Evaluation of histopathology, PCR, and qPCR to detect Mikrocytos mackini in oysters Crassostrea gigas using Bayesian latent class analysis

Abstract

Latent class analysis (LCA) is a common method to evaluate the diagnostic sensitivity (DSe) and specificity (DSp) for pathogen detection assays in the absence of a perfect reference standard. Here we used LCA to evaluate the diagnostic accuracy of 3 tests for the detection of Mikrocytos mackini in Pacific oysters Crassostrea gigas: conventional polymerase chain reaction (PCR), real-time quantitative PCR (qPCR), and histopathology. A total of 802 Pacific oysters collected over 12 sampling events from 9 locations were assessed. Preliminary investigations indicated that standard LCA assumptions of test independence and constant detection accuracy across locations were likely unrealistic. This was mitigated by restructuring the LCA in a Bayesian framework to include test-derived knowledge about pathogen prevalence and load for categorizing populations into 2 classes of infection severity (low or high) and assessing separate DSe and DSp estimates for each class. Median DSp estimates were high (>96%) for all 3 tests in both population classes. DSe estimates varied between tests and population classes but were consistently highest for qPCR (87-99%) and lowest for histopathology (21-51%). Acknowledging that detection of M. mackini may be fitted to multiple diagnostic and management purposes, qPCR had the highest DSe while maintaining similar DSp to both conventional PCR and histopathology and thus is generally well-suited to most applications.