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

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.

Guidelines for resident training in veterinary clinical pathology. IV: Laboratory quality management-Teaching domains, competencies, and suggested learning outcomes.

BACKGROUND: The 2019 ASVCP Education Committee Forum for Discussion, presented at the annual ASVCP/ACVP meeting, identified a need to develop recommendations for teaching laboratory quality management principles in veterinary clinical pathology residency training programs. OBJECTIVES: To present a competency-based framework for teaching laboratory quality management principles in veterinary clinical pathology residency training programs, including entrustable professional activities (EPAs), domains of competence, individual competencies, and learning outcomes. METHODS: A joint subcommittee of the ASVCP Quality Assurance and Laboratory Standards (QALS) and Education Committees executed this project. A draft guideline version was reviewed by the ASVCP membership and shared with selected ACVP committees in early 2022, and a final version was voted upon by the full QALS and Education Committees in late 2022. RESULTS: Eleven domains of competence with relevant individual competencies were identified. In addition, suggested learning outcomes and resource lists were developed. Domains and individual competencies were mapped to six EPAs. CONCLUSIONS: This guideline presents a framework for teaching principles of laboratory quality management in veterinary clinical pathology residency training programs and was designed to be comprehensive yet practical. Guidance on pedagogical terms and possible routes of implementation are included. Recommendations herein aim to improve and support resident training but may require gradual implementation, as programs phase in necessary expertise and resources. Future directions include the development of learning milestones and assessments and consideration of how recommendations intersect with the American College of Veterinary Pathologists training program accreditation and certifying examination.

FREE-RANGING OCELOTS (LEOPARDUS PARDALIS): HEMATOLOGY AND SERUM CHEMISTRY REFERENCE VALUES.

Acquiring baseline physiologic data for animals from a free-ranging wildlife species is an elusive objective. Between 1990 and 2020, a monitoring program on the last population of ocelot (Leopardus pardalis) to inhabit public land in the United States yielded 139 blood samples from 67 individual animals. Ocelots were live trapped and anesthetized for census and radiotelemetric studies. The protocol included morphometrics, photographs, electronic identification, and blood collection. Complete blood count and serum chemistry were performed, and after sorting of the data to remove unhealthy individuals and occasional outliers, the dataset provided sufficient information to compute reliable reference intervals (RI). According to the American Society of Veterinary Clinical Pathology consensus guidelines, RI should be elaborated by using data from each reference individual only once. RI by random selection was determined when several measurements were available over time from one same animal. Second, RI were also computed allowing repeat measurements for reference individuals, exclusively to characterize and quantify the effect on the data distribution and on the generated RI. A summary of published RI for various species of wild felids is also presented. The variations observed between species is due not only to species differences but also to variation in measurement methods and RI study design. Overall, accurate blood work interpretation requires RI generated from a healthy population, with defined measurement methods and state-of-the-art RI study design. Of note, calcium is typically tightly regulated in all mammals, as illustrated by the narrow RI (8.5-10.8 mg/dl); conversely, finding a narrow RI in calcium across as many as 49 healthy individuals suggests a high-quality design study.

Total observed error, total allowable error, and QC rules for canine serum and urine cortisol achievable with the Immulite 2000 Xpi cortisol immunoassay.

Determining a simple quality control (QC) rule for daily performance monitoring depends on the desired total allowable error (TEa) for the measurand. When no consensus TEa exists, the classical approach of QC rule validation cannot be used. Using the results of previous canine serum and urine cortisol validation studies on the Immulite 2000 Xpi, we applied a reverse engineering approach to QC rule determination, arbitrarily imposing sigma = 5, and determining the resulting TEa for the QC material (QCM; TEaQCM) and the resulting probability of error detection (Ped) for each QC rule. For the simple QC rule 12.5S with Ped = 0.96 and probability of false rejection (Pfr) = 0.03, the associated TEaQCM were 20% and 35% for serum and 28% and 24% for urine QCM1 and QCM2. If these levels of TEaQCM are acceptable for interpretation of patient sample results, then users can internally validate the 12.5S QC rule, provided that their QCM CVs and biases are similar to ours. Otherwise, more stringent QC rules can be validated by using a lower sigma to lower the TEaQCM. With spiked samples (relevant cortisol concentrations in the veterinary patient matrix) at 38.6 and 552 nmol/L of cortisol, TEaQCM at sigma = 5 were much higher (54% and 40% for serum; 90.3% and 42.8% for urine). Spiked samples generate TEa that is probably too high to be suitable for daily QC monitoring; however, it is crucial to verify spiked sample observed total error (TEo; 26% and 18% for serum, 60% and 30% for urine) < TEaQCM, and to use spiked sample TEo for patient result interpretation. In the absence of consensus TEa for cortisol in dogs, we suggest the use of a 12.5S rule, provided that users accept the associated level of TEaQCM also as clinical TEa for results interpretation.

Validation study of canine urine cortisol measurement with the Immulite 2000 Xpi cortisol immunoassay.

