Quantitative Evaluation of the Real-World Harmonization Status of Laboratory Test Items Using External Quality Assessment Data
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.A new Real-World Harmonization Index (RWHI) quantifies laboratory test harmonization using external quality assessment data. This index reveals varying harmonization levels across common clinical tests, aiding big data applications.
Area Of Science
- Clinical Chemistry
- Laboratory Medicine
- Biostatistics
Background
- Analytical quality of clinical laboratory results has improved significantly.
- Utilizing big data and machine learning requires understanding test harmonization.
- A quantitative index is needed to assess real-world laboratory test harmonization.
Purpose Of The Study
- To develop a quantitative harmonization index for real-world laboratory tests.
- To assess the harmonization status of common clinical laboratory tests.
Main Methods
- Collected 2021-2022 external quality assessment (EQA) data for eight common tests.
- Calculated total analytical error based on bias% and CV% within peer groups.
- Developed the Real-World Harmonization Index (RWHI) by dividing analytical error by allowable error derived from biological variation at minimum, desirable, and optimal levels.
Main Results
- Total cholesterol, triglyceride, and CEA demonstrated optimal harmonization (RWHI ≤ 1).
- HDL-cholesterol, AFP, and PSA showed desirable harmonization.
- Creatinine achieved minimum harmonization, while HbA1c did not meet the minimum harmonization level.
Conclusions
- A quantitative RWHI was successfully developed using regional EQA data.
- The RWHI provides a metric to reflect the actual harmonization level of laboratory tests in clinical practice.
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
Related Concept Videos
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Specificity: The ability of the method to accurately measure the target analyte without...
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...