Where have the enteric viruses gone? - Differential effects on frequent causes of infectious diarrhoea by SARS-CoV-2 pandemic lockdown measures.

BACKGROUND: Measures of distancing, wearing face/medical masks and lockdown introduced in many countries to meet the challenges of the SARS-CoV-2 pandemic have led to gross changes in the epidemiology of important infections. The observation of decline of positive norovirus tests after introduction of lockdown in Germany led us to investigate changes in the detection of major causes of diarrhoea by comparing pre-pandemic quarters (PPQ: 1Q/17 through 1Q/20) since 2017 and pandemic quarters (PQ: 2Q/20 through 1Q/21). METHODS AND SETTING: Bioscientia Laboratory Ingelheim is a large regional clinical pathology laboratory serving > 50 hospitals and > 5000 general practitioners and specialist outpatient practices located in the federal states Hesse, Rhineland-Palatinate and North Rhine-Westphalia, Germany. Antigen detection assays were used for detection of astrovirus, adenovirus, rotavirus, and Campylobacter antigen and Clostridium difficile Toxin A/B, while norovirus was detected by qualitative RT-PCR. FINDINGS: The mean positivity-ratios of norovirus, adenovirus and astrovirus assays were 3-20 fold lower in periods PQ (2Q/20 through 1Q/21) compared to PPQ (1Q/17 through 1Q/20) (p<.01). The mean positivity-ratio was lower in PQ compared to PPQ for rotavirus (p=.31), but failed to reach statistical significance, while for campylobacter antigen (p=.91) and C. difficile Toxin A/B (p=.17) the mean positivity-ratio was even higher in PQ compared to PPQ. CONCLUSIONS: Apparently, hygienic measures used to contain the SARS-CoV-2 pandemic have differential effects on incidence of diarrhoea viruses as compared to bacterial gastrointestinal agents, particularly C. difficile, which may lead to re-evaluate measures implemented against this important cause of nosocomial diarrhoea.

Analysis of cycle threshold values in SARS-CoV-2-PCR in a long-term study.

BACKGROUND: Cycle threshold (Ct) values can be used in an attempt to semiquantify results in the qualitative real-time polymerase-chain-reaction (PCR) for the new coronavirus SARS-CoV-2. The significance of Ct values in epidemiological studies and large cohorts is still unclear. OBJECTIVE: To monitor Ct values in a long-term study and compare the results with demographic data of patients who tested positive for SARS-CoV-2 by real-time PCR. STUDY DESIGN: S gene SARS-CoV-2 Ct values were analyzed retrospectively from consecutive patients between March 15th to September 15th 2020 with special regard to age, gender, and in- or outpatient status. RESULTS: In total, 65,878 patients were tested, 1103 (1.7 %) of whom were positive for SARS-CoV-2. Twenty-six positive patients were excluded, because the respective PCR runs did not meet the stability requirements (Ct value of the positive controls between 26 and 29). Of the remaining 1077 patients, females (n = 566; 53 %) were significantly older than males (n = 511; 47 %) (50.9 versus 45.1 years; p = 0.006) and had slightly higher mean Ct values than males (25.4 vs. 24.8; p = 0.04). Patients in the age groups >80 years had significantly higher Ct values than the remaining age groups (p < 0.001). Children (0-19 years) showed Ct values in the range of those found in adults (25.2 vs. 25.1, p = 0.9). There were no statistically different Ct values between in- and outpatients (p = 0.1), however, SARS-CoV-2 positive inpatients were significantly older than outpatients (p < 0.0001). CONCLUSIONS: CT values are suitable for more detailed monitoring of the SARS-CoV-2 pandemic. Age is an important cofactor in SARS-CoV-2 positive patients and may have influence on Ct values in SARS-CoV-2-PCR.

Evaluating sample stability in the clinical laboratory with the help of linear and non-linear regression analysis.

As it is common practice for laboratories to store patient samples for a predefined period, allowing clinicians to request additional tests on previously collected samples, knowledge about sample stability is indispensable for the laboratorian. A common approach to estimating the maximum storage time is to use a discrete study design, measuring the analyte of interest at various time-points and then checking for significant differences with the help of a statistical test, such as Student's t-test, Wilcoxon's test or an analysis of variance (ANOVA) test. Because only discrete time intervals are considered, stability data can just be approximated. Alternatively, a continuous study design, as described by the Clinical and Laboratory Standards Institute (CLSI) for performing stability experiments for in vitro diagnostic reagents, can also be adopted by the clinical laboratory to evaluate the stability of biological samples. The major advantage of this approach is that it allows laboratories to define individual stability limits for different medical situations and offers more flexibility when choosing time-points for measurements. The intent of this paper is to demonstrate the evaluation of sample stability in the clinical laboratory with a continuous study design implemented with linear or non-linear regression analysis. Appropriate statistical modeling and acceptance criteria are presented, stability functions are described briefly, and checking the overall validity of the results is discussed.

