Bayesian evaluation of clinical diagnostic test characteristics of visual observations and remote monitoring to diagnose bovine respiratory disease in beef calves

Strategies for Bovine Respiratory Disease (BRD) Diagnosis and Prognosis: A Comprehensive Overview

Despite significant advances in vaccination strategies and antibiotic therapy, bovine respiratory disease (BRD) continues to be the leading disease affecting the global cattle industry. The etiology of BRD is complex, often involving multiple microbial agents, which lead to intricate interactions between the host immune system and pathogens during various beef production stages. These interactions present environmental, social, and geographical challenges. Accurate diagnosis is essential for effective disease management. Nevertheless, correct identification of BRD cases remains a daunting challenge for animal health technicians in feedlots. In response to current regulations, there is a growing interest in refining clinical diagnoses of BRD to curb the overuse of antimicrobials. This shift marks a pivotal first step toward establishing a structured diagnostic framework for this disease. This review article provides an update on recent developments and future perspectives in clinical diagnostics and prognostic techniques for BRD, assessing their benefits and limitations. The methods discussed include the evaluation of clinical signs and animal behavior, biomarker analysis, molecular diagnostics, ultrasound imaging, and prognostic modeling. While some techniques show promise as standalone diagnostics, it is likely that a multifaceted approach-leveraging a combination of these methods-will yield the most accurate diagnosis of BRD.

Diagnostic and Prognostic Value of Clinical Scoring and Lung Ultrasonography to Assess Pulmonary Lesions in Veal Calves

This study on veal calf respiratory disease assessed the association between an on-farm clinical scoring system and lung ultrasonography with the postmortem inspection of the lungs. The comparisons allowed the calculation of predictive values of the diagnostic methods. In total, 600 calves on an Austrian veal calf farm were examined at the beginning and the end of the fattening period. Overall, the area under the curve (AUC) for ultrasonographic scores was 0.90 (rsp = 0.78) with a sensitivity (Se) of 0.86. The specificity (Sp) was 0.78, and the positive predictive value (PPV) was 0.74. The AUC for the physical examination was 0.76 (rsp = 0.55) with a Se of 0.64, an Sp of 0.81, and a PPV of 0.69. For the combination of ultrasonography and physical examination, an AUC curve of 0.85 (rsp = 0.69) was calculated. A Se of 0.65 and a Sp of 0.88 with a PPV of 0.73 was calculated. This study concluded that both physical and ultrasonographic examination scoring are reliable examination methods for the detection of lung diseases in veal calves.

Technological Tools for the Early Detection of Bovine Respiratory Disease in Farms

Simple Summary

The inclusion of remote automatic systems that use continuous learning technology are of great interest in precision livestock cattle farming, since the average size of farms is increasing while time for individual observation is decreasing. Bovine respiratory disease is a main concern in both fattening and heifer rearing farms due to its impact on antibiotic use, loss of performance, mortality, and animal welfare. Much scientific literature has been published regarding technologies for continuous learning and monitoring of cattle’s behavior and accurate correlation with health status, including early detection of bovine respiratory disease. This review summarizes the up-to-date technologies for early diagnosis of bovine respiratory disease and discusses their advantages and disadvantages under practical conditions.

Abstract

Classically, the diagnosis of respiratory disease in cattle has been based on observation of clinical signs and the behavior of the animals, but this technique can be subjective, time-consuming and labor intensive. It also requires proper training of staff and lacks sensitivity (Se) and specificity (Sp). Furthermore, respiratory disease is diagnosed too late, when the animal already has severe lesions. A total of 104 papers were included in this review. The use of new advanced technologies that allow early diagnosis of diseases using real-time data analysis may be the future of cattle farms. These technologies allow continuous, remote, and objective assessment of animal behavior and diagnosis of bovine respiratory disease with improved Se and Sp. The most commonly used behavioral variables are eating behavior and physical activity. Diagnosis of bovine respiratory disease may experience a significant change with the help of big data combined with machine learning, and may even integrate metabolomics as disease markers. Advanced technologies should not be a substitute for practitioners, farmers or technicians, but could help achieve a much more accurate and earlier diagnosis of respiratory disease and, therefore, reduce the use of antibiotics, increase animal welfare and sustainability of livestock farms. This review aims to familiarize practitioners and farmers with the advantages and disadvantages of the advanced technological diagnostic tools for bovine respiratory disease and introduce recent clinical applications.

