title: Bayesian evaluation of clinical diagnostic test characteristics of visual observations and remote monitoring to diagnose bovine respiratory disease in beef calves.
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: 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.
authors.affiliation: Precision Animal Solutions, Manhattan, KS 66503, United States; Department of Clinical Sciences, Kansas State University, Manhattan, KS 66506, United States. Electronic address: bradwhite@precisionanimalsolutions.com.
authors.forename: Brad J
authors.initials: BJ
authors.lastname: White
authors.affiliation: Precision Animal Solutions, Manhattan, KS 66503, United States.
authors.forename: Dan R
authors.initials: DR
authors.lastname: Goehl
authors.affiliation: Adams Land and Cattle Company, Broken Bow, NE 68822, United States.
authors.forename: David E
authors.initials: DE
authors.lastname: Amrine
authors.affiliation: Feedlot Health Management Services, Okotoks, Alberta, Canada.
authors.forename: Calvin
authors.initials: C
authors.lastname: Booker
authors.affiliation: Feedlot Health Management Services, Okotoks, Alberta, Canada.
authors.forename: Brian
authors.initials: B
authors.lastname: Wildman
authors.affiliation: Feedlot Health Management Services, Okotoks, Alberta, Canada.
authors.forename: Tye
authors.initials: T
authors.lastname: Perrett
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D048090:Bovine Respiratory Disease Complex / Q000175:diagnosis*; D002417:Cattle; D003955:Diagnostic Tests, Routine / Q000662:veterinary*; D031121:Mannheimia; D019370:Observation; D016871:Pasteurellaceae Infections / Q000175:diagnosis / Q000662:veterinary; D058998:Remote Sensing Technology / Q000662:veterinary*; D012680:Sensitivity and Specificity
keywords: Bayesian analysis; Bovine respiratory disease; Diagnostic methods; Sensitivity; Specificity