Abstract
Abstract Background Comparison among microscopists is a quality process aimed at achieving comparability of analyses among collaborators who perform microscopy or manual analysis examinations, as in the case of urinary sediment examination. The current laboratory process evaluates red blood cell and white blood cell counts among microscopists within the same unit, aiming to identify discrepant counts through the application of statistical tools, with Chauvenet Statistics and Interquartile Deviation (IQD) being the most common in the literature. The purpose was to systematically compare Chauvenet Statistics and IQD as evaluation criteria for the comparison among microscopists for urinary sediment examination in our laboratories. Methods A total of 45 laboratory units within a total of 684 participants were analyzed throughout 2023. The results reported by microscopists from the same unit were evaluated using Chauvenet Statistics and IQD, calculating the adequacy rate for each type of assessment. Results Adopting IQD as the evaluation criterion, adequacy rates of 94.20% for red blood cell counts and 93.73% for white blood cell counts were observed. Using Chauvenet Statistics, the adequacy rates were 96.20% for red blood cells and 95.65% for white blood cells. This difference stems from 233 results identified as inadequate by the IQD method but deemed adequate by Chauvenet Statistics. In these cases, the results did not exhibit a normal distribution, approaching an expected distribution for categorical (discrete) variables. Additionally, 35 results identified as inadequate by Chauvenet Statistics were evaluated as adequate by the IQD method. These occurrences were observed when the reported results approached a normal distribution. Conclusions Regarding Chauvenet Statistics, IQD is a more efficient statistical tool for use as an evaluation criterion in the comparison among microscopists who perform red and white blood cell counts for urinary sediment examination in the selected laboratory units. However, the present study illustrated that each of the statistical tools will be more efficient in identifying outliers depending on the dataset analyzed. Therefore, it is important to consider the laboratory's epidemiology and population to decide which statistical tool is most suitable for each scenario.
Published Version
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