scholarly journals Predicting bovine respiratory disease outcome in feedlot cattle using latent class analysis

2020 ◽  
Vol 98 (12) ◽  
Author(s):  
Claudia Blakebrough-Hall ◽  
Paul Hick ◽  
Luciano A González
Keyword(s):  
Latent Class Analysis ◽  
Respiratory Disease ◽  
Rectal Temperature ◽  
Nasal Swab ◽  
Latent Class ◽  
Bovine Respiratory Disease ◽  
Feedlot Cattle ◽  
Disease Outcome ◽  
Computer Assisted ◽  
Class Analysis

Abstract 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.

scholarly journals Predicting Bovine Respiratory Disease Outcome Using Latent Class Analysis

2020 ◽  
Author(s):  
C. Blakebrough-Hall ◽  
P. Hick ◽  
L.A. González
Keyword(s):  
Latent Class Analysis ◽  
Respiratory Disease ◽  
Rectal Temperature ◽  
Nasal Swab ◽  
Latent Class ◽  
Bovine Respiratory Disease ◽  
Average Daily Gain ◽  
Disease Outcome ◽  
Computer Assisted ◽  
Class Analysis

Abstract BackgroundBovine 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, 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. Latent class analysis was applied to identify possible outcome groups. Results 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 visual signs of BRD compared to animals with severe BRD. Animals in the severe BRD group were more likely to require more than one treatment for BRD and have ≥ 40oC 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 first BRD pull and have a positive nasal swab result for some BRD viruses. Lower overall ADG (average daily gain) was also associated with severe BRD (P < 0.001). Conclusions 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.

scholarly journals Predicting Bovine Respiratory Disease Outcome Using Latent Class Analysis

2020 ◽  
Author(s):  
C. Blakebrough-Hall ◽  
P. Hick ◽  
L.A. González
Keyword(s):  
Latent Class Analysis ◽  
Respiratory Disease ◽  
Latent Class ◽  
Bovine Respiratory Disease ◽  
Disease Outcome ◽  
Class Analysis

Abstract The authors have withdrawn this preprint due to erroneous posting.

scholarly journals Subphenotypes of nonsteroidal antiinflammatory disease‐exacerbated respiratory disease identified by latent class analysis

Allergy ◽  
2020 ◽  
Vol 75 (4) ◽  
pp. 831-840 ◽  
Author(s):  
Natalia Celejewska‐Wójcik ◽  
Krzysztof Wójcik ◽  
Maria Ignacak‐Popiel ◽  
Adam Ćmiel ◽  
Katarzyna Tyrak ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Respiratory Disease ◽  
Latent Class ◽  
Class Analysis

Latent class analysis identifies multimorbidity patterns in pigs with respiratory disease

2020 ◽  
pp. 105209
Author(s):  
Tamsin S. Barnes ◽  
Annierica Lajarca ◽  
Rona Bernales ◽  
Paul John J. Alvaran ◽  
Flora Shiela Abe ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Respiratory Disease ◽  
Latent Class ◽  
Class Analysis

scholarly journals PSII-1 Blood metabolomics for biomarker discovery and the diagnosis of Bovine Respiratory Disease

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 238-238
Author(s):  
claudia R Blakebrough-Hall ◽  
Anthony Dona ◽  
Michael D’occhio ◽  
Joe McMeniman ◽  
Luciano Gonzalez
Keyword(s):  
Respiratory Disease ◽  
Rectal Temperature ◽  
Biomarker Discovery ◽  
Bovine Respiratory Disease ◽  
Model Development ◽  
Lung Lesion ◽  
Feedlot Cattle ◽  
The Body ◽  
Validation Dataset ◽  
Treatment Protocols

