Gaining a better understanding of Quality Control (QC) processes is a key requirement to improving performance and reducing patient risk. Detecting analytical error is dependent on a QC strategy that reliably detects a critical shift in a result away from the true value.
Recently the concept of Six Sigma has been used by diagnostic laboratories to assess the performance of assays and to assist in the selection of QC rules. The sigma metric is one measure of an assay's ability to perform within specification. However an additional dimension to managing an assay is its stability in bias over time.
The concept of long term stability is the same as measured QC drift (SEdrift) which is the effect of numerous calibrations, changes in reagent lots and other conditions i.e. a long term effect. This implies that the standard error budget is wrong because it is modelled on short term QC and misses this SEdrift stability component.
We show that SEdrift provides a measure of Assay Stability that should be included in Quality Planning and that by including an allowance for this drift, determining target imprecision appropriate for matched QC algorithms that provide high error detection is as simple as dividing the Allowable Performance Specification by 4, 5 or 6.
- Analytical Performance Specification
- Assay bias
- Assay Capability
- Error detection
- QC rules
- Six sigma