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

Abstract

Point-of-care testing (POCT) is currently a means for clinical laboratories to provide test results. However, with the rapid development of new methods and technologies, clinicians are paying more attention to the advantages and disadvantages of POCT. 1 Traditional quality control (QC) programs analyze only QC products of two concentrations at a time, 2 which may not be sufficient for the rapid detection of analytical errors affecting patient outcomes. 3,4 Moreover, artificial serum can produce matrix effects. 5 Although some clinicians use patient samples

Patient‐based real‐time quality control in medical laboratories: On the design and robustness of the moving average control chart with truncation limits

“…), a certain multiple of the standard deviation (e.g., 3 or 4 standard deviations from the mean), Box-Plots, and between-subject biological variation were used for this purpose in the literature. 14,19,21,23,24,[30][31][32] For instance, Ng et al 9 considered truncation limits that are 4 standard deviations from the mean when the data distribution is roughly symmetric. For skewed data distributions, they used point of inflection, where the frequency distribution curve began to level off, which is determined by visual inspection.…”

“…33 In the literature, different block sizes varying between 5 and 200 have been considered in different applications. 19,21,23,24,31,34 For instance, Fleming and Katayev 14 considered block sizes of 25, 50, 100, and 200, and determined a block size of 50 as optimal by using a goal of greater than 90% error detection rate in their simulation runs for the MA chart. However, it has also been concluded that optimal block size may be different in different studies.…”

“…Reference values, a certain percentage of the data (e.g., 1%, 5%, 10%, etc. ), a certain multiple of the standard deviation (e.g., 3 or 4 standard deviations from the mean), Box‐Plots, and between‐subject biological variation were used for this purpose in the literature 14,19,21,23,24,30–32 . For instance, Ng et al 9 .…”

“…A larger block size is more effective in detecting smaller sized shifts, while a smaller block size may be preferred in detecting larger sized shifts 33 . In the literature, different block sizes varying between 5 and 200 have been considered in different applications 19,21,23,24,31,34 . For instance, Fleming and Katayev 14 considered block sizes of 25, 50, 100, and 200, and determined a block size of 50 as optimal by using a goal of greater than 90% error detection rate in their simulation runs for the MA chart.…”