Moving standard deviation and moving sum of outliers as quality tools for monitoring analytical precision

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.

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.

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.