Bayesian Analysis of Three Methods for Diagnosis of Cystic Echinococcosis in Sheep

Abstract

Diagnosis of cystic echinococcosis (CE) in sheep is essentially based on necropsy findings. Clinical symptoms can be easily overlooked, while the use of immunological tests is still not recommended for an intra vitam diagnosis. This study assessed the performances of three post-mortem laboratory methods in the diagnosis of ovine CE. In the absence of a single and accurate test as a gold standard, the results of multiple analytical tests can be combined to estimate diagnostic performance based on a Bayesian statistical approach. For this purpose, livers (n = 77), and lungs (n = 79) were sampled from adult sheep and examined using gross pathology, histopathology and molecular analyses. Data from the three diagnostic methods were analyzed using a Bayesian latent class analysis model to evaluate their diagnostic accuracy in terms of sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV). The gross pathology examination revealed excellent diagnostic capabilities in diagnosing ovine CE with an Se of 99.7 (96.7–99.8), Sp of 97.5 (90.3–99.8), PPV of 97.6 (90.5–100), and NPV of 99.7 (96.5–100). The experimental design used in this work could be implemented as a validation protocol in a quality assurance system.