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Development and validation of a clinical respiratory disease scoring system for guiding treatment decisions in veal calves using a Bayesian framework

  • J. Berman
    Correspondence
    Corresponding author
    Affiliations
    Département des sciences cliniques, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC J2S 2M2, Canada
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  • D. Francoz
    Affiliations
    Département des sciences cliniques, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC J2S 2M2, Canada
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  • A. Abdallah
    Affiliations
    Département des sciences cliniques, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC J2S 2M2, Canada

    Department of Animal Medicine, Faculty of Veterinary Medicine, Zagazig University, Zagazig City 44511, Egypt
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  • S. Dufour
    Affiliations
    Département de Pathologie et Microbiologie, Faculté de médecine vétérinaire, Université de Montréal, St-Hyacinthe, QC J2S 2M2, Canada
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  • S. Buczinski
    Affiliations
    Département des sciences cliniques, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC J2S 2M2, Canada
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Open AccessPublished:October 04, 2022DOI:https://doi.org/10.3168/jds.2021-21695

      ABSTRACT

      Active infectious bovine respiratory disease (BRD) is an infection of the airways that needs to be diagnosed correctly so that appropriate treatment can be initiated. The simplest and most practical test to detect active BRD in dairy calves raised for veal is the detection and interpretation of clinical signs by producers or technicians. However, the clinical scoring system currently available for veal calves lacks sensitivity and specificity, contributing to economic losses and high use of antimicrobials. An accurate and reliable batch-level test to detect active BRD is essential to tailor antimicrobial use and reduce economic losses in veal calves. The objective of this study was therefore to develop and validate a new veal calf respiratory clinical scoring system (VcCRS), including reliable clinical signs (cough, ear droop or head tilt) and increased rectal temperature to detect active BRD in batches of veal calves housed individually, and to describe the accuracy of the scoring system for identifying batches of veal calves to treat. During 2017 to 2018, clinical examination, thoracic ultrasonography (TUS) and a haptoglobin concentration (Hap) were prospectively performed on 800 veal calves housed individually in Québec, Canada. Deep nasopharyngeal swabs were performed on 250 veal calves. A Bayesian latent class model accounting for imperfect accuracy of TUS and Hap was used to obtain weights for the clinical signs and develop the VcCRS. The VcCRS was then validated externally in 3 separate data sets. Finally, the applicability of the VcCRS at batch level was determined. We found that calves with 2 of the following findings—cough, unilateral or bilateral ear droop or head tilt, or increased rectal temperature ≥39.7°C—were considered positive and had a 31% chance of having active BRD. Without at least 2 of these 2 findings, a calf had a 100% chance of not having active BRD. At the batch level, we found that a batch with ≥3 positive calves among 10 calves sampled 2 wk after arrival at the fattening unit had a 94% chance of having an active BRD prevalence ≥10%. A batch with <3 positive calves had a 95% chance of not having an active BRD prevalence ≥10%. In this study, we developed a simple individual and batch-level score that is reliable across examiners and performs effectively in the detection of active BRD in veal calves. The implementation of this VcCRS in the veal calf industry would promote the elaboration of a protocol tailoring antimicrobial use.

