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

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: 57.9, 70.8). The specificity of REDI (median 92.9%; PI: 88.2, 96.9) was also higher compared to VO (median 69.1%; PI: 66.3, 71.8). The differences in sensitivity and specificity resulted in REDI exhibiting higher positive and negative predictive values in both high (41.3%) and low (2.6%) prevalence situations. This research illustrates the potential of remote cattle monitoring to augment conventional methods of BRD diagnosis resulting in more accurate identification of diseased cattle.