The short story of the long-term Sigma metric: shift cannot be treated as a linear parameter

Bias in Laboratory Medicine: The Dark Side of the Moon

Physicians increasingly use laboratory-produced information for disease diagnosis, patient monitoring, treatment planning, and evaluations of treatment effectiveness. Bias is the systematic deviation of laboratory test results from the actual value, which can cause misdiagnosis or misestimation of disease prognosis and increase healthcare costs. Properly estimating and treating bias can help to reduce laboratory errors, improve patient safety, and considerably reduce healthcare costs. A bias that is statistically and medically significant should be eliminated or corrected. In this review, the theoretical aspects of bias based on metrological, statistical, laboratory, and biological variation principles are discussed. These principles are then applied to laboratory and diagnostic medicine for practical use from clinical perspectives.

Bias, the unfinished symphony

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.

Establishment of risk quality control charts based on risk management strategies

BACKGROUND

Risk management strategies have been proposed for applications in clinical laboratories to reduce patient risks; however, effective and visual risk-monitoring tools are currently lacking in medical laboratories. In this study, we constructed a risk quality control (QC) chart based on risk management strategies.

METHODS

We calculated the risk levels of QC materials based on Bayes' theorem by combining the total allowable error, QC results, and the maximum number of unacceptable errors in the laboratory. Then, we constructed a risk QC chart by presenting the Z values and corresponding risk levels of QC materials simultaneously. Finally, we evaluated the risk-monitoring capabilities of the risk QC charts by simulating different long-term errors in the laboratory.

RESULTS

The risk levels of QC materials increased as the QC results moved further away from the set mean. Larger sigma values led to fewer risks obtained for the same QC results. The constructed risk QC charts intuitively showed specific risk levels and could warn lab staff out-of-control, without the need for QC rules to make judgments. The risk levels of erroneous results differed for items with different sigma performance.

CONCLUSIONS

Risk-based QC charts allowed visualization of the QC results and specific risk levels simultaneously, providing more intuitive results than those obtained from traditional QC charts.

Statistical distributions commonly used in measurement uncertainty in laboratory medicine

Uncertainty is an inseparable part of all types of measurement. Recently, the International Organization for Standardization (ISO) released a new standard (ISO 20914) on how to calculate measurement uncertainty (MU) in laboratory medicine. This standard can be regarded as the beginning of a new era in laboratory medicine. Measurement uncertainty comprises various components and is used to calculate the total uncertainty. All components must be expressed in standard deviation (SD) and then combined. However, the characteristics of these components are not the same; some are expressed as SD, while others are expressed as a ± b, such as the purity of the reagents. All non-SD variables must be transformed into SD, which requires a detailed knowledge of common statistical distributions used in the calculation of MU. Here, the main statistical distributions used in MU calculation are briefly summarized.