      Graphical Abstract

      Key words

      INTRODUCTION

      Infectious bovine respiratory disease (BRD) is a disease of the respiratory tract caused by viruses and bacteria (
      • Woolums A.R.
      Lower respiratory tract disease.
      ). Most of the time, the infection starts in the upper respiratory tract before descending to the lower respiratory tract and causing inflammation and lesions to the lung parenchyma (defined in this article as active BRD;
      • Panciera R.J.
      • Confer A.W.
      Pathogenesis and pathology of bovine pneumonia.
      ;
      • Zeineldin M.
      • Lowe J.
      • Aldridge B.
      Contribution of the mucosal microbiota to bovine respiratory health.
      ). Active BRD needs to be treated with anti-inflammatories, antimicrobials, or both treatment approaches (
      • Woolums A.R.
      Lower respiratory tract disease.
      ;
      • Buczinski S.
      • Pardon B.
      Bovine respiratory disease diagnosis: What progress has been made in clinical diagnosis?.
      ). Once the inflammation and infection are resolved, it is common to observe lung scar tissue (defined here as inactive BRD;
      • Ollivett T.L.
      • Caswell J.L.
      • Nydam D.V.
      • Duffield T.
      • Leslie K.E.
      • Hewson J.
      • Kelton D.
      Thoracic ultrasonography and bronchoalveolar lavage fluid analysis in Holstein calves with subclinical lung lesions.
      ). Because dairy calves intended for veal production are commonly commingled during collection, transportation, and housing in a fattening unit, active BRD is highly prevalent in this production system (50% of calves with lung lesions at slaughter;
      • Leruste H.
      • Brscic M.
      • Heutinck L.F.
      • Visser E.K.
      • Wolthuis-Fillerup M.
      • Bokkers E.A.
      • Stockhofe-Zurwieden N.
      • Cozzi G.
      • Gottardo F.
      • Lensink B.J.
      • van Reenen C.G.
      The relationship between clinical signs of respiratory system disorders and lung lesions at slaughter in veal calves.
      ) and could be responsible for an important part of mortality (16–50% of deaths are caused by BRD;
      • Lava M.
      • Pardon B.
      • Schupbach-Regula G.
      • Keckeis K.
      • Deprez P.
      • Steiner A.
      • Meylan M.
      Effect of calf purchase and other herd-level risk factors on mortality, unwanted early slaughter, and use of antimicrobial group treatments in Swiss veal calf operations.
      ;
      • Winder C.B.
      • Kelton D.F.
      • Duffield T.F.
      Mortality risk factors for calves entering a multi-location white veal farm in Ontario, Canada.
      ), lower carcass weight, and lower carcass quality (
      • van der Mei J.
      • van den Ingh T.
      Lung and pleural lesions of veal calves at slaughter and their relationship with carcass weight.
      ;
      • Pardon B.
      • Hostens M.
      • Duchateau L.
      • Dewulf J.
      • De Bleecker K.
      • Deprez P.
      Impact of respiratory disease, diarrhea, otitis and arthritis on mortality and carcass traits in white veal calves.
      ). To prevent and control active BRD in veal calves, antimicrobials are commonly used, representing up to 73% of antimicrobials used during the production cycle (
      • Lava M.
      • Schupbach-Regula G.
      • Steiner A.
      • Meylan M.
      Antimicrobial drug use and risk factors associated with treatment incidence and mortality in Swiss veal calves reared under improved welfare conditions.
      ). These antimicrobials can be given as individual treatment, but the vast majority (>95%) are given as metaphylactic therapy—that is, the simultaneous antimicrobial therapy of clinically healthy calves and of calves that have clinical signs of active BRD in a shared compartment (
      • Pardon B.
      • Catry B.
      • Dewulf J.
      • Persoons D.
      • Hostens M.
      • De Bleecker K.
      • Deprez P.
      Prospective study on quantitative and qualitative antimicrobial and anti-inflammatory drug use in white veal calves.
      ). Reducing individual treatment and, above all, optimal targeting of metaphylactic group treatments are key factors to substantially reduce antimicrobial use in the veal industry. Accurate and reliable individual and group-level diagnosis of active BRD is therefore essential to tailor antimicrobial use in veal production.
      In Canada, the province of Québec is a major player in veal calf production, producing around 80% of Canadian veal calves (
      • Producteurs de Bovins du Québec
      At a glance.
      ). Considering the relatively high number of calves in a fattening unit in Québec (mean = 470 calves;
      • Producteurs de Bovins du Québec
      At a glance.
      ), the simplest and most practical test to detect active BRD in a veal fattening unit is the detection and interpretation of clinical signs by non-veterinarians (e.g., producers or technicians). However, due to the variety of infectious agents involved in BRD, clinical signs vary in intensity and duration, which can make clinical diagnosis difficult (
      • McGuirk S.M.
      • Peek S.F.
      Timely diagnosis of dairy calf respiratory disease using a standardized scoring system.
      ). Additionally, clinical diagnosis in veal calves has been reported to be variable among different examiners (
      • Buczinski S.
      • Faure C.
      • Jolivet S.
      • Abdallah A.
      Evaluation of inter-observer agreement when using a clinical respiratory scoring system in pre-weaned dairy calves.
      ;
      • Berman J.
      • Francoz D.
      • Abdallah A.
      • Dufour S.
      • Buczinski S.
      Evaluation of inter-rater agreement of the clinical signs used to diagnose bovine respiratory disease in individually housed veal calves.
      ). A clinical scoring system assigns values to each predictor, which are used to determine a total score, thus making it possible to assess disease more objectively than with unstructured clinical evaluation alone (
      • Hayes G.
      • Mathews K.
      • Kruth S.
      • Doig G.
      • Dewey C.
      Illness severity scores in veterinary medicine: What can we learn?.
      ;
      • Love W.J.
      • Lehenbauer T.W.
      • Kass P.H.
      • Van Eenennaam A.L.
      • Aly S.S.
      Development of a novel clinical scoring system for on-farm diagnosis of bovine respiratory disease in pre-weaned dairy calves.
      ). A simple, objective, and reliable clinical respiratory scoring chart (CRSC) would therefore be a useful tool to improve and standardize active BRD identification in veal calves.
