Time to address quality control processes applied to antibody testing for infectious diseases.

As testing for infectious diseases moves from manual, biological testing such as complement fixation to high throughput automated autoanalyzer, the methods for controlling these assays have also changed to reflect those used in clinical chemistry. However, there are many differences between infectious disease serology and clinical chemistry testing, and these differences have not been considered when applying traditional quality control methods to serology. Infectious disease serology, which is highly regulated, detects antibodies of varying classes and to multiple and different antigens that change according to the organisms' genotype/serotype and stage of disease. Although the tests report a numerical value (usually signal to cut-off), they are not measuring an amount of antibodies, but the intensity of binding within the test system. All serology assays experience lot-to-lot variation, making the use of quality control methods used in clinical chemistry inappropriate. In many jurisdictions, the use of the manufacturer-provided kit controls is mandatory to validate the test run. Use of third-party controls, which are highly recommended by ISO 15189 and the World Health Organization, must be manufactured in a manner whereby they have minimal lot-to-lot variation and at a level where they detect exceptional variation. This paper outlines the differences between clinical chemistry and infectious disease serology and offers a range of recommendations when addressing the quality control of infectious disease serology.

Implementation of Novel Quality Assurance Program for Hepatitis C Viral Load Point of Care Testing.

All patients should have access to accurate and timely test results. The introduction of point of care testing (PoCT) for infectious diseases has facilitated access to those unable to access traditional laboratory-based medical testing, including those living in remote and regional locations, or individuals who are marginalized or incarcerated individuals. In many countries, laboratory testing for infectious diseases, such as hepatitis C virus (HCV), is performed in a highly regulated environment. However, this is not the case for PoCT, where testing is performed by non-laboratory staff and quality controls are often lacking. An assessment of the provision of laboratory-based quality assurance to PoCT for infectious disease was conducted and the barriers to participation identified. A novel approach to providing quality assurance to PoCT sites, in particular those testing for HCV, was designed and piloted. This novel approach incudes identifying and validating sample types that are inactivated and stable at ambient temperature, creating cost-effective supply chains to facilitate logistics of samples, and the development of a smart phone-enabled portal for data entry and analyses. The creation and validation of this approach to quality assurance of PoCT removes the barriers to participation and acts to improve the quality and accuracy of testing, reduce errors and waste, and improve patient outcomes.

Validation of Dried Tube Sample Format Quality Controls for the Monitoring of Viral Load and Blood Screening Assays.

HIV viral load (VL) and donor screening assays experience variation and require quaity assurance (QA). NRL sought to confirm a dried tube sample format (HIVDTS) sample type for use in quality control (QC) programs for HIV molecular testing. 50 µL of HIV supernatant at 1 × 105 copies per millilitre (copies/mL)) was dried for 48 hours at room temperature. Post-production and shipped integrity studies were undertaken. Dried HIVDTS was reconstituted in PBS buffer and tested in HIV VL (six participants) or blood screening assays (four participants). Results were entered into NRL's QC monitoring software (EDCNet™) for analysis. The mean of 224 VL results when HIVDTS QCs were tested in Biocentric HIV GENERIC Charge Virale assay was 4.54 log10 copies/mL, with the percentage coefficient of variation (CV%) ranging from 1.75 to 13.20%. The mean Ct value for HIVDTS QCs tested on Roche Cobas MPX assay results was 28.71 (range 28.33 to 29.14), with CV% ranging from 1.56 to 3.98%. The study confirms HIVDTS QCs can effectively monitor the performance of HIV molecular testing and offers a cheaper alternative to commercial QC samples that require cold-chain shipping on dry ice and UN3373 conditions.

Does a change in quality control results influence the sensitivity of an anti-HCV test?

Background Laboratories use quality control (QC) testing to monitor the extent of normal variation. Assay lot number changes contribute the greatest amount of variation in infectious disease serology testing. An unexpected change in six lots of an anti-HCV assay allowed the determination of the effect these lot changes made to the assay's clinical sensitivity. Methods Two sets of seroconversion samples comprising of 44 individual samples and 9 external quality assessment scheme (EQAS) samples, all positive to anti-HCV, were tested in affected and unaffected assay lots, and the difference in the quantitative and qualitative results of the samples was analyzed. Results Of 44 low-positive seroconversion samples tested in affected and unaffected assay lots, only three samples had results reported below the assay cutoff when tested on two of the six affected assay lot. A further sample had results below the cutoff for only one affected lot. None of the EQAS samples reported false-negative results. Samples having a signal to cutoff value of less than 6.0 generally had lower results in the affected lots compared with the unaffected lots. Conclusions Unexpected changes in QC reactivity related to variation, in particular assay lot changes, may affect patient results. This study demonstrated that QConnect Limits facilitated the detection of an unexpectedly large variation in QC test results, allowed for the identification of the root cause of the change, and showed that the risk associated with the change was low but credible. The use of evidence-based QC program is essential to detect changes in test systems.

Comparison of four methods of establishing control limits for monitoring quality controls in infectious disease serology testing.