We report here validation of the Immulite 2000 Xpi cortisol immunoassay (Siemens; with kit lot numbers <550) for measurement of urine cortisol in dogs, with characterization of the precision (CV), accuracy (spiking-recovery [SR] bias), and observed total error (TEo = bias + 2CV) across the reportable range. Linearity assessed by simple linear regression was excellent. Imprecision, SR bias, and TEo increased markedly with decreasing urine cortisol concentration. Interlaboratory comparison studies determined range-based (RB) bias and average bias (AB). The 3 biases (SR, RB, and AB) and resulting TEo differed markedly. At 38.6 and 552 nmol/L (1.4 and 20 µg/dL), between-run CVs were 10% and 4.5%, respectively, and TEoRB were ~30% and 20%, respectively, similar to observations in serum in another validation study. These analytical performance parameters should be considered for urine cortisol:creatinine ratio (UCCR) result interpretation, given that, for any hypothetical errorless urine creatinine measurement, the error % on UCCR mirrors the error % on urine cortisol. Importantly, there is no commonly used interpretation threshold for UCCR, given that UCCR varies greatly depending on measurement methods and threshold computation. To date, there is no manufacturer-provided quality control material (QCM) with target values for urine cortisol with an Immulite; for Liquicheck QCM (Bio-Rad), between-run imprecision was ~5% for both QCM levels. Acceptable QC rules are heavily dependent on the desired total allowable error (TEa) for the QCM system, itself limited by the desired clinical TEa.

Validation study of canine serum cortisol measurement with the Immulite 2000 Xpi cortisol immunoassay.

We report the results of validation of canine serum cortisol determination with the Immulite 2000 Xpi cortisol immunoassay (Siemens), with characterization of precision (CV), accuracy (spiking-recovery [SR] bias), and observed total error (TEo = bias + 2CV) across the reportable range, specifically at the most common interpretation thresholds for dynamic testing. Imprecision increased at increasing rate with decreasing serum cortisol concentration and bias was low, resulting in increasing TEo with decreasing serum cortisol concentration. Inter-laboratory comparison study allowed for determination of range-based bias (RB) and average bias (AB). At 38.6 and 552 nmol/L (1.4 and 20 µg/dL), between-run CV was 10% and 7.5%, respectively, and TEo ~30% and ~20%, respectively (TEo remained similar regardless of the considered bias: SR, RB, or AB). These analytical performance parameters should be considered in the interpretation of results and for future expert consensus discussions to determine recommendations for allowable total error (TEa). Importantly, the commonly used thresholds for interpretation of results were determined ~40 y ago with different methods of measurements and computation, hence updating is desirable. Quality control material (QCM) had between-run imprecision of 4% for QCM1 and 7% for QCM2; the bias was minimal for both levels. Acceptable QC rules are heavily dependent on the desired TEa for the QCM system (TEaQCM), itself limited by the desired clinical TEa. At low TEaQCM (20-33%), almost no rules were acceptable, whereas at high TEaQCM (50%), almost all rules were acceptable; further investigation is needed to determine which TEaQCM can be guaranteed by simple QC rules.

SYSTEMATIC EVALUATION OF 106 LABORATORY REFERENCE DATA ARTICLES FROM NONDOMESTIC SPECIES PUBLISHED FROM 2014 TO 2016: ASSESSING COMPLIANCE WITH REFERENCE INTERVAL GUIDELINES.

Population-based reference intervals (RIs) are vital tools used to characterize health and disease based on laboratory values. The science and statistical basis for RI generation have evolved over the past 50 yr. Current veterinary-specific guidelines by the American Society of Veterinary Clinical Pathology exist for establishing RIs from nondomestic and wild animals. A list of 35 items that should be included during generation and publication of reference data was distilled from the currently available RI guidelines. The archives of five peer-reviewed journals were searched and 106 articles presenting laboratory reference data from nondomestic or wildlife species were identified and each reviewed by two authors to determine compliance with the list of 35 items. A compliance score was calculated as the number of articles that fulfilled the item out of the number where it would have been appropriate to fulfill the item. Most articles reported the number of reference individuals (compliance score 0.98), their partitioning demographics (compliance score 0.95), and sample collection and handling practices (compliance scores 0.97 and 0.96, respectively). Common deficiencies included omitting discussion of the validation status of the analytical methods for the species being evaluated (compliance score 0.12), documentation of use of exclusion criteria (compliance score 0.51), outlier detection (compliance score 0.43), appropriate statistical methods for the reference population (compliance score 0.34), and calculation and presentation of confidence intervals around the reference limits (compliance score 0.35). Compliance scores were not statistically different when stratified on the number of individuals in the largest and smallest evaluated group or the format of the article (full vs short format). Articles that cited RI generation guidelines fulfilled more of the required steps and provided a more complete description of their data (compliance score 0.74) than those that did not cite guidelines (compliance score 0.58). Additional attention to the science of and recommendations for RI generation is recommended to strengthen the utility of published data.