Search

Introduction An increase in analytical imprecision (expressed as CV a ) can introduce additional variability (i.e. noise) to the patient results, which poses a challenge to the optimal management of patients. Relatively little work has been done to address the need for continuous monitoring of analytical imprecision. Methods Through numerical simulations, we describe the use of moving standard deviation (movSD) and a recently described moving sum of outlier (movSO) patient results as means for detecting increased analytical imprecision, and compare their performances against internal quality control (QC) and the average of normal (AoN) approaches. Results The power of detecting an increase in CV a is suboptimal under routine internal QC procedures. The AoN technique almost always had the highest average number of patient results affected before error detection (ANPed), indicating that it had generally the worst capability for detecting an increased CV a . On the other hand, the movSD and movSO approaches were able to detect an increased CV a at significantly lower ANPed, particularly for measurands that displayed a relatively small ratio of biological variation to CV a. Conclusion The movSD and movSO approaches are effective in detecting an increase in CV a for high-risk measurands with small biological variation. Their performance is relatively poor when the biological variation is large. However, the clinical risks of an increase in analytical imprecision is attenuated for these measurands as an increased analytical imprecision will only add marginally to the total variation and less likely to impact on the clinical care. [ABSTRACT FROM AUTHOR]

Objectives “Average of normal” (AoN) or “moving average” is increasingly used as an adjunct quality control tool in laboratory practice. Little guidance exists on how to verify if an out-of-control situation in the AoN chart is due to a shift in analytical performance, or underlying patient characteristics. Design and methods Through simulation based on clinical data, we examined 1) the location of the last apparently stable period in the AoN control chart after an analytical shift, and 2) an approach to verify if the observed shift is related to an analytical shift by repeat testing of archived patient samples from the stable period for 21 common analytes. Results The number of blocks of results to look back for the stable period increased with the duration of the analytical shift, and was larger when smaller AoN block sizes were used. To verify an analytical shift, 3 archived samples from the analytically stable period should be retested. In particular, the process is deemed to have shifted if a difference of > 2 analytical standard deviations (i.e. 1:2 s rejection rule) between the original and retested results are observed in any of the 3 samples produced. The probability of Type-1 error (i.e., false rejection) and power (i.e., detecting true analytical shift) of this rule are < 0.1 and > 0.9, respectively. Conclusions The use of appropriately archived patient samples to verify an apparent analytical shift is preferred to quality control materials. Nonetheless, the above findings may also apply to quality control materials, barring matrix effects. [ABSTRACT FROM AUTHOR]

Background When a systematic error is detected in the analytical process, ideally, one seeks to retest only patient samples between the onset of the error and the time the error was detected. In practice, the onset of error is often unknown, and patient samples are retrospectively retested back to the last acceptable QC sample. This can be wasteful of reagents and operator time. Methods An alternative approach that is based on the expected number of spurious results is described to determine when retrospective retesting should terminate. Assuming each patient sample was independently measured by an analytical process with an underlying Gaussian distribution, a Bayesian model that takes into account the difference between the original and retest result of each patient sample was developed. Results We are able to significantly reduce the number of samples retested, while ensuring that the average number of spurious results observed under the proposed retesting procedure was similar to or only marginally higher than the baseline number of spurious results when the assay was in control. Conclusion Patient samples measured after the systematic error have high probabilities of being retested under the proposed retesting procedure. [ABSTRACT FROM AUTHOR]

Replacing equipment is an important decision that nearly all entities must face, generally motivated by rising operating and maintenance costs of current assets or the technological advances of available assets in the market. The problem has been studied for nearly a century. In this article, we survey single and multiple asset solution approaches under a variety of settings, including technological change, variable utilization, tax, and various uncertainties. Furthermore, we illustrate a number of open problems that are worthy of future study. [ABSTRACT FROM AUTHOR]

Lot-to-lot verification is an integral component for monitoring the long-term stability of a measurement procedure. The practice is challenged by the resource requirements as well as uncertainty surrounding experimental design and statistical analysis that is optimal for individual laboratories, although guidance is becoming increasingly available. Collaborative verification efforts as well as application of patient-based monitoring are likely to further improve identification of any differences in performance in a relatively timely manner. Appropriate follow up actions of failed lot-to-lot verification is required and must balance potential disruptions to clinical services provided by the laboratory. Manufacturers need to increase transparency surrounding release criteria and work closer with laboratory professionals to ensure acceptable reagent lots are released to end users. A tripartite collaboration between regulatory bodies, manufacturers, and laboratory medicine professional bodies is key to developing a balanced system where regulatory, manufacturing, and clinical requirements of laboratory testing are met, to minimize differences between reagent lots and ensure patient safety. Clinical Chemistry and Laboratory Medicine has served as a fertile platform for advancing the discussion and practice of lot-to-lot verification in the past 60 years and will continue to be an advocate of this important topic for many more years to come. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented which discusses a simple method to derive Z-score of reference change value involving more than two serial results.