Sentinel testing, analytical sigma metrics and a risk management approach as part of a simplified method verification/validation process
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.A new risk assessment tool effectively identifies critical
Area Of Science
- Clinical Chemistry
- Laboratory Quality Assurance
- Analytical Method Validation
Background
- Verification and validation of laboratory analytical methods are essential for quality assurance.
- Identifying 'sentinel' tests, crucial for quality control, can be a complex process.
- Streamlining this identification is key to efficient laboratory management.
Purpose Of The Study
- To develop and evaluate a risk analysis and assessment tool for identifying sentinel tests.
- To optimize the verification and validation process in a clinical laboratory setting.
Main Methods
- Evaluation of Roche Cobas 8000 systems for 83 serum analytes.
- Application of failure mode and effects analysis (FMEA) to determine analytic risk ratings.
- Calculation of risk priority numbers (RPN) based on Sigma metrics, potential damage, and environmental factors.
Main Results
- On the Cobas C701/ISE, 17 of 54 methods were high risk ('A'), requiring systematic review; 37 were low risk ('B'), eligible for selective verification.
- On the Cobas E801, 10 of 29 methods were high risk ('A'), requiring systematic verification; 19 were low risk ('B'), eligible for selective verification.
Conclusions
- The developed risk analysis and assessment model effectively identifies sentinel tests.
- This tool streamlines the verification and validation process, contributing to lean management principles.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they 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 assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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...