Bayesian evaluation of clinical diagnostic test characteristics of visual observations and remote monitoring to diagnose bovine respiratory disease in beef calves

Preventive Veterinary Medicine
Brad J WhiteTye Perrett

Abstract

Accurate diagnosis of bovine respiratory disease (BRD) in beef cattle is a critical facet of therapeutic programs through promotion of prompt treatment of diseased calves in concert with judicious use of antimicrobials. Despite the known inaccuracies, visual observation (VO) of clinical signs is the conventional diagnostic modality for BRD diagnosis. Objective methods of remotely monitoring cattle wellness could improve diagnostic accuracy; however, little information exists describing the accuracy of this method compared to traditional techniques. The objective of this research is to employ Bayesian methodology to elicit diagnostic characteristics of conventional VO compared to remote early disease identification (REDI) to diagnose BRD. Data from previous literature on the accuracy of VO were combined with trial data consisting of direct comparison between VO and REDI for BRD in two populations. No true gold standard diagnostic test exists for BRD; therefore, estimates of diagnostic characteristics of each test were generated using Bayesian latent class analysis. Results indicate a 90.0% probability that the sensitivity of REDI (median 81.3%; 95% probability interval [PI]: 55.5, 95.8) was higher than VO sensitivity (64.5%; PI:...Continue Reading

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Citations

Nov 22, 2018·Animal Health Research Reviews·N K Van Engen, J F Coetzee
Feb 23, 2018·Translational Animal Science·John T RichesonBrad J White
May 27, 2020·The Veterinary Clinics of North America. Food Animal Practice·Sébastien Buczinski, Bart Pardon
Jun 20, 2018·Microbiology Spectrum·David H Lloyd, Stephen W Page
Jun 23, 2018·Microbiology Spectrum·Luca GuardabassiScott Weese

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