LAUSR

The measurement of internal quality-control samples (iQC) is the cornerstone of analytical quality assurance as implemented on a daily basis in medical laboratories. For many tests, iQC is effective in ensuring that analytical performance is adequate. For other tests, however, using iQC alone is insufficient to assure appropriate analytical quality. Reasons include, firstly, the absence of (stable) quality-control materials, secondly, rapid-onset or temporary critical errors (between scheduled iQC measures), thirdly, qualitycontrol materials with non-commutability issues and, fourthly, tests with a sigma value less than or equal to 4. Although stable, preferably third-party, iQC materials are essential to perform iQC, it can be challenging or even impossible to obtain them for some tests, including those for serum indices, erythrocyte sedimentation rate or mean corpuscular volume (MCV). There may also be non-commutability issues with the iQC materials that do not properly represent for example, lot-to-lot variations of reagent or introduction of a new generation of a specific test. Such a case has been described by Hinge et al. for alkaline phosphatase, and the author has experienced these kinds of issues with several immunoassay-based tests, such as PSA, CEA and vitamin D. Another limiting factor is the scheduling frequency of iQC, which becomes relevant for tests with short turnaround times that may be reported before iQC confirmation of proper analytical performance. The high degree of automation and digitization of modern medical laboratories enables rapid turnaround times for many tests. Although this is extremely valuable for urgent medical diagnostics and patient-friendly healthcare by integrated care pathways, there is always a risk of a rapid-onset of critical error due to a technical failure. When applying iQC alone, there may be a significant delay before this failure is detected. Furthermore, temporary assay failure may be undetectable by iQC due to its scheduling frequency, as has been demonstrated. Finally, given its design, iQC is limited in detecting clinically relevant errors in tests with a low sigma performance value according to the sigma metrics approach. Such tests, characterized by a low ratio of biological variation to analytical variation, generally require stringent control limits or rules and necessitate frequent analysis of iQC samples. However, even with such a strict iQC setup, the probability of detecting a clinically relevant error by iQC remains limited. Despite all the efforts to improve the effectiveness of iQC, for example by introducing sigma-metrics-based iQC to reduce the false-alarm rate and by designing iQC plans based on patient risk, the abovementioned issues remain intrinsic limitations of iQC. Furthermore, these more complex mathematical approaches used to increase the performance and efficiency of iQC are not only challenging to understand but also are complicated to implement in clinical practice. This is evident in the