Abstract Bovine Respiratory Disease (BRD) is the leading cause of morbidity and mortality in Australian feedlot cattle. Diagnosis for BRD is based on visual scoring of illness and the use of rectal temperature above a defined level to trigger treatment protocols. These methods often have a low accuracy at diagnosing BRD. Blood metabolomics monitors alterations in small metabolites in the body and can be used to indicate the presence of disease. The aim of the current study was to search for biomarkers for BRD and develop alternate diagnosis methods for BRD using the blood metabolome profile of feedlot steers. Visually BRD affected (n = 148) and visually healthy (n = 152) steers were removed from their group pens for clinical assessment and blood sampling for metabolomics analysis. Lung lesions indicative of BRD were scored for all trial animals upon slaughter. A non-targeted metabolomics approach based on nuclear magnetic resonance (NMR) spectrometry was used to search for blood biomarkers using classification and regression trees. The data were split into training and validation datasets for model development. Visual diagnosis (VD), visual + clinical diagnosis (VCD; visually sick and elevated rectal temperature or lung auscultation score), and lung lesion diagnosis (LLD; lung consolidation ≥ 10% or pleurisy score ≥ 2) were used as reference diagnosis methods for BRD. Metabolomics demonstrated a high accuracy at detecting BRD in the validation dataset when using the VD (Acc=0.85, Se=0.82, SP = 0.87) and VCD (Acc=0.81, Se=0.88, SP = 0.74), but was less accurate at detecting animals defined as sick using the LLD (Acc=0.74, Se=0.38, SP = 0.89) (Table 1). The models selected nine metabolites important in differentiating sick and healthy animals. The results suggest the blood metabolome is a useful indicator of BRD status and could therefore be used for confirmation of BRD in feedlot cattle.

scholarly journals Certain subphenotypes of aspirin-exacerbated respiratory disease distinguished by latent class analysis

2014 ◽  
Vol 133 (1) ◽  
pp. 98-103.e6 ◽  
Author(s):  
Grazyna Bochenek ◽  
Joanna Kuschill-Dziurda ◽  
Krystyna Szafraniec ◽  
Hanna Plutecka ◽  
Andrzej Szczeklik ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Respiratory Disease ◽  
Latent Class ◽  
Class Analysis ◽  
Aspirin Exacerbated Respiratory Disease

Factors associated with Bovine Respiratory Disease case fatality in feedlot cattle

2021 ◽  
Author(s):  
C Blakebrough-Hall ◽  
P Hick ◽  
T J Mahony ◽  
L A González
Keyword(s):  
Respiratory Disease ◽  
Nasal Swab ◽  
Animal Health ◽  
Bovine Respiratory Disease ◽  
Case Fatality ◽  
Mixed Effects ◽  
Feedlot Cattle ◽  
Blood Metabolites ◽  
Factors Associated ◽  
Production Factors

Abstract Bovine Respiratory Disease (BRD) is the primary cause of morbidity and mortality in cattle feedlots. There is a need to understand what animal health and production factors are associated with increased mortality risk due to BRD. The aim of the present study was to explore factors associated with BRD case fatality in feedlot cattle. Four pens totalling 898 steers were monitored daily for visual signs of BRD such as difficult breathing and coughing, and animals exhibiting signs of BRD were taken to the hospital shed for further examination and clinical measures. Blood samples were obtained at feedlot entry and at time of first BRD pull from animals diagnosed with BRD (n=121) and those that died due to BRD confirmed by post-mortem examination (n=16; 13.2% case fatality rate). Mixed-effects linear regression models were used to estimate differences in animal health and production factors and the relative concentrations of 34 identified blood metabolites between animals that survived versus those that died. Generalised linear mixed-effects models were used to obtain the odds of being seronegative (at both feedlot entry and first BRD pull) to five BRD viruses and having a positive nasal swab result at the time of first pull in died and survived animals. Animals that died from BRD had lower average daily gain (ADG), reduced weight at first BRD pull, higher visual BRD scores and received more treatments for BRD compared to animals that survived BRD (P &lt; 0.05). The odds of being seronegative for bovine viral diarrhea virus 1 (BVDV-1) was 5.66 times higher for animals that died compared to those that survived (P = 0.013). The odds of having a positive bovine coronavirus nasal swab result were 13.73 times higher in animals that died versus those that survived (P = 0.007). Animals that died from BRD had higher blood concentrations of α glucose chain, β-hydroxybutyrate, leucine, phenylalanine and pyruvate compared to those that survived (P &lt; 0.05). Animals that died from BRD had lower concentrations of acetate, citrate and glycine compared to animals that survived (P &lt; 0.05). The results of the current study suggest that ADG to first BRD pull, weight at first BRD pull, visual BRD score, the number of BRD treatments, seronegativity to BVDV-1, virus positive to BCoV nasal swab, and that certain blood metabolites are associated with BRD case fatality risk. The ability of these measures to predict the risk of death due to BRD needs further research.

Sign in / Sign up

Export Citation Format

Share Document