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Abstract
Choosing QC rules for monitoring quantitative methods is compulsory and often frustrating and not easy. Part of these protocol there are many possible QC statistical 'rules' (e.g. rejecting a single value outside 2 SDs) to be selected. Each analyte should use the rule(s) that have the fewest accepted wrong results (for patients and controls. Selecting the best primary QC rule to ensure that we have developed a simple, rapid system that calculates the rule best primary for each level used by for an analyte, our algorism uses three readily available data points for each QC level - the lab's mean and SD, the true (survey) mean. With these data plus the TEa the program calculates the values for the TE and (TEa-TE). This algorithm generates the primary QC rule (e.g.1 2.0 SD, 2.5 SD, 1 3SD rule). The rules -1 2.5 or 1 3 SDs (and ones between if wanted) will reduce wrong results without accepting false results. Additionally, QC rules such as 4 1 SD and 10 SD are no longer necessary. The 2 2 SD rule need not be rejected but needed only a aware. Using the algorithm by hand or in a lap top is easy, and removes the guesswork of choosing the primary QC rules.
- Received August 2, 2018.
- Accepted August 8, 2018.
- Published by American Society for Clinical Laboratory Science