      Currently, 3 CRSC to detect BRD in pre-weaning dairy-breed calves have been published. The earliest used a grading system of 0 to 3 for the following findings and clinical signs: elevated rectal temperature, nasal discharge, cough, ocular discharge, and ear droop or head tilt (WiCRSC;
      • McGuirk S.M.
      Disease management of dairy calves and heifers.
      ). Although this clinical scoring system used weights and decision rules, it did not use quantitative methods to assign these weights. Moreover, WiCRSC was reported to be unreliable between examiners with minimal training (
      • Buczinski S.
      • Faure C.
      • Jolivet S.
      • Abdallah A.
      Evaluation of inter-observer agreement when using a clinical respiratory scoring system in pre-weaned dairy calves.
      ) and had only moderate diagnostic accuracy [screening sensitivity (Se) and specificity (Sp) of 46–62% and 74–91%, respectively;
      • Buczinski S.
      • Ollivett T.L.
      • Dendukuri N.
      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.
      ;
      • Love W.J.
      • Lehenbauer T.W.
      • Van Eenennaam A.L.
      • Drake C.M.
      • Kass P.H.
      • Farver T.B.
      • Aly S.S.
      Sensitivity and specificity of on-farm scoring systems and nasal culture to detect bovine respiratory disease complex in preweaned dairy calves.
      ]. Another CRSC was developed to circumvent these issues in pre-weaned dairy calves (CaCRSC;
      • Love W.J.
      • Lehenbauer T.W.
      • Kass P.H.
      • Van Eenennaam A.L.
      • Aly S.S.
      Development of a novel clinical scoring system for on-farm diagnosis of bovine respiratory disease in pre-weaned dairy calves.
      ). This was later adapted and evaluated, accounting for the imperfect reference standard definition in pre-weaning dairy calves in Québec (QcCaCRSC;
      • Buczinski S.
      • Fecteau G.
      • Dubuc J.
      • Francoz D.
      Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework.
      ). The CaCRSC and QcCaCRSC used the same clinical signs as WiCRSC but dichotomized them (presence vs. absence). They also included an additional sign: breathing quality. Moreover, the weights assigned were determined quantitatively. However, the performances of CaCRSC and QcCaCRSC were reported to be similar to that of WiCRSC, with Se and Sp of 47% and 87%, respectively, for CaCRSC (
      • Love W.J.
      • Lehenbauer T.W.
      • Van Eenennaam A.L.
      • Drake C.M.
      • Kass P.H.
      • Farver T.B.
      • Aly S.S.
      Sensitivity and specificity of on-farm scoring systems and nasal culture to detect bovine respiratory disease complex in preweaned dairy calves.
      ), and Se varying from 67% to 83% and Sp of 69% to 83% for QcCaCRSC (
      • Buczinski S.
      • Fecteau G.
      • Dubuc J.
      • Francoz D.
      Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework.
      ). Different reasons could explain these moderate diagnostic performances. First, the accuracy measures of WiCRSC, CaCRSC, and QcCaCRSC were validated using different imperfect reference tests for comparison, which could increase interpretation variability as a function of both the lesions and the agents involved. Second, those scores included clinical signs that do not have high inter-rater reliability (
      • Buczinski S.
      • Faure C.
      • Jolivet S.
      • Abdallah A.
      Evaluation of inter-observer agreement when using a clinical respiratory scoring system in pre-weaned dairy calves.
      ;
      • Berman J.
      • Francoz D.
      • Abdallah A.
      • Dufour S.
      • Buczinski S.
      Evaluation of inter-rater agreement of the clinical signs used to diagnose bovine respiratory disease in individually housed veal calves.
      ), which could increase variability across examiners beyond clinical variability and, thus, across studies.
      We recently reported the reliability of the respiratory clinical signs (i.e., nasal discharge, cough, ocular discharge, ear droop or head tilt, and respiration) commonly used to detect respiratory disease in veal calves by different types of persons involved in veal calves' health monitoring (producers, technicians, and veterinarians;
      • Berman J.
      • Francoz D.
      • Abdallah A.
      • Dufour S.
      • Buczinski S.
      Evaluation of inter-rater agreement of the clinical signs used to diagnose bovine respiratory disease in individually housed veal calves.
      ). We showed that induced cough (presence or absence) and ear droop or head tilt (absence or presence of slight unilateral ear droop, bilateral ear droop, or head tilt) were the most reliable clinical signs compared with all other clinical signs that were assessed (Cohen's kappa ≥ 0.6;
      • Berman J.
      • Francoz D.
      • Abdallah A.
      • Dufour S.
      • Buczinski S.
      Evaluation of inter-rater agreement of the clinical signs used to diagnose bovine respiratory disease in individually housed veal calves.
      ). In presence of active BRD, inflammation of the trachea causes spontaneous or easily induced cough. Increased coughing frequency when using an algorithm for continuous monitoring of calves' sounds was 99.2% specific and 50.3% sensitive for disease detection in dairy calves (
      • Vandermeulen J.
      • Bahr C.
      • Johnston D.
      • Earley B.
      • Tullo E.
      • Fontana I.
      • Guarino M.
      • Exadaktylos V.
      • Berckmans D.
      Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds.
      ). Ear droop or head tilt occurred in case of pain or depression, or if otitis was conjointly present. In the latter case, pneumonia is commonly associated with otitis media (
      • Francoz D.
      • Fecteau G.
      • Desrochers A.
      • Fortin M.
      Otitis media in dairy calves: A retrospective study of 15 cases (1987 to 2002).
      ;
      • Gosselin V.B.
      • Francoz D.
      • Babkine M.
      • Desrochers A.
      • Nichols S.
      • Doré E.
      • Bédard C.
      • Parent J.
      • Fairbrother J.-H.
      • Fecteau G.
      A retrospective study of 29 cases of otitis media/interna in dairy calves.
      ). Elaborating a new score by adding those clinical signs to the objective measure of rectal temperature to investigate fever caused by the infectious process would reduce variability due to examiners and, therefore, would likely increase assessment reliability.
      The objective of the current study was therefore to develop and validate a new clinical scoring system (VcCRS) and its corresponding chart, including reliable clinical signs to detect active BRD in veal calves housed individually, and to describe its accuracy for identifying batches of veal calves to treat metaphylactically.

