Bias in clinical chemistry

Theodorsson, Elvar; Magnusson, Bertil; Leito, Ivo

doi:10.17116/labs2018703141

Cited by 7 publications

(4 citation statements)

References 64 publications

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“…With M as the mean value obtained on 20 replicates and V as known value for positive quality controls provided by the manufacturer's package inserts. 7,8 2.7 | Limit of blank, limit of detection, limit of quantitation, linearity, and carry-over (A) The limit of blank (LOB) was defined as the mean of blank replicates + (1.645 × SD blank replicates ). For this purpose, negative samples (NS) with no measured (NaCl 0.09% solution) analyzed 10 times in row (IgG mean= 0.02 AU/mL; IgM mean = 0.44 AU/ mL).…”

Section: Precision and Truenessmentioning

confidence: 99%

Clinical usefulness of fully automated chemiluminescent immunoassay for quantitative antibody measurements in COVID‐19 patients

Soleimani

Khourssaji

Gruson

et al. 2020

Journal of Medical Virology

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Background Since December 2019 we have been in the battlefield with a new threat to the humanity, known as SARS‐CoV‐2, which causes COVID‐19, characterized by viral pneumonia. It may be asymptomatic or cause various symptoms, ranging from flu like symptoms to ARDS and eventually death. At present, the only reliable test for COVID‐19 diagnosis is RT‐qPCR. Assessing the immune response against SARS‐CoV‐2 could increase the detection sensitivity of infected population. Hereby, we report the performances of a fully automated chemiluminescent immunoassay (CLIA) on 276 serum samples. Methods One hundred samples obtained from COVID‐19 negative subjects (COVID‐19 free) were analyzed to evaluate diagnostic specificity of antibody (Ab) detection. Thereafter, 176 samples obtained from 125 patients with confirmed COVID‐19 (COVID‐19 patients) were selected to assess the diagnostic sensitivity of the CLIA. All samples were analyzed on MAGLUMI TM 800 platform. Results All COVID‐19 free samples had Ab levels below the cut‐off values. Hence, the diagnostic specificity was estimated at 100% (95%CI= 96.3‐100.0; PPV= 100%). By the 18 th day from the onset of symptoms we reached to an optimal diagnostic sensitivity (more than 95.0 %) In fact, the diagnostic sensitivity increased over time and between 15 to 25 days after symptoms onset, reached to 95.5% (95% CI= 84.9‐99.2). Conclusion The new automated CLIA analyzer appeared to be a robust and reliable method to measure specific Ab against COVID‐19 at a high throughput. Our data suggest that combining Ab and nucleic acid detection could increase the diagnostic sensitivity. This article is protected by copyright. All rights reserved.

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Section: Precision and Truenessmentioning

confidence: 99%

Clinical usefulness of fully automated chemiluminescent immunoassay for quantitative antibody measurements in COVID‐19 patients

Soleimani

Khourssaji

Gruson

et al. 2020

Journal of Medical Virology

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“…Variation refers to random fluctuations in repeated results generated for a particular sample or individual. This is distinct from systematic error, also termed bias, which describes consistent under‐ or overestimation of the true value (Theodorsson et al ., ). The standard deviation (SD) or the coefficient of variation (CV) (= SD/mean) provides summary measures of variation of the observed standard mean.…”

Section: What Is Meant By Variation?mentioning

confidence: 97%

When neuroscience met clinical pathology: partitioning experimental variation to aid data interpretation in neuroscience

Jeffery

Bate

Safayi

et al. 2018

Eur J of Neuroscience

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In animal experiments, neuroscientists typically assess the effectiveness of interventions by comparing the average response of groups of treated and untreated animals. While providing useful insights, focusing only on group effects risks overemphasis of small, statistically significant but physiologically unimportant, differences. Such differences can be created by analytical variability or physiological within-individual variation, especially if the number of animals in each group is small enough that one or two outlier values can have considerable impact on the summary measures for the group. Physicians face a similar dilemma when comparing two results from the same patient. To determine whether the change between two values reflects disease progression or known analytical and physiological variation, the magnitude of the difference between two results is compared to the reference change value. These values are generated by quantifying analytical and within-individual variation, and differences between two results from the same patient are considered clinically meaningful only if they exceed the combined effect of these two sources of 'noise'. In this article, we describe how the reference change interval can be applied within neuroscience. This form of analysis provides a measure of outcome at an individual level that complements traditional group-level comparisons, and therefore, introduction of this technique into neuroscience can enrich interpretation of experimental data. It can also safeguard against some of the possible misinterpretations that may occur during analysis of the small experimental groups that are common in neuroscience and, by illuminating analytical error, may aid in design of more efficient experimental methods.

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“…In this research, we assume that the random error follows a normal distribution, with zero mean and standard deviation

σ

. Because it has zero mean, it is theoretically possible to eliminate the random error by measuring the same sample an infinite number of times (Theodorsson, Magnusson, & Leito, 2014). The systematic error in Equation () affects the results by the same amount (i.e., we assume

S

to be constant).…”

Section: Error In Analytical Chemistrymentioning

confidence: 99%

Statistical modelling of measurement error in wet chemistry soil data

Leeuwen

Mulder

Batjes

et al. 2021

European J Soil Science

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There is a growing demand for high‐quality soil data. However, soil measurements are subject to many error sources. We aimed to quantify uncertainties in synthetic and real‐world wet chemistry soil data through a linear mixed‐effects model, including batch and laboratory effects. The use of synthetic data allowed us to investigate how accurately the model parameters were estimated for various experimental measurement designs, whereas the real‐world case served to explore if estimates of the random effect variances were still accurate for unbalanced datasets with few replicates. The variance estimates for synthetic pHH2normalO data were unbiased, but limited laboratory information led to imprecise estimates. The same was observed for unbalanced synthetic datasets, where 20, 50 and 80% of the data were removed randomly. Removal led to a sharp increase of the interquartile range (IQR) of the variance estimates for batch effect and the residual. The model was also fitted to real‐world pHH2normalO and total organic carbon (TOC) data, provided by the Wageningen Evaluating Programmes for Analytical Laboratories (WEPAL). For pHH2normalO, the model yielded unbiased estimates with relatively small IQRs. However, the limited number of batches with replicate measurements (5.8%) caused the batch effect to be larger than expected. A strong negative correlation between batch effect and residual variance suggested that the model could not distinguish well between these two random effects. For TOC, batch effect was removed from the model as no replicates were available within batches. Again, unbiased model estimates were obtained. However, the IQRs were relatively large, which could be attributed to the smaller dataset with only a single replicate measurement. Our findings demonstrated the importance of experimental measurement design and replicate measurements in the quantification of uncertainties in wet chemistry soil data. Highlights Accurate uncertainty quantification depends on the experimental measurement design. Linear mixed‐effects models can be used as a tool to quantify uncertainty in wet chemistry soil data. Lack of replicate measurements leads to poor estimates of error variance components. Measurement error in wet chemistry soil data should not be ignored.

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Bias in clinical chemistry

Cited by 7 publications

References 64 publications

Clinical usefulness of fully automated chemiluminescent immunoassay for quantitative antibody measurements in COVID‐19 patients

Clinical usefulness of fully automated chemiluminescent immunoassay for quantitative antibody measurements in COVID‐19 patients

When neuroscience met clinical pathology: partitioning experimental variation to aid data interpretation in neuroscience

Statistical modelling of measurement error in wet chemistry soil data

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