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

Duane W Newton1,2, Nico Vandepoele3, John C Yundt-Pacheco3

  • 1NaviDx Consulting, Mount Prospect, Illinois, USA.

Journal of Clinical Microbiology
|October 20, 2021
PubMed
Summary

Implementing Westgard rules in viral load testing balances error detection with costs. This study used Unity Real Time software to optimize quality control rules, reducing false rejections and improving accuracy for CMV, HIV, and EBV viral load assays.

Keywords:
quality controlquality data managementtotal allowable errorviral load

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Area of Science:

  • Clinical Chemistry
  • Molecular Diagnostics
  • Laboratory Medicine

Background:

  • Westgard rules are crucial for quality control (QC) in viral load testing, but balancing error detection with false rejection costs is challenging.
  • Optimizing QC requires assessing assay performance against total allowable error (TEa) and utilizing data management tools.

Purpose of the Study:

  • To evaluate the performance of viral load assays (CMV, HIV, EBV) using sigma metrics and identify opportunities to optimize QC rules.
  • To demonstrate the utility of Unity Real Time software in managing QC data and supporting QC decision-making for molecular assays.

Main Methods:

  • Calculated means, standard deviations (SDs), and coefficients of variation (CV) for control data over 73-83 days.
  • Determined sigma values relative to a TEa of 0.5 log10 for Epstein-Barr virus (EBV), cytomegalovirus (CMV), and human immunodeficiency virus (HIV) viral load assays.
  • Utilized the Unity Real Time QC Design module to recommend QC rules based on calculated sigma values.

Main Results:

  • Sigma values indicated high performance for CMV (>6) and HIV (>6), with <4 erroneous results per million tests.
  • EBV viral load assay had a sigma value of 5.06, predicting approximately 230 erroneous results per million tests.
  • Optimized QC rules, informed by sigma values, could reduce false rejection rates by up to 10-fold for the EBV assay.

Conclusions:

  • Unity Real Time software provides a framework for evaluating molecular viral load assay performance and optimizing QC rules.
  • Objective data analysis using sigma metrics supports evidence-based adjustments to QC rules, enhancing efficiency and patient safety.
  • This approach enables laboratories to establish optimal rules for routine monitoring of molecular viral load assays.