Literature DB >> 27931931

Diagnostic accuracy of clinical illness for bovine respiratory disease (BRD) diagnosis in beef cattle placed in feedlots: A systematic literature review and hierarchical Bayesian latent-class meta-analysis.

E Timsit1, N Dendukuri2, I Schiller3, S Buczinski4.   

Abstract

Diagnosis of bovine respiratory disease (BRD) in beef cattle placed in feedlots is typically based on clinical illness (CI) detected by pen-checkers. Unfortunately, the accuracy of this diagnostic approach (namely, sensitivity [Se] and specificity [Sp]) remains poorly understood, in part due to the absence of a reference test for ante-mortem diagnosis of BRD. Our objective was to pool available estimates of CI's diagnostic accuracy for BRD diagnosis in feedlot beef cattle while adjusting for the inaccuracy in the reference test. The presence of lung lesions (LU) at slaughter was used as the reference test. A systematic review of the literature was conducted to identify research articles comparing CI detected by pen-checkers during the feeding period to LU at slaughter. A hierarchical Bayesian latent-class meta-analysis was used to model test accuracy. This approach accounted for imperfections of both tests as well as the within and between study variability in the accuracy of CI. Furthermore, it also predicted the SeCI and SpCI for future studies. Conditional independence between CI and LU was assumed, as these two tests are not based on similar biological principles. Seven studies were included in the meta-analysis. Estimated pooled SeCI and SpCI were 0.27 (95% Bayesian credible interval: 0.12-0.65) and 0.92 (0.72-0.98), respectively, whereas estimated pooled SeLU and SpLU were 0.91 (0.82-0.99) and 0.67 (0.64-0.79). Predicted SeCI and SpCI for future studies were 0.27 (0.01-0.96) and 0.92 (0.14-1.00), respectively. The wide credible intervals around predicted SeCI and SpCI estimates indicated considerable heterogeneity among studies, which suggests that pooled SeCI and SpCI are not generalizable to individual studies. In conclusion, CI appeared to have poor Se but high Sp for BRD diagnosis in feedlots. Furthermore, considerable heterogeneity among studies highlighted an urgent need to standardize BRD diagnosis in feedlots.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Diagnostic test; Imperfect reference; Latent class model; Lung lesion; Pen-rider; Shipping fever

Mesh:

Year:  2016        PMID: 27931931     DOI: 10.1016/j.prevetmed.2016.11.006

Source DB:  PubMed          Journal:  Prev Vet Med        ISSN: 0167-5877            Impact factor:   2.670


  28 in total

1.  Differential haptoglobin responsiveness to a Mannheimia haemolytica challenge altered immunologic, physiologic, and behavior responses in beef steers.

Authors:  Lauren R Wottlin; Gordon E Carstens; William C Kayser; William E Pinchak; Jennifer M Thomson; Valerie Copié; Galen P O'Shea-Stone
Journal:  J Anim Sci       Date:  2020-12-22       Impact factor: 3.159

2.  Efficacy of statistical process control procedures to identify deviations in continuously measured physiologic and behavioral variables in beef steers experimentally challenged with Mannheimia haemolytica.

Authors:  William C Kayser; Gordon E Carstens; Ira L Parsons; Kevin E Washburn; Sara D Lawhon; William E Pinchak; Eric Chevaux; Andrew L Skidmore
Journal:  J Anim Sci       Date:  2020-02-01       Impact factor: 3.159

Review 3.  Do antimicrobial mass medications work? A systematic review and meta-analysis of randomised clinical trials investigating antimicrobial prophylaxis or metaphylaxis against naturally occurring bovine respiratory disease.

Authors:  Keith Edward Baptiste; Niels Christian Kyvsgaard
Journal:  Pathog Dis       Date:  2017-09-29       Impact factor: 3.166

4.  Evaluation of reticulorumen temperature boluses for the diagnosis of subclinical cases of bovine respiratory disease in feedlot cattle.

Authors:  Emilie A-L Flattot; Tony R Batterham; Edouard Timsit; Brad J White; Joe P McMeniman; Michael P Ward; Luciano A González
Journal:  J Anim Sci       Date:  2021-12-01       Impact factor: 3.159

5.  Efficacy of statistical process control procedures to identify deviations in continuously measured physiological and behavioral variables in beef heifers resulting from an experimentally combined viral-bacterial challenge.

Authors:  William Christian Kayser; Gordon E Carstens; Ira Loyd Parsons; Kevin E Washburn; Sara D Lawhon; William E Pinchak; Eric Chevaux; Andrew L Skidmore
Journal:  J Anim Sci       Date:  2021-09-01       Impact factor: 3.338

6.  Using Canine Olfaction to Detect Bovine Respiratory Disease: A Pilot Study.

Authors:  Aiden E Juge; Nathaniel J Hall; John T Richeson; Courtney L Daigle
Journal:  Front Vet Sci       Date:  2022-07-01

7.  Cattle adapted to tropical and subtropical environments: social, nutritional, and carcass quality considerations.

Authors:  Reinaldo F Cooke; Courtney L Daigle; Philipe Moriel; Stephen B Smith; Luis O Tedeschi; João M B Vendramini
Journal:  J Anim Sci       Date:  2020-02-01       Impact factor: 3.159

8.  Evaluation of statistical process control procedures to monitor feeding behavior patterns and detect onset of bovine respiratory disease in growing bulls.

Authors:  William C Kayser; Gordon E Carstens; Kirby S Jackson; William E Pinchak; Amarnath Banerjee; Yu Fu
Journal:  J Anim Sci       Date:  2019-03-01       Impact factor: 3.159

9.  Differential haptoglobin responsiveness to a Mannheimia haemolytica challenge altered immunologic, physiologic, and behavior responses in beef steers.

Authors:  Lauren R Wottlin; Gordon E Carstens; William C Kayser; William E Pinchak; Jennifer M Thomson; Valerie Copié; Galen P O'Shea-Stone
Journal:  J Anim Sci       Date:  2021-01-01       Impact factor: 3.159

10.  Comparison of a traditional bovine respiratory disease control regimen with a targeted program based upon individualized risk predictions generated by the Whisper On Arrival technology.

Authors:  Jason S Nickell; John P Hutcheson; David G Renter; David A Amrine
Journal:  Transl Anim Sci       Date:  2021-05-06
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.