BACKGROUND: A general trend towards conducting infectious disease serology testing in centralized laboratories means that quality control (QC) principles used for clinical chemistry testing are applied to infectious disease testing. However, no systematic assessment of methods used to establish QC limits has been applied to infectious disease serology testing. METHODS: A total of 103 QC data sets, obtained from six different infectious disease serology analytes, were parsed through standard methods for establishing statistical control limits, including guidelines from Public Health England, USA Clinical and Laboratory Standards Institute (CLSI), German Richtlinien der Bundesärztekammer (RiliBÄK) and Australian QConnect. The percentage of QC results failing each method was compared. RESULTS: The percentage of data sets having more than 20% of QC results failing Westgard rules when the first 20 results were used to calculate the mean±2 standard deviation (SD) ranged from 3 (2.9%) for R4S to 66 (64.1%) for 10X rule, whereas the percentage ranged from 0 (0%) for R4S to 32 (40.5%) for 10X when the first 100 results were used to calculate the mean±2 SD. By contrast, the percentage of data sets with >20% failing the RiliBÄK control limits was 25 (24.3%). Only two data sets (1.9%) had more than 20% of results outside the QConnect Limits. CONCLUSIONS: The rate of failure of QCs using QConnect Limits was more applicable for monitoring infectious disease serology testing compared with UK Public Health, CLSI and RiliBÄK, as the alternatives to QConnect Limits reported an unacceptably high percentage of failures across the 103 data sets.

Results of cytomegalovirus DNA viral loads expressed in copies per millilitre and international units per millilitre are equivalent.

Quantification of Cytomegalovirus (CMV) DNA is required for the initiation and monitoring of anti-viral treatment and the detection of viral resistance. However, due to the lack of standardisation of CMV DNA nucleic acid tests, it is difficult to set universal thresholds. In 2010, the 1st WHO International Standard for Human Cytomegalovirus for Nucleic Acid Amplification Techniques was released. Since then CMV DNA viral load assays have been calibrated using this standard. Three external quality assessment (EQA) providers sent the same five samples to their participants and analysed the results to determine the equivalence of reporting CMV DNA results in international units per millilitre (IU/mL), and compared the difference in results reported in IU/mL with those reported in copies per millilitre (c/mL), and to determine the rate of adoption of IU/mL. About 78% of participants continue to report results in c/mL even though six of the 12 commercial assays are calibrated against the standard. The range of the results reported in IU/mL was less than those reported in c/mL indicating that the adoption of the WHO standard successfully improved the reporting of the CMV viral load. The variation in individual sample results reported by different assays, irrespective of whether in IU/mL or c/mL, is still great and therefore more standardisation of the assays is needed to allow the setting of treatment and monitoring thresholds. This study can act as a bench mark to determine rate of future adoption if reporting CMV DNA viral load results in IU/mL.

Determination of quality control limits for serological infectious disease testing using historical data.

BACKGROUND: An effective quality control (QC) program requires the establishment of control limits within which the results of the QC sample is expected to fall. Traditionally, the mean plus/minus two standard deviations calculated for a set of QC sample results is used to establish control limits. Allowable total error (TEa) and Westgard rules aid in interpreting QC sample results. Westgard rules assume QC sample results are normally distributed and TEa assumes commutability between the QC sample and patient results. None of these paradigms apply to infectious disease testing. METHODS: RESULTS from the NRL's QC program were extracted and sorted into assay/QC lot number-specific data. Control limits for selected QC samples used to monitor 64 commonly used serological assays were calculated and validated using the within- and between-QC lot variance of data from each of the assay/QC combinations. RESULTS: No assay/QC combination had more than 10% of results less than the lower control limit or greater than the upper control limit. Of the 423 assay/QC lot combinations, 14 (3.3%) had more than 5% of results less than the lower limit and 48 (11.3%) had more than 5% of results greater than the upper limit calculated for that assay/QC combination. CONCLUSIONS: The control limits, established by this novel method, are based on more than a decade of QC test results from >300 laboratories from 30 countries and provides users of the NRL QC program evidence-based control limits that can be applied in isolation or in conjunction with more traditional methods for establishing control limits.

Development of working reference materials for clinical virology.

Nucleic acid amplification technique (NAT)-based assays are replacing traditional diagnostic methods in clinical laboratories. However, many of these assays are developed in-house and the lack of standardised reference materials has hindered assay implementation and control. Consequently, in the UK, the Clinical Virology Network (CVN), the National Institute for Biological Standards and Control (NIBSC), and the Health Protection Agency (HPA), are working in collaboration to develop working standards or 'run controls' for diagnostic NAT-based assays, particularly real-time PCR. These run controls are intended for use in microbiology laboratories and are designed to be extracted and amplified in the same way as clinical samples and included in each assay run. The aim is to enable clinical laboratories to continuously monitor the performance of their diagnostic NAT assays on a run-by-run basis allowing inter-laboratory comparisons, and ultimately improving the consistency of results. At present, eight candidate run controls representing clinically relevant viral targets have been prepared for evaluation by CVN laboratories. Data have been returned on the performance of each run control in routine diagnostic assays. Preliminary results presented here indicate a high level of variability in intra- and inter-assay detection of these targets, highlighting the need for standardisation of assays within molecular diagnostics.