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…
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Treatment was more successful and required fewer antibiotic treatments per animal when employing the REDI system for respiratory infection detection.
“Research indicated that treatment was more successful and required fewer antibiotic treatments per animal when employing this system for respiratory infection detection [232].”
From Strategies for Bovine Respiratory Disease (BRD) Diagnosis and Prognosis: A Comprehensive Overview (2024) · cites this paper as [232]
The REDI system proved more accurate than manual checks by barn staff for identifying clinical cases.
“With no gold standard for identifying clinical cases, the REDI system proved more accurate than manual checks by barn staff.”
From Strategies for Bovine Respiratory Disease (BRD) Diagnosis and Prognosis: A Comprehensive Overview (2024) · cites this paper as [232]
Cites
Bacterial Pathogens of the Bovine Respiratory Disease Complex
Griffin, D., Chengappa, M. M., Kuszak, J., & McVey, D. S. (2010) · Veterinary Clinics of North America: Food Animal Practice · doi:10.1016/j.cvfa.2010.04.004 · Cited by 465 · see citing works
Wolfger, B., Schwartzkopf-Genswein, K. S., Barkema, H. W., Pajor, E. A., Levy, M., & Orsel, K. (2015) · Journal of Animal Science · doi:10.2527/jas.2013-8030 · Cited by 70 · cited 2× · see citing works
“…74–80 R b i d a c o c l t t o b i d b c 2 m s o a c R c d r i t g EDI system continuously monitors cattle behavior while VO is ased on distinct observation events, and research on remote mon- toring of behavior indicates BRD may be identified earlier in the isease process (Wolfger et al., 2015b).”
BackgroundBannikov, G. A., Hinds, C. A., Rajala-Schultz, P. J., Premanandan, C., Rings, D. M., & Lakritz, J. (2011) · Veterinary Immunology and Immunopathology · doi:10.1016/j.vetimm.2010.08.004 · Cited by 25 · see citing works
Theurer, M. E., White, B. J., Larson, R. L., Holstein, K. K., & Amrine, D. E. (2014) · Journal of the American Veterinary Medical Association · doi:10.2460/javma.245.11.1279 · Cited by 24 · see citing works
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Timsit, E., Dendukuri, N., Schiller, I., & Buczinski, S. (2016) · Preventive Veterinary Medicine · doi:10.1016/j.prevetmed.2016.11.006 · Cited by 112 · see citing works
Bovine Respiratory Disease Diagnosis
Buczinski, S., & Pardon, B. (2020) · Veterinary Clinics of North America: Food Animal Practice · doi:10.1016/j.cvfa.2020.03.004 · Cited by 89 · see citing works
Strategies for Bovine Respiratory Disease (BRD) Diagnosis and Prognosis: A Comprehensive Overview
Kamel, M. S., Davidson, J. L., & Verma, M. S. (2024) · Animals · doi:10.3390/ani14040627 · Cited by 76 · see citing works
“The remote early disease identification (REDI) system, coupled with a Bayesian latent class model [231,232], utilized real-time animal position data to monitor feedlot calves.”
BackgroundMethodUsing advanced technologies to quantify beef cattle behavior1
Richeson, J. T., Lawrence, T. E., & White, B. J. (2018) · Translational Animal Science · doi:10.1093/tas/txy004 · Cited by 55 · see citing works
Self-citationBuczinski, S., Fecteau, G., Dubuc, J., & Francoz, D. (2018) · Preventive Veterinary Medicine · doi:10.1016/j.prevetmed.2018.05.004 · Cited by 43 · see citing works
“The absence of a gold standard test is a recurrent issue in BRD research (White and Renter, 2009; Abdallah et al., 2016; Timsit et al., 2016; White et al., 2016).”
BackgroundPredictive Modeling of Bovine Respiratory Disease Outcomes in Feedlot Cattle: A Narrative Review
Wisnieski, L., Amrine, D. E., & Renter, D. G. (2021) · Livestock Science · doi:10.1016/j.livsci.2021.104666 · Cited by 23 · see citing works
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- Authors
- White, B. J., Goehl, D. R., Amrine, D. E., Booker, C., Wildman, B., & Perrett, T.
- Affiliations
- Kansas State University (US)
- Corresponding author
- White, Brad J.
- Venue
- Preventive Veterinary Medicine
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 126
- Pages
- 74-80
- ISSN
- 0167-5877 (ISSN-L)
- OpenAlex
- W2280176900
- Cited by
- 33
- References
- 4
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 4 · 2022: 3 · 2021: 3 · 2020: 2 · 2018: 10 · 2017: 4 · 2016: 1
- FWCI
- 7.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Preventive Veterinary Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Veterinary › Small Animals › Animal health and immunology
- Life Sciences › Immunology and Microbiology › Microbiology › Microbial infections and disease research
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- bovine respiratory disease, beef calves, visual observation, diagnostic accuracy, sensitivity and specificity, Bayesian latent class analysis, positive and negative predictive value, prevalence, antimicrobial use
- MeSH terms
- Animals, Bayes Theorem, Cattle, Diagnostic Tests, Routine, Sensitivity and Specificity, Pasteurellaceae Infections, Observation, Mannheimia, Bovine Respiratory Disease Complex, Remote Sensing Technology
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