Predicting Bovine Respiratory Disease Outcome Using Latent Class Analysis

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.