Evaluation of analytical performance of qualitative and semi-quantitative assays in the clinical laboratory.

As is true for quantitative assays, qualitative and semi-quantitative assays, producing strict binary or ordinal results, must undergo a verification process prior to their implementation for routine clinical laboratory testing. Standard method validation parameters used for quantitative assays, however, do not apply here. Rather, contingency tables, Bayesian statistics and statistical hypothesis testing for inter-rater agreement must be used. This article provides an overview of simple, practical tools, which can be used to verify the analytical performance of such assays. Topics discussed include the verification of precision and accuracy with a single experiment approach and performing method comparison experiments for assays with binary or ordinal results. Acceptance criteria are recommended for each test to provide a standardized framework for performance assessment. The approach is appropriate for all CE/IVD-marked and CLIA-waived assays and will ensure compliance with CAP, ISO 17025 and ISO 15189 regulations.

A practical guide to validation and verification of analytical methods in the clinical laboratory.

Although the measures to improve quality in the clinical laboratory have been enormous in the past years, not least of all due to the introduction of the ISO standards 15189 and 17025, the handling of validation and verification of method performance often still differs widely from laboratory to laboratory. Much of what is published on the topic contains complex statistics and is difficult to implement in routine laboratories. The result is, that this point is often neglected or implemented incorrectly, which in turn can lead to false conclusions about method performances, potentially compromising patient safety or contributing to incorrect diagnoses. As it has long become a standard requirement for accredited laboratories to evaluate and document the analytical performance of all methods not only prior to their first implementation, but also during ongoing operation, there is a need for clear, standardized and practical guidelines on the subject. This review summarizes the current literature on the topic, focusing on the requirements for method validations, or as the case may be, verifications and describes when to validate, when to verify and which statistical tests are appropriate for each. Proper interpretation of statistical test results and acceptance criteria for each procedure are alluded to. Specific topics, which are addressed, are precision and bias verification of quantitative, qualitative and semi-quantitative procedures, method comparisons with Bland-Altman Plots, Passing-Bablok regression analysis, 2×2 contingency tables and bubble charts, linearity studies, analytical sensitivity and specificity, performing carry-over studies and establishing and confirming reference ranges.

Comparison of HbA1c Measurements using 3 Methods in 75 Patients Referred to One Outpatient Department.

OBJECTIVE: HbA1c is the most important surrogate parameter to assess the quality of diabetes care and is also used for the diagnosis of diabetes mellitus (DM) since 2010. We investigated the comparability of 3 HbA1c methods in the city of Jena (Germany). METHODS: The HbA1c determination was carried out in 50 healthy subjects and 24 people with DM (age 51.2±16.3 years, HbA1c 6.8±2.2%) with 3 different hemoglobin A1c testing methods at 4 locations in one city. Our laboratory (HPLC method) served as a reference for comparing the results. All methods are IFCC standardized and all devices are certified by the interlaboratory test. RESULTS: The mean HbA1c of people without diabetes was: laboratory A (TOSOH G8, HPLC) 5.7±0.3%; laboratory B (TOSOH G8, HPLC) 5.5±0.3%, laboratory C (VARIANT II) 5.2±0.3%; laboratory D (COBAS INT.) 5.6±0.3%. All differences are significant (p=0.001).The mean HbA1c of patients with mild to moderate elevated HbA1c was: Laboratory A 7.5±0.9%; B 7.3±1.0%; C 7.0±0.9%; D 7.5±1.1%. Differences are significant (p=0.001) except between laboratory A and D (p=0.8).The mean HbA1c of patients with massively increased HbA1c was: laboratory A 11.5±1.8%; laboratory B 11.4±1.8%; laboratory C 10.8±1.6%; laboratory D 11.5±1.5%. Differences between laboratory A and C, as well as between C and D were significant (p=0.001). CONCLUSION: The mean IFCC standardized HbA1c from 75 people differs by up to 0.5% absolute between 4 laboratories. This difference is clinically significant and may lead to misdiagnosis and wrong treatment decisions, while HbA1c value from one patient were analyzed in different laboratories within a short time.

Evaluation of HbA1C results in an external quality assessment scheme in South Africa.

BACKGROUND: Measuring HbA1c blood levels allows us to assess average glycaemia in individuals during the preceding 6-8 weeks. There is a clear association between increasing risk of complications with higher HbA1c values and a significant risk reduction of the complications with lower HbA1c values. METHODS: The performance of South African laboratories in an External Quality Assurance scheme for HbA1c is reported. CONCLUSIONS: A number of laboratories and methods do not meet the required analytical standards. South African laboratories should adopt measures similar to other regional and national initiatives to significantly improve laboratory performance and bring about harmonization of HbA1c assays.