Wellness Management in Beef Feeder Cattle: Changing Mental Models to Support Beneficial Emergent System Behaviors

Despite evidence-based "improvements" in animal health products and management, losses to bovine respiratory disease have increased with associated animal wastage, welfare concerns, and antimicrobial use; questioning the fitness of current disease-centric paradigms for improving critical outcomes in complex adaptive systems. Systems thinking is used to model a paradigm shift from mental models based on management of failure outcomes in a flawed pass/fail dichotomy to one of managing success outcomes on a continuum. In the proposed wellness paradigm, the notion of health as absence of disease is rejected and replaced with perspective of disease as symptomatic of systems insufficiently supporting wellness.

Bovine Respiratory Syncytial Virus (BRSV) Infection Detected in Exhaled Breath Condensate of Dairy Calves by Near-Infrared Aquaphotomics

Bovine respiratory syncytial virus (BRSV) is a major contributor to respiratory disease in cattle worldwide. Traditionally, BRSV infection is detected based on non-specific clinical signs, followed by reverse transcriptase-polymerase chain reaction (RT-PCR), the results of which can take days to obtain. Near-infrared aquaphotomics evaluation based on biochemical information from biofluids has the potential to support the rapid identification of BRSV infection in the field. This study evaluated NIR spectra (n = 240) of exhaled breath condensate (EBC) from dairy calves (n = 5) undergoing a controlled infection with BRSV. Changes in the organization of the aqueous phase of EBC during the baseline (pre-infection) and infected (post-infection and clinically abnormal) stages were found in the WAMACS (water matrix coordinates) C1, C5, C9, and C11, likely associated with volatile and non-volatile compounds in EBC. The discrimination of these chemical profiles by PCA-LDA models differentiated samples collected during the baseline and infected stages with an accuracy, sensitivity, and specificity >93% in both the calibration and validation. Thus, biochemical changes occurring during BRSV infection can be detected and evaluated with NIR-aquaphotomics in EBC. These findings form the foundation for developing an innovative, non-invasive, and in-field diagnostic tool to identify BRSV infection in cattle.

Profiling Mannheimia haemolytica infection in dairy calves using near infrared spectroscopy (NIRS) and multivariate analysis (MVA)

Bovine respiratory disease (BRD) linked with Mannheimia haemolytica is the principal cause of pneumonia in cattle. Diagnosis of BRD traditionally relies on visual assessment, which can be untimely, insensitive, and nonspecific leading to inadequate treatment and further spread of disease. Near Infrared Spectroscopy (NIRS) is a rapid acquisition vibrational spectroscopy that can profile changes in biofluids, and when used in combination with multivariate analysis, has potential for disease diagnosis. This study characterizes the NIR spectral profile of blood plasma from dairy calves infected with M. haemolytica and validates the spectral biochemistry using standardized clinical and hematological reference parameters. Blood samples were collected for four days prior to (baseline), and 23 days after, a controlled intrabronchial challenge. NIR spectral profiles of blood plasma discriminated and predicted Baseline and Infected states of animal disease progression with accuracy, sensitivity, and specificity ≥ 90% using PCA–LDA models. These results show that physiological and biochemical changes occurring in the bloodstream of dairy calves during M. haemolytica infection are reflected in the NIR spectral profiles, demonstrating the potential of NIRS as a diagnostic and monitoring tool of BRD over time.

Bovine Respiratory Disease Diagnosis: What Progress Has Been Made in Clinical Diagnosis?

