Comparison of 3 options for choosing control limits in biochemistry testing

Background: Quality control (QC) validation is an important step in the laboratory harmonization process. This includes the application of statistical QC requirements, procedures, and control rules to identify and maintain ongoing stable analytical performance. This provides confidence in the production of patient results that are suitable for clinical interpretation across a network of veterinary laboratories.Objectives: To determine that a higher probability of error detection (P ed ) and lower probability of false rejection (P fr ) using a simple control rule and one level of quality control material (QCM) could be achieved using observed analytical performance than by using the manufacturer's acceptable ranges for QCM on the Sysmex XT-2000iV hematology analyzers for veterinary use. We also determined whether Westgard Sigma Rules could be sufficient to monitor and maintain a sufficiently high level of analytical performance to support harmonization.Methods: EZRules3 was used to investigate candidate QC rules and determine the P ed and P fr of manufacturer's acceptable limits and also analyzer-specific observed analytical performance for each of the six Sysmex analyzers within our laboratory system using the American Society of Veterinary Clinical Pathology (ASVCP)-recommended or internal expert opinion quality goals (expressed as total allowable error, TE a ) as the quality requirement. The internal expert quality goals were generated by consensus of the Quality, Education, Planning, and Implementation (QEPI) group comprised of five clinical pathologists and seven laboratory technicians and managers. Sigma metrics, which are a useful monitoring tool and can be used in conjunction with Westgard Sigma Rules, were also calculated. Results:The QC validation using the manufacturer's acceptable limits for analyzer 1 showed only 3/10 measurands reached acceptable P ed for veterinary laboratories (>0.85). For QC validation based on observed analyzer performance, the P ed was >0.94 using a 1-2.5s QC rule for the majority of observations (57/60) across the group of analyzers at the recommended TE a . We found little variation in P fr between manufacturer acceptable limits and individual analyzer observed performance as this is a characteristic of the rule used, not the analyzer performance.

Section: Discussionsupporting

confidence: 58%

Section: Discussionmentioning

confidence: 56%

Section: Discussionmentioning

confidence: 66%

“…21,22 In veterinary medicine, biochemistry analyzer QC validation has encompassed more of the focus. 14,15 Early work by…”

Section: Discussionmentioning

confidence: 99%

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Quality control validation for a veterinary laboratory network of six Sysmex XT‐2000iV hematology analyzers

Daly

Graham

Freeman³

2022

“…QC validation is also important because manufacturers' ranges for acceptable QC are often based on performance across multiple laboratories, resulting in wider limits than those suitable for an individual analyzer and, therefore, inability to achieve the desired high P ed . 2,9,11,12 Finally, choosing candidate QC procedures for individual tests instead of indiscriminately using the traditional 1 2s rule may be economically more efficient and save time and money. [13][14][15] False rejection rates of 5%, 9%, 14%, or 18% are expected with a 1 2s rule if 1, 2, 3, or 4 controls are measured per run, respectively.…”

Section: When Q C Validati On S Hould B E Undertakenmentioning

confidence: 99%

The importance of quality control validation and relationships with total error quality goals and bias in the interpretation of laboratory results

Freeman,

Klenner‐Gastreich,

Korchia

2024

Self Cite

The objective of a quality system is to provide accurate and reliable results for clinical decision‐making. One part of this is Quality Control (QC) validation. QC validation is not routinely applied in veterinary laboratories. This leads to the inappropriate usage of random QC rules without knowing the Probability of error detection (Ped) and Probability of false rejection (Pfr) of a method. In this paper, we will discuss why QC validation is important, when it should be undertaken, why QC validation is done, and why it is not commonly done. We will present the role of total analytical error (TEa) in the QC validation process and the challenges when a consensus TEa has not been published. Finally, we will also discuss the possibilities of ‘gray zone’ determinations and mention the effects of bias on the quality of results. Reasons for the low prevalence of performing QC validation may include (a) lack of familiarity with the concept, (b) lack of time and resources needed to conduct QC validation, and (c) lack of TEa goal for some measurands. If no TEa is available, the user may elect to use a ‘reverse approach’ to QC validation. This uses the CV and bias generated from the evaluation of QC measurements, specifying Ped, Pfr, and N (number of QC measurements/run). This identifies the lowest total error that can be controlled under these defined conditions, thus enabling the laboratory to have an estimate of the ‘gray zone’ associated with results generated with a specific assay.

Repeat patient testing‐quality control compared to commercial quality control material for the Sysmex XT‐2000iV hematology analyzer in a multi‐site veterinary laboratory

Daly,

Rishniw,

Graham

et al. 2024

BackgroundQuality control material (QCM) for hematology in veterinary laboratories is limited, and repeat patient testing quality control (RPT‐QC) is an alternative method using excess matrix‐specific samples.ObjectivesThis study aimed to determine if median differences between RPT‐QC analyses for each time interval for RBC, HGB, HCT, and WBC were the same, determine if unified RPT‐QC limits can be applied to a network of veterinary laboratories, compare the performance of RPT‐QC to commercial QCM for the reference analyzer and evaluate the experience over a 4 month period and design, improve and implement an automated spreadsheet for RPT‐QC data management.MethodsThe potential to unify individual analyzer RPT‐QC limits for red blood cells (RBC), hematocrit (HCT), hemoglobin (HGB), and white blood cells (WBC) on multi‐site Sysmex XT‐2000‐iV analyzers was explored by a difference of means test and confidence interval determination for the median difference for each network analyzer in comparison to the network reference analyzer. User experience of an automated RPT‐QC data management Excel spreadsheet was collected by user feedback during monthly meetings. Numbers of out‐of‐control results and the root causes for these for RPT‐QC were compared against those of a commercial QCM over a 4‐month period.ResultsDifferences between individual analyzer RPT‐QC limits were too large to allow for unification of network limits. The automated spreadsheet successfully highlighted out‐of‐control events for RPT‐QC. Trends or shifts were more frequent for commercial QCM based on observed performance and a 1–2.5 s QC rule than for RPT‐QC. Following routine troubleshooting, RPT‐QC out‐of‐control events were resolved with an alternative RPT‐QC sample indicating random error associated with excessive deterioration. Use of an automated spreadsheet for recording RPT‐QC, documentation and troubleshooting of out‐of‐control events, and collating monthly summary calculations were considered an asset in laboratory quality management.ConclusionsRPT‐QC can be successfully implemented and integrated into a multi‐site veterinary laboratory. Individual analyzer RPT‐QC limit generation is recommended. The deterioration of commercial QCM caused shifts or trends in QC results, which initiated more repeat analyses and investigations than did RPT‐QC.