Missed detection of significant positive and negative shifts in gentamicin assay: implications for routine laboratory quality practices

Abstract

IntroductionA product recall was issued for the Roche/Hitachi Cobas Gentamicin II assays on 25th May 2016 in Australia, after a 15 - 20% positive analytical shift was discovered. Laboratories were advised to employ the Thermo Fisher Gentamicin assay as an alternative. Following the reintroduction of the revised assay on 12th September 2016, a second reagent recall was made on 20th March 2017 after the discovery of a 20% negative analytical shift due to erroneous instrument adjustment factor.Materials and methodsThe practices of an index laboratory were examined to determine how the analytical shifts evaded detection by routine internal quality control (IQC) and external quality assurance (EQA) systems. The ability of the patient result-based approaches, including moving average (MovAvg) and moving sum of outliers (MovSO) approaches in detecting these shifts were examined.ResultsInternal quality control data of the index laboratory were acceptable prior to the product recall. The practice of adjusting IQC target following a change in assay method resulted in the missed negative shift when the revised Roche assay was reintroduced. While the EQA data of the Roche subgroup showed clear negative bias relative to other laboratory methods, the results were considered as possible ‘matrix effect’. The MovAvg method detected the positive shift before the product recall. The MovSO did not detect the negative shift in the index laboratory but did so in another laboratory 5 days before the second product recall.ConclusionsThere are gaps in current laboratory quality practices that leave room for analytical errors to evade detection.

Impact of combining data from multiple instruments on performance of patient-based real-time quality control

“…Recently, there has been great activity on the optimization of various parameters for PBRTQC, its application in different laboratory settings, and the detection of different types of errors (2)(3)(4)(5)(6)(7)(8). At the same time, several reviews and recommendations have been published by the International Federation of Clinical Chemistry and Laboratory Medicine Working Group on PBRTQC to provide guidance in these areas to laboratory practitioners, and interested readers are encouraged to refer to them (1,2,9,10).…”

Setting Bias Specifications Based on Qualitative Assays With a Quantitative Cutoff Using COVID-19 as a Disease Model

“…These qualitative approaches may not detect the presence of analytical shifts or drift—that is, the systematic error characterized by constant or proportional bias—that could affect disease classification. 3 , 4 Use of the signal output or S/C ratio from automated analytical instruments provides an opportunity to apply the concepts of analytical performance metrics to qualitative serology assays.…”

Recommendation for performance verification of patient-based real-time quality control

“…More recently, novel techniques such as the moving standard deviation, moving delta, moving sum of outliers and moving percentiles have been described [8][9][10]. These techniques have gained increasing attention owing to maturing statistical methodology, improved information technology capabilities and increasing awareness of the limitations of internal quality control systems [9,[11][12][13][14][15][16]. Indeed, Bull's algorithm (a form of average of normals) is routinely used in clinical hematology laboratories.…”