Bovine respiratory disease (BRD) complex is a worldwide health problem in cattle and is a major reason for antimicrobial use in young cattle. Several challenges may explain why it is difficult to make progress in the management of this disease. This article defines the limitation of BRD complex nomenclature, which may not easily distinguish upper versus lower respiratory tract infection and infectious bronchopneumonia versus other types of respiratory diseases. It then discusses the obstacles to clinical diagnosis and reviews the current knowledge of readily available diagnostic test to reach a diagnosis of infectious bronchopneumonia.

Efficacy of statistical process control procedures to identify deviations in continuously measured physiologic and behavioral variables in beef steers experimentally challenged with Mannheimia haemolytica

The objective of this experiment was to determine if statistical process control (SPC) procedures coupled with the remote continuous collection of feeding behavior patterns, accelerometer-based behaviors, and rumen temperature can accurately differentiate between animals experimentally inoculated with Mannheimia haemolytica (MH) or PBS. Thirty-six crossbred steers (BW = 352 ± 23 kg) seronegative for MH were randomly assigned to bronchoselective endoscopic inoculation with MH (n = 18) or PBS (n = 18). Electronic feed bunks were used to measure DMI and feeding behavior traits, accelerometer-based neck collars measured feeding- and activity-behavior traits, and ruminal thermo-boluses measured rumen temperature. Data were collected for 28 d prior to and following inoculation. Steers inoculated with MH exhibited elevated (P < 0.02) levels of neutrophils and rumen temperature indicating that MH challenge effectively stimulated immunologic responses. However, only nine of the MH steers exhibited increased serum haptoglobin concentrations indicative of an acute-phase protein response and one displayed clinical signs of disease. Shewhart charts (SPC procedure) were used for two analyses, and sensitivity was computed using all MH-challenged steers (n = 18), and a subset that included only MH-challenged haptoglobin-responsive steers (n = 9). Specificity was calculated using all PBS steers in both analyses. In the haptoglobin-responsive only analysis, DMI and bunk visit (BV) duration had the greatest accuracy (89%), with accuracies for head-down (HD) duration, BV frequency, time to bunk, and eating rate being less (83%, 69%, 53%, and 61%, respectively). To address the diurnal nature of rumen temperature, data were averaged over 6-h intervals, and quarterly temperature models were evaluated separately. Accuracy for the fourth quarter rumen temperature was higher (78%) than the other quarterly temperature periods (first = 56%, second = 50%, and third = 67%). In general, the accelerometer-based behavior traits were highly specific ranging from 82% for ingestion to 100% for rest, rumination, and standing. However, the sensitivity of these traits was low (0% to 50%), such that the accuracies were moderate compared with feeding behavior and rumen temperature response variables. These results indicate that Shewhart procedures can effectively identify deviations in feeding behavior and rumen temperature patterns to enable subclinical detection of BRD in beef cattle.

Antimicrobial Stewardship in Veterinary Medicine

While antimicrobial resistance is already a public health crisis in human medicine, therapeutic failure in veterinary medicine due to antimicrobial resistance remains relatively uncommon. However, there are many pathways by which antimicrobial resistance determinants can travel between animals and humans: by close contact, through the food chain, or indirectly via the environment. Antimicrobial stewardship describes measures that can help mitigate the public health crisis and preserve the effectiveness of available antimicrobial agents. Antimicrobial stewardship programs have been principally developed, implemented, and studied in human hospitals but are beginning to be adapted for other applications in human medicine. Key learning from the experiences of antimicrobial stewardship programs in human medicine are summarized in this article-guiding the development of a stewardship framework suitable for adaptation and use in both companion animal and livestock practice. The antimicrobial stewardship program for veterinary use integrates infection prevention and control together with approaches emphasizing avoidance of antimicrobial agents. The 5R framework of continuous improvement that is described recognizes the importance of executive support; highly motivated organizations and teams (responsibility); the need to review the starting position, set objectives, and determine means of measuring progress and success; and a critical focus on reducing, replacing, and refining the use of antimicrobial agents. Significant issues that are currently the focus of intensive research include improved detection and diagnosis of infections, refined dosing regimens that are simultaneously effective while not selecting resistance, searches for alternatives to antimicrobial agents, and development of improved vaccines to enhance immunity and reduce disease.

