Statistical distributions commonly used in measurement uncertainty in
laboratory medicine

Coşkun, Abdurrahman; Oosterhuis, Wytze P.

doi:10.11613/bm.2020.010101

Cited by 17 publications

(7 citation statements)

References 12 publications

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“…Several other variability indices, including SD, coefficient of variation, Z-score, and variability independent of mean were calculated in this study and showed significantly higher aOR in the highest quartiles; however, none of these indices showed stepwise significance in the regression analysis. SDs are best suited to measure variability in normally distributed data ( 54 ), but ASV may be an alternative for measuring SUA in patients withT2D, where distribution of SUA could be dispersed and skewed in the research and clinical settings.…”

Section: Discussionmentioning

confidence: 99%

Uric Acid Variability as a Predictive Marker of Newly Developed Cardiovascular Events in Type 2 Diabetes

Kim

Lee

et al. 2021

Front. Cardiovasc. Med.

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Background: Cardiovascular disease (CVD) is associated with morbidity and mortality in patients with type 2 diabetes mellitus (T2D). However, the role of serum uric acid as a risk factor for developing cardiovascular disease is controversial. This study investigated whether uric acid variability was associated with new-onset symptomatic CVD in patients with T2D, requiring percutaneous coronary intervention.Methods: A total of 1,071 patients were enrolled in this retrospective cross-sectional study after propensity score matching. Patients with T2D and new-onset symptomatic CVD who received percutaneous coronary intervention for the first time, and with at least three consecutive 6-monthly measurements of serum uric acid were recruited from Severance Hospital between January 2015 and December 2019. Uric acid variability was measured by average successive variability (ASV) and analyzed to evaluate a possible correlation with the risk of developing CVD.Results: The patients were divided into quartiles based on the uric acid variability. Patients in the highest quartile were older and presented lower renal function and a higher mortality from CVD. There was a linear association between a high uric acid variability and the development of CVD. Compared to the lowest quartile, patients in the higher quartiles had a higher risk of CVD [quartile 3: adjusted odds ratio (aOR) = 1.76; 95% confidence interval (CI), 1.20–2.82; P = 0.019; quartile 4 aOR = 2.89; 95% CI, 1.74–4.80; P < 0.001].Conclusion: High uric acid variability is independently associated with an increased risk of new-onset symptomatic CVD requiring percutaneous coronary intervention in patients with T2D. Thus, maintaining serum uric acid in a narrow range by prescribing effective medications is essential to prevent new-onset CVD in patients with T2D. Nonetheless, the potential use of uric acid variability as a predictive marker of CVD in patients with T2D needs further validation.

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Section: Discussionmentioning

confidence: 99%

Uric Acid Variability as a Predictive Marker of Newly Developed Cardiovascular Events in Type 2 Diabetes

Kim

Lee

et al. 2021

Front. Cardiovasc. Med.

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“…The six sigma theory is based on the normal distribution and the curve of the normal distribution is bell-shaped. Bias can be treated as a linear component in uniform distribution but not in a normal distribution ( 6 ). Using bias as a linear component in SM calculation (see Eq.…”

Section: To the Editormentioning

confidence: 99%

Bias, the unfinished symphony

Coşkun

2022

Biochem. med. (Online)

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In laboratory medicine, mathematical equations are frequently used to calculate various parameters including bias, imprecision, measurement uncertainty, sigma metric (SM), creatinine clearance, LDL-cholesterol concentration, etc. Mathematical equations have strict limitations and cannot be used in all situations and are not open to manipulations. Recently, a paper “Bias estimation for Sigma metric calculation: Arithmetic mean versus quadratic mean” was published in Biochemia Medica. In the paper, the author criticized the approach of taking the arithmetic mean of the multiple biases to obtain a single bias and proposed a quadratic method to estimate the overall bias using external quality assurance services (EQAS) data for SM calculation. This approach does not fit the purpose and it should be noted that using the correct equation in calculations is as important as using the correct reagent in the measurement of the analytes, therefore before using an equation, its suitability should be checked and confirmed.

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“…One way laboratories can timely evaluate and monitor the dispersion of measurements is to use internal quality controls (IQC), which estimates assay precision at a regular rate [6]. Several measurements could be used to calculate the standard deviation (SD) for a result and, assuming the data are normally distributed, the probability that the difference between the true value and the measured value is greater than two standard deviations is less than 5% [7]. For a given assay, the laboratory can calculate the SD for several levels to encompass a measurement range that can be encountered in clinical situations.…”

Section: Introductionmentioning

confidence: 99%

Applying the concept of uncertainty to the sFlt-1/PlGF cut-offs for diagnosis and prognosis of preeclampsia

Lévy

Hamdi

Gadrey

et al. 2021

Clinical Chemistry and Laboratory Medicine (CCLM)

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Five pages, 2866 words, three tables, one figure, one supplement, 25 references Objectives: Placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) assays and the corresponding ratios (sFlt-1/PlGF) have been proposed to aid in the diagnosis by exclusion and/or prognosis of preeclampsia (PE). A method for evaluating ratio uncertainties (RUs), based on the theory of error propagation, was applied to the sFlt-1/PlGF ratio.Methods: RUs were calculated using data derived from sFlt-1 and PlGF Internal Quality Control (IQC) results collected from four centers using Elecsys (Roche) or Kryptor (Thermo Fisher) sFlt1 and PlGF assays. The corresponding RUs were defined for each ratio value.Results: the RUs increased linearly with the sFlt-1/PlGF ratio values. The Elecsys RUs were lower than the Kryptor RUs. Although RUs cannot eliminate differences in ratio values observed among various immunoassays, it can affect interpretation of the sFlt-1/PlGF ratio, especially when results are within the range of predefined PE diagnosis or prognosis cut-offs. Since RUs are only a function of PlGF and sFlt-1 precision, they can be calculated for each assay from each laboratory to adjust the interpretation of sFlt-1/PlGF ratio results in the context of PE.

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Statistical distributions commonly used in measurement uncertainty in laboratory medicine

Cited by 17 publications

References 12 publications

Uric Acid Variability as a Predictive Marker of Newly Developed Cardiovascular Events in Type 2 Diabetes

Uric Acid Variability as a Predictive Marker of Newly Developed Cardiovascular Events in Type 2 Diabetes

Bias, the unfinished symphony

Applying the concept of uncertainty to the sFlt-1/PlGF cut-offs for diagnosis and prognosis of preeclampsia

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