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Predicting bovine respiratory disease outcome in feedlot cattle using latent class analysis
Bovine respiratory disease (BRD) is the most significant disease affecting feedlot cattle. Indicators of BRD often used in feedlots such as visual signs, rectal temperature, computer-assisted lung auscultation (CALA) score, the number of BRD treatments, presence of viral pathogens, viral seroconvers...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7755173/ https://www.ncbi.nlm.nih.gov/pubmed/33247918 http://dx.doi.org/10.1093/jas/skaa381 |
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author | Blakebrough-Hall, Claudia Hick, Paul González, Luciano A |
author_facet | Blakebrough-Hall, Claudia Hick, Paul González, Luciano A |
author_sort | Blakebrough-Hall, Claudia |
collection | PubMed |
description | Bovine respiratory disease (BRD) is the most significant disease affecting feedlot cattle. Indicators of BRD often used in feedlots such as visual signs, rectal temperature, computer-assisted lung auscultation (CALA) score, the number of BRD treatments, presence of viral pathogens, viral seroconversion, and lung damage at slaughter vary in their ability to predict an animal’s BRD outcome, and no studies have been published determining how a combination of these BRD indicators may define the number of BRD disease outcome groups. The objectives of the current study were (1) to identify BRD outcome groups using BRD indicators collected during the feeding phase and at slaughter through latent class analysis (LCA) and (2) to determine the importance of these BRD indicators to predict disease outcome. Animals with BRD (n = 127) were identified by visual signs and removed from production pens for further examination. Control animals displaying no visual signs of BRD (n = 143) were also removed and examined. Blood, nasal swab samples, and clinical measurements were collected. Lung and pleural lesions indicative of BRD were scored at slaughter. LCA was applied to identify possible outcome groups. Three latent classes were identified in the best model fit, categorized as non-BRD, mild BRD, and severe BRD. Animals in the mild BRD group had a higher probability of having visual signs of BRD compared with non-BRD and severe BRD animals. Animals in the severe BRD group were more likely to require more than 1 treatment for BRD and have ≥40 °C rectal temperature, ≥10% total lung consolidation, and severe pleural lesions at slaughter. Animals in the severe BRD group were also more likely to be naïve at feedlot entry and the first BRD pull for Bovine Viral Diarrhoea Virus, Bovine Parainfluenza 3 Virus, and Bovine Adenovirus and have a positive nasal swab result for Bovine Herpesvirus Type 1 and Bovine Coronavirus. Animals with severe BRD had 0.9 and 0.6 kg/d lower overall ADG (average daily gain) compared with non-BRD animals and mild BRD animals (P < 0.001). These results demonstrate that there are important indicators of BRD severity. Using this information to predict an animal’s BRD outcome would greatly enhance treatment efficacy and aid in better management of animals at risk of suffering from severe BRD. |
format | Online Article Text |
id | pubmed-7755173 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77551732021-01-25 Predicting bovine respiratory disease outcome in feedlot cattle using latent class analysis Blakebrough-Hall, Claudia Hick, Paul González, Luciano A J Anim Sci Animal Health and Well Being Bovine respiratory disease (BRD) is the most significant disease affecting feedlot cattle. Indicators of BRD often used in feedlots such as visual signs, rectal temperature, computer-assisted lung auscultation (CALA) score, the number of BRD treatments, presence of viral pathogens, viral seroconversion, and lung damage at slaughter vary in their ability to predict an animal’s BRD outcome, and no studies have been published determining how a combination of these BRD indicators may define the number of BRD disease outcome groups. The objectives of the current study were (1) to identify BRD outcome groups using BRD indicators collected during the feeding phase and at slaughter through latent class analysis (LCA) and (2) to determine the importance of these BRD indicators to predict disease outcome. Animals with BRD (n = 127) were identified by visual signs and removed from production pens for further examination. Control animals displaying no visual signs of BRD (n = 143) were also removed and examined. Blood, nasal swab samples, and clinical measurements were collected. Lung and pleural lesions indicative of BRD were scored at slaughter. LCA was applied to identify possible outcome groups. Three latent classes were identified in the best model fit, categorized as non-BRD, mild BRD, and severe BRD. Animals in the mild BRD group had a higher probability of having visual signs of BRD compared with non-BRD and severe BRD animals. Animals in the severe BRD group were more likely to require more than 1 treatment for BRD and have ≥40 °C rectal temperature, ≥10% total lung consolidation, and severe pleural lesions at slaughter. Animals in the severe BRD group were also more likely to be naïve at feedlot entry and the first BRD pull for Bovine Viral Diarrhoea Virus, Bovine Parainfluenza 3 Virus, and Bovine Adenovirus and have a positive nasal swab result for Bovine Herpesvirus Type 1 and Bovine Coronavirus. Animals with severe BRD had 0.9 and 0.6 kg/d lower overall ADG (average daily gain) compared with non-BRD animals and mild BRD animals (P < 0.001). These results demonstrate that there are important indicators of BRD severity. Using this information to predict an animal’s BRD outcome would greatly enhance treatment efficacy and aid in better management of animals at risk of suffering from severe BRD. Oxford University Press 2020-11-28 /pmc/articles/PMC7755173/ /pubmed/33247918 http://dx.doi.org/10.1093/jas/skaa381 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) |
spellingShingle | Animal Health and Well Being Blakebrough-Hall, Claudia Hick, Paul González, Luciano A Predicting bovine respiratory disease outcome in feedlot cattle using latent class analysis |
title | Predicting bovine respiratory disease outcome in feedlot cattle using latent class analysis |
title_full | Predicting bovine respiratory disease outcome in feedlot cattle using latent class analysis |
title_fullStr | Predicting bovine respiratory disease outcome in feedlot cattle using latent class analysis |
title_full_unstemmed | Predicting bovine respiratory disease outcome in feedlot cattle using latent class analysis |
title_short | Predicting bovine respiratory disease outcome in feedlot cattle using latent class analysis |
title_sort | predicting bovine respiratory disease outcome in feedlot cattle using latent class analysis |
topic | Animal Health and Well Being |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7755173/ https://www.ncbi.nlm.nih.gov/pubmed/33247918 http://dx.doi.org/10.1093/jas/skaa381 |
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