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Objectives An increase in analytical imprecision and/or the introduction of bias can affect the interpretation of quantitative laboratory results. In this study, we explore the impact of varying assay imprecision and bias introduction on the classification of patients based on fixed thresholds. Methods Simple spreadsheets (Microsoft Excel) were constructed to simulate conditions of assay deterioration, expressed as coefficient of variation and bias (in percentages). The impact on patient classification was explored based on fixed interpretative limits. A combined matrix of imprecision and bias of 0%, 2%, 4%, 6%, 8%, and 10% (tool 1) as well as 0%, 2%, 5%, 10%, 15%, and 20% (tool 2) was simulated, respectively. The percentage of patients who were reclassified following the addition of simulated imprecision and bias was summarized and presented in tables and graphs. Results The percentage of patients who were reclassified increased with increasing/decreasing magnitude of imprecision and bias. The impact of imprecision lessens with increasing bias such that at high biases, the bias becomes the dominant cause for reclassification. Conclusions The spreadsheet tools, available as Supplemental Material , allow laboratories to visualize the impact of additional analytical imprecision and bias on the classification of their patients when applied to locally extracted historical results. [ABSTRACT FROM AUTHOR]

Objectives: Automated qualitative serology assays often measure quantitative signals that are compared against a manufacturer-defined cutoff for qualitative (positive/negative) interpretation. The current general practice of assessing serology assay performance by overall concordance in a qualitative manner may not detect the presence of analytical shift/drift that could affect disease classifications.Methods: We describe an approach to defining bias specifications for qualitative serology assays that considers minimum positive predictive values (PPVs) and negative predictive values (NPVs). Desirable minimum PPVs and NPVs for a given disease prevalence are projected as equi-PPV and equi-NPV lines into the receiver operator characteristic curve space of coronavirus disease 2019 serology assays, and the boundaries define the allowable area of performance (AAP).Results: More stringent predictive values produce smaller AAPs. When higher NPVs are required, there is lower tolerance for negative biases. Conversely, when higher PPVs are required, there is less tolerance for positive biases. As prevalence increases, so too does the allowable positive bias, although the allowable negative bias decreases. The bias specification may be asymmetric for positive and negative direction and should be method specific.Conclusions: The described approach allows setting bias specifications in a way that considers clinical requirements for qualitative assays that measure signal intensity (eg, serology and polymerase chain reaction). [ABSTRACT FROM AUTHOR]

This article is a reply to a letter that raised concerns about a previous study. The authors clarify that their study was not intended to derive analytical performance specifications based on indirect outcomes. Instead, their supplementary tool aimed to show the impact of changes in bias and imprecision on the interpretation of laboratory results. The authors also address concerns about the presentation and interpretation of simulation results, noting that different measurands, interpretation thresholds, and statistical assessments can lead to differing conclusions. They emphasize the importance of careful and objective review to avoid drawing misguided interpretations and conclusions. [Extracted from the article]

Objectives: We examined the false acceptance rate (FAR) and false rejection rate (FRR) of varying precision verification experimental designs.Methods: Analysis of variance was applied to derive the subcomponents of imprecision (ie, repeatability, between-run, between-day imprecision) for complex matrix experimental designs (day × run × replicate; day × run). For simple nonmatrix designs (1 day × multiple replicates or multiday × 1 replicate), ordinary standard deviations were calculated. The FAR and FRR in these different scenarios were estimated.Results: The FRR increased as more samples were included in the precision experiment. The application of an upper verification limit, which seeks to cap FRR at 5% for multiple experiments, significantly increased the FAR. The FRR decreases as the observed imprecision increases relative to the claimed imprecision and when a greater number of days, runs, or replicates are included in the verification design. Increasing the number or days, runs, or replicates also reduces the FAR for between-day imprecision and repeatability.Conclusions: Design of verification experiments should incorporate the local availability of resources and analytical expertise. The largest imprecision component should be targeted with a greater number of measurements. Consideration of both FAR and FRR should be given when committing a platform into service. [ABSTRACT FROM AUTHOR]

Objectives: Preanalytical processes in pediatric patients are generally manual and associated with a higher risk of error. The optimized delta check rules for detecting misidentified children samples are examined.Methods: Relative difference and absolute different delta check limits were applied on original and reshuffled (to simulate sample mislabeling/mix-up) paired deidentified pediatric results of 57 laboratory tests. The sensitivity, specificity, and accuracy of a range of delta check limits were determined. The delta check limit associated with the highest accuracy was considered optimal.Results: In general, the delta check limits had poor to moderate accuracy (0.50-0.81) in detecting misidentified patient samples. The sensitivity (rule out misidentified sample) quickly deteriorated at increasing delta check limits. At the same time, the specificity (rule in misidentified sample) of the delta check limit was also low. The performance of the relative difference and absolute difference delta check rules was similar.Conclusions: Our findings showed poor delta check performance in the pediatric population. The high false-positive flag rate may lead to wasteful resource-intensive investigations and delay in result reporting. In addition, we observed that the optimized pediatric delta check correlated strongly with within-subject biologic variation, whereas delta check accuracy correlated poorly with index of individuality. [ABSTRACT FROM AUTHOR]