      MATERIALS AND METHODS

      We proceeded in 3 steps: (1) developing a new diagnostic score (VcCRS), (2) validating the developed score at the individual level using external validation, and (3) modeling the application of the score at the batch level. The TRIPOD guidelines (Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis) were used to facilitate reporting on the design, conduct, and results of the current study (Supplemental File S1; https://dataverse.harvard.edu/dataverse/clinical-scoring-system;
      • Berman J.
      TRIPODS. Harvard Dataverse, V1.
      ;
      • Moons K.G.M.
      • Altman D.G.
      • Reitsma J.B.
      • Ioannidis J.P.
      • Macaskill P.
      • Steyerberg E.W.
      • Vickers A.J.
      • Ransohoff D.F.
      • Collins G.S.
      Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and elaboration.
      ). The study protocol was approved by the Comité d'éthique de l'utilization des animaux de l'Université de Montréal (17_rech-1898; Saint-Hyacinthe, QC, Canada).

      Step 1: Developing a New Clinical Score (VcCRS): Attributing Weights to Each Clinical Sign Using a Quantitative Method and Choosing a Cut-Off to Detect Active BRD (800 Veal Calves)

      Study Population

      During a prospective cross-sectional study performed from October 1, 2017, to December 20, 2018, a total of 800 veal calves housed individually were randomly recruited from 80 batches (10 calves per batch) of 51 commercial veal calf fattening units in Québec (Délimax, Veaux Lourds Ltée, Saint-Hyacinthe, Québec, Canada, and Les Aliments Prolacto Inc., Villeroy, Québec, Canada). All fattening units performed all-in/all-out management. Because the number of calves present in every unit was known before the visit and each box on the farms was numbered, 10 boxes' numbers were randomly chosen before the visit (RANDOM function in Excel; Microsoft Corp.). Whenever a box was empty due to early mortality, the next random number was selected.
      The calves were mostly obtained from multiple local dairy farms after commingling through auction markets (
      • Buczinski S.
      • Fecteau G.
      • Blouin L.
      • Villettaz-Robichaud M.
      Factors affecting dairy calf price in auction markets in Québec, Canada: 2008–2019.
      ). Data were collected within 2 wk after arrival at the fattening unit. We intervened before the main peak incidence of active BRD in veal calves, generally occurring around 3 wk after arrival (
      • Miller W.M.
      • Harkness J.W.
      • Richards M.S.
      • Pritchard D.G.
      Epidemiological studies of calf respiratory disease in a large commercial veal unit.
      ;
      • Pardon B.
      • De Bleecker K.
      • Hostens M.
      • Callens J.
      • Dewulf J.
      • Deprez P.
      Longitudinal study on morbidity and mortality in white veal calves in Belgium.
      ). At that time, calves were about 3 to 4 wk old and were housed in individual duckboard pens. Multiple batches of calves (i.e., groups of calves that arrived at the farm to be fattened together) were considered per farm, and the selected batches were distributed across all 4 seasons. Data collection was performed in the morning (between 0900 and 1100 h), between meals.

      Data Collection

      Sex (male or female), breed (Holstein, Jersey, Red Holstein, Ayrshire), and treatment received before sampling (antimicrobial, anti-inflammatory, or both) were recorded. On the same day, each calf underwent a physical examination, in which the main clinical signs hypothesized to be of value for the scoring system were measured. For that purpose, we selected only the most reliable clinical signs (cough, ear droop or head tilt), as determined previously in
      • Berman J.
      • Francoz D.
      • Abdallah A.
      • Dufour S.
      • Buczinski S.
      Evaluation of inter-rater agreement of the clinical signs used to diagnose bovine respiratory disease in individually housed veal calves.
      , and increased rectal temperature as predictors. Moreover, for each calf, thoracic ultrasonography (TUS) and a haptoglobin concentration (Hap) measurement were conducted successively, as described in the next section. These latter 2 tests were conducted to estimate the true disease status (active or non-active BRD). Finally, a sample of 250 veal calves from the last 25 sampled batches underwent a deep nasopharyngeal swab to describe the bacteria present in the studied population.

      Clinical Signs

      A complete physical examination was performed on each calf by the same experienced operators (A.A., J.B., or S.B.). The presence of abnormalities upon the physical examination (e.g., diarrhea, navel infection, arthritis, mass, skin or eye disorders) was recorded. The presence of respiratory clinical signs (i.e., nasal discharge, ocular discharge, induced or spontaneous cough, abnormal respiration, and ear droop or head tilt) was recorded based on the 2-level reliable combination reported previously by
      • Berman J.
      • Francoz D.
      • Abdallah A.
      • Dufour S.
      • Buczinski S.
      Evaluation of inter-rater agreement of the clinical signs used to diagnose bovine respiratory disease in individually housed veal calves.
      and summarized in Supplemental File S2 (https://dataverse.harvard.edu/dataverse/clinical-scoring-system;
      • Berman J.
      Supplemental methods. Harvard Dataverse, V1.
      ). Increased rectal temperature was considered when rectal temperature was ≥39.7°C, as reported in feedlots (
      • Timsit E.
      • Assié S.
      • Quiniou R.
      • Seegers H.
      • Bareille N.
      Early detection of bovine respiratory disease in young bulls using reticulo-rumen temperature boluses.
      ,
      • Timsit E.
      • Bareille N.
      • Seegers H.
      • Lehebel A.
      • Assie S.
      Visually undetected fever episodes in newly received beef bulls at a fattening operation: Occurrence, duration, and impact on performance.
      ). Because the physical examination was performed first, the operators were blinded to results of both reference test (TUS and Hap).

      Deep Nasopharyngeal Swab

      A deep nasopharyngeal swab was taken as previously reported from the right or left nostril (
      • Godinho K.S.
      • Sarasola P.
      • Renoult E.
      • Tilt N.
      • Keane S.
      • Windsor G.D.
      • Rowan T.G.
      • Sunderland S.J.
      Use of deep nasopharyngeal swabs as a predictive diagnostic method for natural respiratory infections in calves.
      ) and placed in transport medium (BBL Port-A-Cul Tubes, Becton, Dickinson and Company) for routine bacterial and mycoplasma cultures. All laboratory analyses were performed in a diagnostic laboratory accredited by the American Association of Veterinary Laboratory Diagnosticians. Details are available in Supplemental File S2.

      Reference Tests

      In the absence of a gold standard to measure active BRD, the true status of active BRD was considered as a latent variable and was investigated via the following 2 reference tests.

      Thoracic Ultrasonography

      Bilateral TUS was performed immediately after the physical examination by the same experienced operators (A.A., J.B., or S.B.), using a 7.5-MHz linear probe (Imago, ECM), according to the method described by
      • Ollivett T.L.
      • Buczinski S.
      On-farm use of ultrasonography for bovine respiratory disease.
      ; details in Supplemental File S2). Active BRD detected by TUS was considered to be present if the maximal depth of consolidation observed was ≥3 cm (TUS positive;
      • Berman J.
      • Francoz D.
      • Dufour S.
      • Buczinski S.
      Bayesian estimation of sensitivity and specificity of systematic thoracic ultrasound exam for diagnosis of bovine respiratory disease in pre-weaned calves.
      ). The operators were blinded to Hap results but not to the physical examinations.

