Patient-based quality control for glucometers using the moving sum of positive patient results and moving average

Introduction: The capability of glucometer internal quality control (QC) in detecting varying magnitude of systematic error (bias), and the potential use of moving sum of positive results (MovSum) and moving average (MA) techniques as potential alternatives were evaluated. Materials and methods: The probability of error detection using routine QC and manufacturer’s control limits were investigated using historical data. Moving sum of positive results and MA algorithms were developed and optimized before being evaluated through numerical simulation for false positive rate and probability of error detection. Results: When the manufacturer’s default control limits (that are multiple times higher than the running standard deviation (SD) of the glucometer) was used, they had 0-75% probability of detecting small errors up to 0.8 mmol/L. However, the error detection capability improved to 20-100% when the running SD of the glucometer was used. At a binarization threshold of 6.2 mmol/L and block sizes of 200 to 400, MovSum has a 100% probability of detecting a bias that is greater than 0.5 mmol/L. Compared to MovSum, the MA technique had lower probability of bias detection, especially for smaller bias magnitudes; MA also had higher false positive rates. Conclusions: The MovSum technique is suited for detecting small, but clinically significant biases. Point of care QC should follow conventional practice by setting the control limits according to the running mean and SD to allow proper error detection. The glucometer manufacturers have an active role to play in liberalizing QC settings and also enhancing the middleware to facility patient-based QC practices.

Moving sum of number of positive patient result as a quality control tool

Background:Recently, the total prostate-specific antigen (PSA) assay used in a laboratory had a positive bias of 0.03 μg/L, which went undetected. Consequently, a number of post-prostatectomy patients with previously undetectable PSA concentrations (defined as <0.03 μg/L in that laboratory) were being reported as having detectable PSA, which suggested poorer prognosis according to clinical guidelines.Methods:Through numerical simulations, we explored (1) how a small bias may evade the detection of routine quality control (QC) procedures with specific reference to the concentration of the QC material, (2) whether the use of ‘average of normals’ approach may detect such a small bias, and (3) describe the use of moving sum of number of patient results with detectable PSA as an adjunct QC procedure.Results:The lowest QC level (0.86 μg/L) available from a commercial kit had poor probability (<10%) of a bias of 0.03 μg/L regardless of QC rule (i.e. 1:2S, 2:2S, 1:3S, 4:1S) used. The average number of patient results affected before error detection (ANPed) was high when using the average of normals approach due to the relatively wide control limits. By contrast, the ANPed was significantly lower for the moving sum of number of patient results with a detectable PSA approach.Conclusions:Laboratory practitioners should ensure their QC strategy can detect small but critical bias, and may require supplementation of ultra-low QC levels that are not covered by commercial kits with in-house preparations. The use of moving sum of number of patient results with a detectable result is a helpful adjunct QC tool.