and validating MA procedures on the example of creatinine, potassium, sodium, and albumin ( 12 ). Respectively, the same was done for the other six analytes.…”

When internal quality control is insufficient or inefficient: Consider patient-based real-time quality control!

“…A major breakthrough is that one of these methods has been made available online in the MA Generator application, so that laboratories can readily use the advanced simulation techniques to set up and validate their laboratory-specific PBRTQC procedures. 20,21 Using these new optimization techniques to design PBRTQC procedures in routine practice, it has been shown that PBRTQC application is feasible and valuable for (rapid) detection of relevant errors and increases the effectiveness and cost-effectiveness of quality control plans. 3,16,18,19 Insights and guidance on when to select which type of calculation algorithm for PBRTQC are needed to enhance further the performance and application of PBRTQC.…”