Development, Application, and Quality Control of Serology Assays Used for Diagnostic Monitoring of Laboratory Nonhuman Primates

• Published in: ILAR Journal Vol. 49; no. 2; pp. 157 - 169
• Main Author: Simmons, Joe H.
• Format: Journal Article Web Resource
• Language: English
• Published: England Institute for Laboratory Animal Research 01.01.2008; Oxford University Press
• Subjects: Animals; Primates - blood; Quality Control; Reproducibility of Results; Serologic Tests - methods; Serologic Tests - standards; diagnostics; nonhuman primate; quality control; assay; validation; serology
• ISSN: 1084-2020; 1930-6180
• DOI: 10.1093/ilar.49.2.157

The careful development, validation, and implementation of serodiagnostic assays can provide reliable results that make them a valuable tool in microbial quality control for nonhuman primates. This article includes identification and description of the components of assay development, including formulas for calculating the number of positive serum samples needed for assay validation and methods for calculating their diagnostic sensitivity and specificity. To ensure that assays are performing within predetermined specifications, there must be a quality control system that includes appropriate system and sample suitability controls as well as mechanisms to track assay performance over time. The section on quality assurance includes definitions of precision and accuracy in assay performance, and how to interpret these two factors using the Levey-Jennings chart, Westgard's rules, and other monitoring methods. Because all serologic assays are prone to false positive and false negative results, it is essential to interpret all diagnostic test results using both the expected prevalence of disease in the population and the population-specific assay performance characteristics that are determined during assay validation. The discussion on interpreting diagnostic test results also includes guidelines for calculating the positive and negative predictive values of an assay and for interpreting results based on the disease prevalence of the test population. A glossary provides definitions of commonly used terms.