      Haptoglobin Concentration

      Blood samples from jugular veins were collected for each calf (tube without anticoagulants) immediately after both physical examination and TUS. Within 2 h after collection, samples were centrifuged at 1,500 × g for 15 min at approximately 20°C, and the serum was stored in aliquots at −20°C until analysis by the University of Guelph Animal Health Laboratory (Guelph, ON, Canada). Before analysis, serum samples were thawed at room temperature. Serum Hap content was measured in duplicate by the hemoglobin binding capacity method (
      • Skinner J.G.
      • Brown R.A.
      • Roberts L.
      Bovine haptoglobin response in clinically defined field conditions.
      ), using an automated analyzer (Cobas 6000 c 501, Roche Diagnostics). The lower limit of quantification was 0.3 μmol/L (0.03 g/L); the inter-assay coefficient of variation (CV) was 6.5%; and the intra-assay CV was 0.9%. In the absence of an established cut-off in veal calves, active BRD according to Hap concentration was considered to be present if the concentration of Hap was ≥2.5 μmol/L (0.25 g/L), based on the distribution of our data and as reported by

      Timsit, E., S. Assié, and N. Bareille. 2009. Failure to detect bovine respiratory disease in newly received young beef cattle by visual appraisal. Proceedings of the 12th Symposium of the International Society for Veterinary Epidemiology and Economics, Durban, South Africa.

      in feedlots (Hap positive; details in Supplemental File S2).

      VcCRS Development

      Calf was the unit of interest. Analyses were performed using SAS version 9.4 (SAS Institute Inc.) and OpenBUGS version 3.2.3, rev. 1012 (MRC Biostatistics Unit).

      Assignment of Weight to Each Clinical Sign

      Score weights were attributed individually (univariable) for each selected clinical sign (cough, ear droop or head tilt) or increased rectal temperature (3 univariate models), to avoid underestimating the influence of individual signs due to their interaction with other signs. Briefly, we quantified the association between 1 selected clinical sign or increased rectal temperature and the real probability of active BRD using Bayesian univariable mixed logistic regression models where the dependent variable was the real status of active BRD (a latent unmeasured variable) and the predictor was the selected clinical sign or increased rectal temperature (a measured observation). Using these models, we determined the weight (w) to attribute to each individual dichotomous predictor (j) simply by using the coefficient reported in the model for this predictor. For the 3 different predictors, the true unmeasured probability of active BRD in the ith calf from the jth batch of the kth farm (PrBRDijk+) was expressed by the following formula:
      logit(PrBRDijk+)=β0+w×Selectedpredictorijk+μBatchjk+μFarmk.


      Thus, farm (µFarm) and batch (µBatch) random intercepts accounted for the data structure, with calves clustered within batches, and batches within farms.
      Because of the absence of a gold standard to measure the target condition, we used a latent class logistic regression analysis (LCM regression; Figure 1a, details in Supplemental File S2) to associate the observed clinical sign (predictor) with the real status of active BRD (latent variable), following a previously reported framework (
      • McInturff P.
      • Johnson W.O.
      • Cowling D.
      • Gardner I.A.
      Modelling risk when binary outcomes are subject to error.
      ;
      • Buczinski S.
      • Fecteau G.
      • Dubuc J.
      • Francoz D.
      Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework.
      ). The probability that a calf had active BRD (PrBRDi+) was estimated using the observed TUS result for that calf and the Se and Sp of TUS. Briefly, the probability of the ith calf for testing TUS-positive (PrTUS+) was described as a function of the probability of the calf being truly affected by the target condition (i.e.,activeBRD,PrBRDi+) as
      PrTUSijk+=PrBRDijk+×SeTUS+(1PrBRDijk+)×(1SpTUS).


      For the ith calf, the latent class variable (active BRD; Yijk) was assumed to be a Bernoulli event defined by the probability of having active BRD (PrBRD+):
      YijkBernoulli(PrBRDijk+).


      Figure thumbnail gr1
      Figure 1Diagram of the latent class models (LCM): LCM regression and LCM-TUS performances (a) and LCM validation (b) used for determining and validating the clinical scoring system chart (VcCRSC) to detect active bovine respiratory disease (BRD) in veal calves. Priors of each model are shown. PrBRDi+ = probability that calf i has active bovine respiratory disease; wj = weight coefficient for predictor j (j = batch or herd; k = farm); μFarm accounted for data structure with calves clustered within a specific farm; μBatch accounted for data structure with calves clustered within specific batches; SeTUS = sensitivity of thoracic ultrasonography; SpTUS = specificity of thoracic ultrasonography; SeHap = sensitivity of haptoglobin; SpHap = specificity of haptoglobin; SeVcCRSC = sensitivity of VcCRSC; SpVcCRSC = specificity of VcCRSC; prevalence = prevalence of active BRD.

      Choice of Priors

      In this Bayesian framework, we had to describe the prior available knowledge (as prior distributions) on the intercept (β0), the clinical sign's coefficient (w), the mean and variance of the batch (µBatch jk) and farm (µFarm k) random intercepts, and finally, TUS Se and Sp.
      Priors on w were determined considering a diffuse range of odds ratios between 0.01 and 100 (back-transformed for use in a logit scale as a range of coefficients between −2 and 4.6), corresponding to the normal distribution w ∼ Normal (1.4, 1.7) and intercept εj ∼ Normal (1.4, 1.7):
      µFarm ∼ Normal (0; τ) and τ ∼ Gamma (0.1, 0.1);
      []


      µBatch ∼ Normal (0; τ) and τ ∼ Gamma (0.1, 0.1).


