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; TEa_QCM) and the resulting probability of error detection (P_ed) for each QC rule. For the simple QC rule 1_2.5S with P_ed = 0.96 and probability of false rejection (P_fr) = 0.03, the associated TEa_QCM were 20% and 35% for serum and 28% and 24% for urine QCM1 and QCM2. If these levels of TEa_QCM are acceptable for interpretation of patient sample results, then users can internally validate the 1_2.5S 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 TEa_QCM. With spiked samples (relevant cortisol concentrations in the veterinary patient matrix) at 38.6 and 552 nmol/L of cortisol, TEa_QCM 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) < TEa_QCM, 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 1_2.5S rule, provided that users accept the associated level of TEa_QCM 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.