External Quality Assessment (EQA) for SARS-CoV-2 RNA Point-of-Care Testing in Primary Healthcare Services: Analytical Performance over Seven EQA Cycles.

In April 2020, the Aboriginal and Torres Strait Islander COVID-19 Point-of-Care (POC) Testing Program was initiated to improve access to rapid molecular-based SARS-CoV-2 detection in First Nations communities. At capacity, the program reached 105 health services across Australia. An external review estimated the program contributed to averting between 23,000 and 122,000 COVID-19 infections within 40 days of the first infection in a remote community, equating to cost savings of between AU$337 million and AU$1.8 billion. Essential to the quality management of this program, a customised External Quality Assessment (EQA) program was developed with the Royal College of Pathologists of Australasia Quality Assurance Programs (RCPAQAP). From July 2020 to May 2022, SARS-CoV-2 EQA participation ranged from 93 to 100%. Overall concordance of valid EQA results was high (98%), with improved performance following the first survey. These results are consistent with those reported by 12 Australian and 4 New Zealand laboratories for three SARS-CoV-2 RNA EQA surveys in March 2020, demonstrating that SARS-CoV-2 RNA POC testing in primary care settings can be performed to an equivalent laboratory analytical standard. More broadly, this study highlights the value of quality management practices in real-world testing environments and the benefits of ongoing EQA program participation.

Performance of four regression frameworks with varying precision profiles in simulated reference material commutability assessment.

OBJECTIVES: One approach to assessing reference material (RM) commutability and agreement with clinical samples (CS) is to use ordinary least squares or Deming regression with prediction intervals. This approach assumes constant variance that may not be fulfilled by the measurement procedures. Flexible regression frameworks which relax this assumption, such as quantile regression or generalized additive models for location, scale, and shape (GAMLSS), have recently been implemented, which can model the changing variance with measurand concentration. METHODS: We simulated four imprecision profiles, ranging from simple constant variance to complex mixtures of constant and proportional variance, and examined the effects on commutability assessment outcomes with above four regression frameworks and varying the number of CS, data transformations and RM location relative to CS concentration. Regression framework performance was determined by the proportion of false rejections of commutability from prediction intervals or centiles across relative RM concentrations and was compared with the expected nominal probability coverage. RESULTS: In simple variance profiles (constant or proportional variance), Deming regression, without or with logarithmic transformation respectively, is the most efficient approach. In mixed variance profiles, GAMLSS with smoothing techniques are more appropriate, with consideration given to increasing the number of CS and the relative location of RM. In the case where analytical coefficients of variation profiles are U-shaped, even the more flexible regression frameworks may not be entirely suitable. CONCLUSIONS: In commutability assessments, variance profiles of measurement procedures and location of RM in respect to clinical sample concentration significantly influence the false rejection rate of commutability.