      The choice of Gamma (0.1, 0.1) for precision specification was considered as reasonably non-informative (
      • Gelman A.
      Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper).
      ).
      In LCM-TUS performances, we assumed that TUS and Hap were conditionally independent because Hap and TUS assess different biological processes (
      • Berman J.
      • Francoz D.
      • Dufour S.
      • Buczinski S.
      Bayesian estimation of sensitivity and specificity of systematic thoracic ultrasound exam for diagnosis of bovine respiratory disease in pre-weaned calves.
      ). Another assumption of the model was that the TUS accuracy was the same for all calves included in the study (
      • Buczinski S.
      • Fecteau G.
      • Dubuc J.
      • Francoz D.
      Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework.
      ). Beta distributions (β) were used as informative priors for all parameters of interest to optimize precision (Se and Sp of TUS, Se and Sp of Hap, and active BRD prevalence;
      • Dunson D.B.
      Commentary: Practical advantages of Bayesian analysis of epidemiologic data.
      ;
      • Gustafson P.
      On model expansion, model contraction, identifiability and prior information: Two illustrative scenarios involving mismeasured variables.
      ). The choice of priors is detailed in Supplemental File S2 and shown in Figure 1a.

      Sample Size Calculation

      The sample size of 800 calves was obtained a priori to optimize the accurate estimation of active BRD status by physical examination, TUS, and Hap (i.e., with a 95% confidence interval varying by ± 2.5–12.5% of Se and Sp for each test) and considering an expected active BRD prevalence of 20%.

      From the Logistic Analysis to the VcCRS

      The coefficients obtained from the regression analysis were used to define the specific sign score weight (W). The scores' weights were obtained after rounding logistic regression coefficient parameters multiplied by 10 (
      • Moons K.G.M.
      • Harrell F.E.
      • Steyerberg E.W.
      Should scoring rules be based on odds ratios or regression coefficients?.
      ;
      • Toll D.B.
      • Janssen K.J.M.
      • Vergouwe Y.
      • Moons K.G.M.
      Validation, updating and impact of clinical prediction rules: A review.
      ). At the end of the 3 different univariable analyses for the 3 selected predictors, a specific score with the different scores weight was built as follows:
      Specific score = WCough × Cough (presence or absence) + WEar droop or head tilt × Ear droop or head tilt (slight uni- or bilateral ear droop or head tilt) + WTemperature × Temperature (≥39.7°C).
      []


      Assessment of Model Sensitivity to Priors

      The probability that a calf had active BRD (PrBRDi+) was estimated with the posterior distribution of Se and Sp of Hap instead of the Se and Sp of TUS (Table 3; sensitivity analysis scenario).

      Software

      The model was based on a total of 20,000 iterations using a 5,000-iteration burn-in. Three different chains with different initial values were run for each model. Rapid mixing and stationary distribution were sought as signs of good convergence to the posterior distribution. The convergence of the models was checked using visual trace plots and Gelman-Rubin statistic plots. Autocorrelation was detected using autocorrelation plots, and thinning was performed when required. The posterior distributions of each parameter were reported as medians and the corresponding 95% Bayesian credible interval (BCI).

      Determination of Optimal Cut-Off

      To investigate the optimal cut-off for VcCRS to detect active BRD, the posterior distributions of Se and Sp of VcCRS were computed using another Bayesian LCM (LCM validation; Figure 1b; details in Supplemental File S2) to compare the 3 tests, VcCRS, TUS, and Hap, in the development population across a range of possible cut-off values (0, 6, 9, 10, 15, 16, 19, and 25). The priors were the same as used in LCM-TUS performances for Se and Sp of TUS, Se and Sp of Hap, and prevalence of active BRD. Non-informative priors were used for Se and Sp of VcCRS corresponding to a β (1.0; 1.0) distribution. Priors are presented in Figure 1b.
      A misclassification cost-term (MCT) analysis was conducted as described by
      • Greiner M.
      Two-graph receiver operating characteristic (TG-ROC): Update version supports optimisation of cut-off values that minimise overall misclassification costs.
      . Briefly, MCT analysis is a powerful tool to illustrate the robustness of the optimal cut-off, because it considers not only the Se and Sp of VcCRS but also the prevalence of active BRD (Prev) and the cost ratio between false negatives and false positives. The MCT can be plotted for different cost ratios of false negatives to false positives (r), making it possible to develop a cut-off that accounts for different cost ratios associated with test misclassification. The exact plausible ranges for the relative costs of r are presently unknown for veal calves. We used wide ranges that were obtained in a previous study in which 4 different experts were asked to determine this value in feedlot calves (
      • Buczinski S.
      • Rademacher R.
      • Tripp H.
      • Edmonds M.
      • Johnson E.
      • Dufour S.
      Assessment of l-lactatemia as a predictor of respiratory disease recognition and severity in feedlot steers.
      ). In the absence of specific studies on veal calves reporting this ratio, we assumed plausible ratios of r = 1:1, r = 3:1, and r = 8:1, indicating that the cost of a false negative case is generally higher than that of a false positive case (
      • Buczinski S.
      • Fecteau G.
      • Dubuc J.
      • Francoz D.
      Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework.
      ). The MCT was calculated for each specific cut-off using the following formula:
      MCT = (1 − Prev) × (1 − SpVcCRS) + r × Prev × (1 − SeVcCRS).


      The minimum value of MCT was considered as the value that minimizes the misclassification costs (
      • Greiner M.
      Two-graph receiver operating characteristic (TG-ROC): Update version supports optimisation of cut-off values that minimise overall misclassification costs.
      ;
      • Buczinski S.
      • Fecteau G.
      • Dubuc J.
      • Francoz D.
      Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework.
      ). To determine the variability of the optimal cut-off with the prevalence, the MCT analysis was performed for different batches' active BRD prevalence scenarios: low (Prev = 5%), medium (Prev = 10%), and high prevalence (Prev = 25%).

