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An approach for determining allowable between reagent lot variation.

Marith van Schrojenstein LantmanHikmet Can CubukcuGuilaine BoursierMauro PanteghiniFrancisco A Bernabeu-AndreuNeda MilinkovicPika Mesko BrguljanSolveig LinkoDuilio BrugnoniRuth O'KellyChristos KroupisMaria LohmanderLuděk ŠpronglFlorent VanstapelMarc Thelennull null
Published in: Clinical chemistry and laboratory medicine (2022)
Clinicians trust medical laboratories to provide reliable results on which they rely for clinical decisions. Laboratories fulfil their responsibility for accurate and consistent results by utilizing an arsenal of approaches, ranging from validation and verification experiments to daily quality control procedures. All these procedures verify, on different moments, that the results of a certain examination procedure have analytical performance characteristics (APC) that meet analytical performance specifications (APS) set for a particular intended use. The APC can in part be determined by estimating the measurement uncertainty component under conditions of within-laboratory precision ( u Rw ), which comprises all components influencing the measurement uncertainty of random sources. To maintain the adequacy of their measurement procedures, laboratories need to distinguish aspects that are manageable vs. those that are not. One of the aspects that may influence u Rw is the momentary significant bias caused by shifts in reagent and/or calibrator lots, which, when accepted or unnoticed, become a factor of the APC. In this paper, we postulate a model for allocating a part of allowable u Rw to between-reagent lot variation, based on the need for long-term consistency of the measurement variability for that specific measurand. The allocation manages the ratio between short-term and long-term variation and indicates laboratories when to reject or correct certain variations due to reagent lots.
Keyphrases
  • quality control
  • healthcare
  • palliative care
  • high resolution
  • drinking water
  • minimally invasive
  • mass spectrometry
  • neural network