Optimization of Antimicrobial Treatment to Minimize Resistance Selection

Optimization of antimicrobial treatment is a cornerstone in the fight against antimicrobial resistance. Various national and international authorities and professional veterinary and farming associations have released generic guidelines on prudent antimicrobial use in animals. However, these generic guidelines need to be translated into a set of animal species- and disease-specific practice recommendations. This article focuses on prevention of antimicrobial resistance and its complex relationship with treatment efficacy, highlighting key situations where the current antimicrobial drug products, treatment recommendations, and practices may be insufficient to minimize antimicrobial selection. The authors address this topic using a multidisciplinary approach involving microbiology, pharmacology, clinical medicine, and animal husbandry. In the first part of the article, we define four key targets for implementing the concept of optimal antimicrobial treatment in veterinary practice: (i) reduction of overall antimicrobial consumption, (ii) improved use of diagnostic testing, (iii) prudent use of second-line, critically important antimicrobials, and (iv) optimization of dosage regimens. In the second part, we provided practice recommendations for achieving these four targets, with reference to specific conditions that account for most antimicrobial use in pigs (intestinal and respiratory disease), cattle (respiratory disease and mastitis), dogs and cats (skin, intestinal, genitourinary, and respiratory disease), and horses (upper respiratory disease, neonatal foal care, and surgical infections). Lastly, we present perspectives on the education and research needs for improving antimicrobial use in the future.

Effects of transportation on cattle health and production: a review

The goal of this review is to present a concise and critical assessment of the literature related to physiologic responses in cattle that are subjected to transportation. Over two-thirds of US cattle are transported. Understanding trends in circulating physiologic parameters is an important part of mitigating the negative effects of transportation. For the producer, linking these effects after transportation to morbidity outcomes within the first 45 days on feed (i.e. especially development of bovine respiratory disease) is critical. Physiologic parameters in circulation are of primary importance and may have value for prediction of bovine respiratory disease on arrival and for the understanding of disease pathogenesis. The results of our literature survey indicated that post-transportation immune function, increased acute phase proteins, glucocorticoids, and inflammation are a pivotal starting point for understanding disease. These potential biomarkers may have utility in identifying disease for targeted therapeutics so that traditional protocols that rely heavily on metaphylaxis can be avoided. Additional research is needed to develop strategies for physiological marker identification, treatment methods, or predictive behaviors to prevent respiratory disease before and after transport. This review examines the significant deleterious effects of transportation handling and stress, and current immune system translation and non-antimicrobial mitigation strategies.

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

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A prediction rule using thoracic ultrasound as an imperfect test for BRD diagnosis was modeled.

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Selection of the optimal threshold for case definition was proposed accounting for misclassification cost term analysis.

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Probability of active infection of the lower respiratory tract was determined for all 64 clinical signs combinations.

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.

Diagnosis of bovine dictyocaulosis by bronchoalveolar lavage technique: A comparative study using a Bayesian approach