      Step 2: External Validation of the Developed Score and Estimation of Its Performance at the Individual Level Using Different Populations (209, 313, and 722 Veal Calves)

      The VcCRS was validated using 2 separate data sets from previous studies (population 1,
      • Berman J.
      • Francoz D.
      • Dufour S.
      • Buczinski S.
      Bayesian estimation of sensitivity and specificity of systematic thoracic ultrasound exam for diagnosis of bovine respiratory disease in pre-weaned calves.
      ; population 2,

      Morin, M., J. Dubuc, D. Francoz, and S. Buczinski. 2020. Randomized clinical trial of intranasal vaccination with Once PMH IN. BOVILIS Coronavirus Vaccine placebo on prevalence of lung consolidation diarrhea and health parameters in veal calves. Research report.

      ) that measured the same predictors (cough, ear droop or head tilt, and increased rectal temperature) but were sampled previously by other investigators (temporal validation;
      • Moons K.G.M.
      • Altman D.G.
      • Reitsma J.B.
      • Ioannidis J.P.
      • Macaskill P.
      • Steyerberg E.W.
      • Vickers A.J.
      • Ransohoff D.F.
      • Collins G.S.
      Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and elaboration.
      ). The target condition (active BRD) was measured identically by a Bayesian latent class model in population 1 (TUS and Hap;
      • Berman J.
      • Francoz D.
      • Dufour S.
      • Buczinski S.
      Bayesian estimation of sensitivity and specificity of systematic thoracic ultrasound exam for diagnosis of bovine respiratory disease in pre-weaned calves.
      ) but only with TUS in population 2 (

      Morin, M., J. Dubuc, D. Francoz, and S. Buczinski. 2020. Randomized clinical trial of intranasal vaccination with Once PMH IN. BOVILIS Coronavirus Vaccine placebo on prevalence of lung consolidation diarrhea and health parameters in veal calves. Research report.

      ). Details of the data sets are shown in Table 1. The Se, Sp, positive predictive value (PPV), negative predictive value (NPV) (i.e., the probability of a calf with or without active BRD receiving a positive or negative test result, respectively), positive likelihood ratio [LR+; i.e., percentage of calves with active BRD having a positive test result (Se), divided by the percentage of calves without active BRD but deemed positive based on the test result (i.e., false positive fraction = 1 − Sp)], and negative likelihood ratio [LR−; i.e., percentage of calves with active BRD classified as negative based on the test result (i.e., false negative fraction = 1 − Se), divided by the percentage of calves without active BRD with a negative test result (Sp)] were estimated by LCM analysis (LCM validation; Figure 1b, details in Supplemental File S2) to compare 2 or 3 imperfect tests [TUS, Hap (only population 1), and VcCRS] in 1 or 2 populations. Thus, 3 LCM were used: (1) a LCM where TUS and Hap were compared with the VcCRS in population 1; (2) a LCM where TUS was compared with VcCRS in population 2; and (3) a LCM where TUS was compared with VcCRS in 2 distinct populations (1 and 2). The model's priors are shown in Figure 1b.
      Table 1Demographic and clinical features of the validation populations for a clinical respiratory disease scoring system for veal calves
      Predictors (cough, increased temperature, and ear droop or head tilt) were measured identically as in the score development population but sampled previously by other investigators (temporal validation). The target condition (active bovine respiratory disease) was estimated by the same reference tests (thoracic ultrasonography and serum haptoglobin concentration) as in the developing population in population 1, but only by thoracic ultrasonography in population 2.
      ItemDevelopment populationPopulation 1
      Population from Berman et al., 2019.
      Population 2
      Population from Morin et al., 2020.
      Calves (no.)800209513
      Age3–4 wk3–4 wk3–4 wk
      Male, no. (%)756 (94.5)194 (92.8)Not available
      Holsteins768188Not available
      Batches (no.)8014
      Farms5112
      Sampling datesOctober 2017–December 2018July and October 2015February–May 2019
      PredictorsNasal discharge Ocular discharge Ear droop or head tilt Abnormal respiration CoughNasal discharge Ocular discharge Ear droop or head tilt Abnormal respiration CoughNasal discharge Ocular discharge Ear droop or head tilt Cough
      Imperfect reference testsThoracic ultrasonography Haptoglobin dosageThoracic ultrasonography Haptoglobin dosageThoracic ultrasonography
      1 Predictors (cough, increased temperature, and ear droop or head tilt) were measured identically as in the score development population but sampled previously by other investigators (temporal validation). The target condition (active bovine respiratory disease) was estimated by the same reference tests (thoracic ultrasonography and serum haptoglobin concentration) as in the developing population in population 1, but only by thoracic ultrasonography in population 2.
      2 Population from
      • Berman J.
      • Francoz D.
      • Dufour S.
      • Buczinski S.
      Bayesian estimation of sensitivity and specificity of systematic thoracic ultrasound exam for diagnosis of bovine respiratory disease in pre-weaned calves.
      .
      3 Population from

      Morin, M., J. Dubuc, D. Francoz, and S. Buczinski. 2020. Randomized clinical trial of intranasal vaccination with Once PMH IN. BOVILIS Coronavirus Vaccine placebo on prevalence of lung consolidation diarrhea and health parameters in veal calves. Research report.

      .

