Bayesian estimation of the accuracy of the calf respiratory scoring chart and ultrasonography for the diagnosis of bovine respiratory disease in pre-weaned dairy calves

Abstract

There is currently no gold standard method for the diagnosis of bovine respiratory disease (BRD) complex in Holstein pre-weaned dairy calves. Systematic thoracic ultrasonography (TUS) has been used as a proxy for BRD, but cannot be directly used by producers. The Wisconsin calf respiratory scoring chart (CRSC) is a simpler alternative, but with unknown accuracy. Our objective was to estimate the accuracy of CRSC, while adjusting for the lack of a gold standard. Two cross sectional study populations with a high BRD prevalence (n=106 pre-weaned Holstein calves) and an average BRD prevalence (n=85 pre-weaned Holstein calves) from North America were studied. All calves were simultaneously assessed using CRSC (cutoff used ≥5) and TUS (cutoff used ≥1cm of lung consolidation). Bayesian latent class models allowing for conditional dependence were used with informative priors for BRD prevalence and TUS accuracy (sensitivity (Se) and specificity (Sp)) and non-informative priors for CRSC accuracies. Robustness of the model was tested by relaxing priors for prevalence or TUS accuracy. The SeCRSC (95% credible interval (CI)) and SpCRSC were 62.4% (47.9–75.8) and 74.1% (64.9–82.8) respectively. The SeTUS was 79.4% (66.4–90.9) and SpTUS was 93.9% (88.0–97.6). The imperfect accuracy of CRSC and TUS should be taken into account when using those tools to assess BRD status.