Management of Cytomegalovirus, Epstein-Barr Virus, and HIV Viral Load Quality Control Data Using Unity Real Time.

Quality control (QC) rules (Westgard rules) are applied to viral load testing to identify runs that should be reviewed or repeated, but this requires balancing the patient safety benefits of error detection with the cost and inefficiency of false rejection. In this study, we identified the total allowable errors (TEa) from the literature and utilized a commercially available software program (Unity Real Time; Bio-Rad Laboratories) to manage QC data, assess assay performance, and provide QC decision support for both FDA-approved/cleared (Abbott cytomegalovirus [CMV] and HIV viral load) as well as laboratory-developed (Epstein-Barr virus [EBV] viral load) assays. Unity Real Time was used to calculate means, standard deviations (SDs), and coefficient of variation (CV; in percent) of negative, low-positive, and high-positive control data from 73 to 83 days of testing. Sigma values were calculated to measure the test performance relative to a TEa of 0.5 log10. The sigma value of 5.06 for EBV predicts ∼230 erroneous results per million individual patient tests (0.02% frequency), whereas sigma values of >6 for CMV (11.32) and HIV (7.66) indicate <4 erroneous results per million individual patient tests. The Unity Real Time QC Design module utilized these sigma values to recommend QC rules and provided objective evidence for loosening the laboratory's existing QC rules for run acceptability, potentially reducing false rejection rates by 10-fold for the assay with the most variation (EBV viral load). This study provides a framework for laboratories, with Unity Real Time as a tool, to evaluate assay performance relative to clinical decision points and establish optimal rules for routine monitoring of molecular viral load assay performance.

Should I repeat my 1:2s QC rejection?

BACKGROUND: Repeating a QC that is outside 2SD from the mean (1:2s rule) appears to be a common practice. Although this form of repeat sampling is frowned on by many, the comparative power of the approach has not been formally evaluated. METHODS: We computed power functions mathematically and by computer simulation for 4 different 1:2s repeat-sampling strategies, as well as the 1:2s rule, the 1:3s rule, and 2 common QC multirules. RESULTS: The false-rejection rates for the repeat-sampling strategies were similarly low to those of the 1:3s QC rule. The error detection rates for the repeat-sampling strategies approached those of the 1:2s QC rule for moderate to large out-of-control error conditions. In most cases, the power of the repeat-sampling strategies was superior to the power of the QC multirules we evaluated. The increase in QC utilization rate ranged from 4% to 13% for the repeat-sampling strategies investigated. CONCLUSIONS: The repeat-sampling strategies provide an effective tactic to take advantage of the desirable properties of both the 1:2s and 1:3s QC rules. Additionally, the power of the repeat-sampling strategies compares favorably with the power of 2 common QC multirules. These improvements come with a modest increase in the average number of controls tested.