Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework

Abstract

Bovine respiratory disease complex is a major cause of illness in dairy calves. The diagnosis of active infection of the lower respiratory tract is challenging on daily basis in the absence of accurate clinical signs. Clinical scoring systems such as the Californian scoring system, are appealing but were developed without considering the imperfection of reference standard tests used for case definition. This study used a Bayesian latent class model to update Californian prediction rules. The results of clinical examination and ultrasound findings of 608 preweaned dairy calves were used. A model accounting for imperfect accuracy of thoracic ultrasound examination was used to obtain updated weights for the clinical signs included in the Californian scoring system. There were 20 points (95% Bayesian credible intervals: 11-29) for abnormal breathing pattern, 16 points (95% BCI: 4-29) for ear drop/head tilt, 16 points (95% BCI: 9-25) for cough, 10 points (95% BCI: 3-18) for the presence of nasal discharge, 7 points (95% BCI: -1 to 8) for rectal temperature ≥39.2 °C, and -1 points (95% BCI: -9 to 8) for the presence of ocular discharge. The optimal cut-offs were determined using the misclassification cost-term term (MCT) approach with different possible scenarios of expected prevalence and different plausible ratio of false negative costs/false positive costs. The predicted probabilities of active infection of the lower respiratory tract were also obtained using posterior densities of the main logistic regression model. Depending on the context, cut-off varying from 9 to 16 can minimized the MCT. The optimal cut-off decreased when expected prevalence of disease and false negative/false positive ratio increased.

Keywords: Accuracy; Bayesian; Latent class; Pneumonia.

Fig. 1

Diagram representation of the latent class model used for determining the clinical score accuracy for the diagnosis of active infection of the lower respiratory tract in dairy calves. The rectangles correspond to observed variables (ultrasound result and clinical score results) and the oval represents the latent variable (active infection of the lower respiratory tract). The circles represent the sensitivity and specificity of ultrasound (Se_us and Sp_us) and clinical score (Se_c and Sp_c).

Fig. 2

Repartition of clinical signs observed in 608 preweaned dairy calves in association with their and consolidation status. For each clinical sign included in the scoring system, the calves with no abnormal clinical sign are indicated in green and the calves with abnormal clinical sign are indicated in red. The relative number of calves with lung consolidation conditional on their clinical sign status (normal or abnormal) is graphically represented as the relative length of dark (no consolidation) vs pale (consolidation) colored bars. Lung consolidation was defined as a calf with at least one site of visible lung tissue ≥1 cm of depth when performing thoracic ultrasonography. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Median and 95% credible intervals of predicted probabilities and ultrasound accuracy for the diagnosis of active infection of the lower respiratory tract in dairy calves compared with experts’ conditional means probabilities. M1: Model 1 is obtained using informative prior information from thoracic ultrasonography accuracy and clinical profiles BRD probability (see Table 2). M2: Model 2 is obtained using informative prior information from thoracic ultrasonography accuracy and non-informative priors for clinical profiles BRD probability (see Table 2). M3: Model 3 is obtained using non-informative prior for thoracic ultrasonography accuracy and clinical profiles BRD probability (see Table 2). p1, …p7 are corresponding to 7 different calves’ clinical profiles (see Table 1). Prior_E1, Prior_E2 are the conditional means priors obtained from expert 1 and 2 consultation (see Table 1).

Fig. 4

Practical application of clinical assessment of dairy calves using 6 different clinical signs. The decision tree is based on 64 possible different clinical signs profiles to predict the probability (95% credible intervals) of an active infection of the lower respiratory tract based on posterior densities from model 1 (Table 2). The calves with normal vs rapid or abnormal breathing pattern are presented in Fig. 4A and 4B respectively. The clinical signs are dichotomous as reported by Love et al. (2014).

Fig. 4

Practical application of clinical assessment of dairy calves using 6 different clinical signs. The decision tree is based on 64 possible different clinical signs profiles to predict the probability (95% credible intervals) of an active infection of the lower respiratory tract based on posterior densities from model 1 (Table 2). The calves with normal vs rapid or abnormal breathing pattern are presented in Fig. 4A and 4B respectively. The clinical signs are dichotomous as reported by Love et al. (2014).

Fig. 5

Sensitivity and specificity of clinical score for the diagnosis of active infection of the lower respiratory tract in dairy calves and misclassification cost-term of the cut-offs used depending on expected respiratory disease prevalence and relative value of false negative to false positive cases. The 95% credible intervals are represented for sensitivity and specificity. MCT: misclassification cost term, Se: sensitivity, Sp: specificity, r: ratio of the cost of false negative case/false positive case.