Bovine dictyocaulosis is a pulmonary parasitic disease present in temperate countries, with potential important clinical and economic impacts. The Baermann technique is routinely used despite its low sensitivity in adult cows. Recently developed serological tests seem to offer better sensitivity, but validations of these tests in field conditions are few. We aimed to study two non-previously evaluated diagnosis methods of dictyocaulosis based on bronchoalveolar lavage sampling (BAL), which allows finding lungworm stages in the lungs as well as determination of eosinophilia. We compared them to the Baermann technique and serological tests. As no gold standard was available, we performed a Bayesian analysis by the simultaneous use of latent class and mixture models. The study was carried out during the 2015 pasture season on 60 adult cows originating from 11 herds with clinical signs of dictyocaulosis, and 10 apparently healthy cows originating from the teaching herd of VetAgro Sup, in France. Prevalence of infection was highly variable among herds with clinical signs (10-90%). Despite a maximal specificity (100%), the sensitivity of parasitological methods was low (7.4% for the Baermann sedimentation and 24.7% for the examination of BAL fluids). Better results were observed with serology (Se = 74.9%, Sp = 85.5%) with an optimal cut-off value estimated at 0.397 for the optical density ratio. Even better results were obtained with the count of eosinophil in BAL (Se = 89.4%, Sp = 85.2%) with an optimal cut-off value estimated at 4.77% for the eosinophil proportion. The BAL is a relevant diagnostic method of dictyocaulosis for practitioners due to the opportunity to perform two analyses (direct parasitic research and the eosinophil count) and to its good sensitivity and specificity.

Using advanced technologies to quantify beef cattle behavior

For decades, we have relied upon visual observation of animal behavior to define clinical disease, assist in breeding selection, and predict growth performance. Limitations of visual monitoring of cattle behavior include training of personnel, subjectivity, and brevity. In addition, extensive time and labor is required to visually monitor behavior in large numbers of animals, and the prey instinct of cattle to disguise abnormal behaviors in the presence of a human evaluator is problematic. More recently, cattle behavior has been quantified objectively and continuously using advanced technologies to assess animal welfare, indicate lameness or disease, and detect estrus in both production and research settings. The current review will summarize three methodologies for quantification of cattle behavior with focus on U.S. beef production systems; 1) three-axis accelerometers that quantify physical behavior, 2) systems that document feeding and watering behavior via radio frequency, and 3) triangulation or global positioning systems to determine location and movement of cattle within a pen or pasture. Furthermore, advances in Wi-Fi and radio frequency technology have allowed many of these systems to operate remotely and in real-time and efforts are underway to develop commercial applications that may allow early detection of respiratory or other cattle diseases in the production environment. Current challenges with commercial application of technology for early disease detection include establishment of an appropriate algorithm to ensure maximum sensitivity and specificity, reliable and repeatable data collection in harsh environments, cost:benefit, and integration with traditional methodology for clinical diagnosis. Advanced technologies have also allowed cattle researchers to determine temporal variance in behavior or variability between experimental treatments. However, these data sets are typically very large and challenges exist regarding statistical analysis and reporting.

Comparison of two indirect ELISA coating antigens for the detection of dairy cow antibodies against Pasteurella multocida

The ELISA is recognized as an efficient diagnostic tool for antibody detection, but there is no standard ELISA assay for detection of antibodies against hemorrhagic septicemia (HS) in cattle. The present study reports on an indirect ELISA assay for antibody detection of HS in dairy cows, and evaluates the sensitivity (Se) and specificity (Sp) of the method using a Bayesian approach. An indirect ELISA was developed with two types of heat extract antigens, Pasteurella multocida strains P-1256 and M-1404, as coating antigens. A checkerboard titration was employed using dairy cow sera immunized with P. multocida bacterin and colostrum-deprived calf sera. The concentrations of heat extract antigen (160μg/mL), sample serum (1:100) and goat anti-bovine immunoglobulin G labeled with horseradish peroxidase (1:2000) were optimal for the assay. The cut-off values were 0.147 and 0.128 for P-1256 and M-1404 coating antigens, and there were no differences in the results of tests with positive and negative sera (p<0.05). The characteristics of three diagnostic tests were evaluated using a one-population Bayesian model, assuming conditional dependence between two types of coating antigen-based ELISAs and indirect hemagglutination assay (IHA). A total of 415 sera samples from dairy cows without HS vaccination and no history of disease were tested. The Se and Sp of the P-1256 and M-1404 ELISAs were higher than those of the IHA. The Se and Sp of the P-1256 ELISA were 90.3% and 90.1%, while the Se and Sp of the M-1404 ELISA were 92.1% and 71.9%. The median values of Se and Sp from the IHA were 36.0% and 58.2%.