      Step 3: Simulation: Comparing and Choosing Interpretation for Batch-Level Treatment Decision (800 Veal Calves from 80 Batches)

      We use the term “batch” or “herd” as a general term for any cluster or aggregate ≥100 veal (
      • Producteurs de Bovins du Québec
      At a glance.
      ). To improve the applicability of the VcCRS in veal production, we estimated its batch-level PPV [HPPV, i.e., the probability of a positive batch (i.e., with ≥active BRD prevalence threshold) receiving a positive batch-level test result] and batch-level NPV [HNPV, i.e., the probability of a negative batch (i.e., with <active BRD prevalence threshold) receiving a negative batch-level test result]. The active BRD prevalence threshold can vary according to several factors, including the calves (price paid in the auction market, quality, origin), the anticipated meat marketing context, specific farm risk factors (ventilation, caretakers), the season, and the agent involved. We therefore decided to report HPPV and HNPV for a range of relevant active BRD prevalence thresholds in veal calves (i.e., 5, 10, 15, 20, 25, and 30%) according to personal communication with the veal industry in Québec (F. Beaulac, Delimax, Saint Hyacinthe, Québec, Canada, and R. Jean, Clinique Vétérinaire de Saint Valier, Saint-Valier, Québec, Canada) and a study on a feedlot (
      • Baptiste K.E.
      • Kyvsgaard N.C.
      Do antimicrobial mass medications work? A systematic review and meta-analysis of randomised clinical trials investigating antimicrobial prophylaxis or metaphylaxis against naturally occurring bovine respiratory disease.
      ). Both HPPV+ and HNPV− were estimated by the following formulas (
      • Christensen J.
      • Gardner I.A.
      Herd-level interpretation of test results for epidemiologic studies of animal diseases.
      ):
      HPPV=HTP×HSeHTP×HSe+(1HTP)×(1HSp);


      HPNV=(1HTP)×HSp(1HTP)×HSp+HTP×(1HSe).


      Batch-level predictive values depend on the true proportion of positive batches [HTP; i.e., the pre-test probability that a batch is positive (≥active BRD prevalence threshold)]. Because the true batch prevalence of active BRD is unknown in veal calves, HPPV and HPNV were estimated for a range of HTP: 5, 10, 15, and 20%. Batch-level predictive values also depend on the batch-level Se of VcCRS [HSe; i.e., the probability that a positive batch (i.e., with ≥active BRD prevalence threshold) yields a positive batch-level test result] and the batch-level Sp of VcCRS [HSp; i.e., the probability that a negative batch (i.e., with <active BRD prevalence threshold) yields a negative batch-level test result], estimated by the following formulas (
      • Christensen J.
      • Gardner I.A.
      Herd-level interpretation of test results for epidemiologic studies of animal diseases.
      ):
      HSe=0k1(nk1)APk1(1AP)n(k1);


      HSp=0k1(nk1)Spn(k1)(1Sp)k1,


      where AP corresponds to the apparent prevalence, defined as AP = prevalence threshold × Se + (1 − prevalence threshold) × (1 − Sp). The HSe and HSp of VcCRS are dependent on the Se and Sp of the individual test, the number of animals tested (n), the prevalence threshold in infected batches, and the batch cut-off value (k; e.g., 1, 2, or 3 positive test results) used to classify the batch as positive.
      We first determined the minimal number of veal calves (n) to sample (10, 20, or 30) in a batch (≥100 calves) that optimized HPPV and HPNV, considering the most probable value of HTP at 0.05 based on the batch prevalence of lung consolidation on dairy farms in Québec (
      • Buczinski S.
      • Borris M.E.
      • Dubuc J.
      Herd-level prevalence of the ultrasonographic lung lesions associated with bovine respiratory disease and related environmental risk factors.
      ). We hypothesized an active BRD prevalence threshold of 15%, which represents the threshold where the number of veal calves needing treatment is minimal according to
      • Baptiste K.E.
      • Kyvsgaard N.C.
      Do antimicrobial mass medications work? A systematic review and meta-analysis of randomised clinical trials investigating antimicrobial prophylaxis or metaphylaxis against naturally occurring bovine respiratory disease.
      .
      After the minimal number of veal calves was fixed (10, 20, or 30), we determined the batch cut-off value (k) that optimized HPPV and HNPV for the different scenarios of prevalence thresholds and HTP.
      Additional details on the materials and methods, including the statistical models used and the data sets, are available as Supplemental Files S2 and S3 (https://dataverse.harvard.edu/dataverse/clinical-scoring-system;
      • Berman J.
      Supplemental methods. Harvard Dataverse, V1.
      ,
      • Berman J.
      LCM regression. Harvard Dataverse, V1.
      ).

      RESULTS

      Step 1: Development of the VcCRS

      Study Population

      Descriptive data of the 800 veal calves recruited in this study are shown in Table 2. Most of the veal calves were male Holsteins. A total of 115 calves (14%) were treated with anti-inflammatories, antimicrobials, or both approaches before collecting data. Calves' treatments before sampling had no influence on the results (assessed by comparing proportion of TUS- and Hap-positive and proportion of bacteria isolation in treated and untreated calves; data not shown). A total of 46 calves (5%) had another disease (diarrhea, navel infection, arthritis, ringworm, mandibular mass, eye disorders) detected at physical examination. Deep nasopharyngeal results showed that most calves (n = 135; 54%) were carrying Mycoplasma bovis. Pasteurella multocida and Mannheimia haemolytica were isolated in 54 (21.4%) and 3 (1.2%) samples.
      Table 2Descriptive data of the random 800 veal calves included in the study for validation of a clinical respiratory disease scoring system for veal calves; data collection was performed 2 wk after arrival at the fattening unit, when calves were around 3 to 4 wk old
      Categorial variableNumber of calvesProportion of calves (%)
      Batch
       Milk-fed (white)61076.3
       Grain-fed (red)19023.8
      Season
       Fall32040
       Winter9011.3
       Spring24030
       Summer15018.8
      Breed
       Holstein76996
       Jersey50.63
       Red Holstein182.3
       Ayrshire81
      Sex
       Female445.5
       Male75694.5
      Treatment before sampling
      Antimicrobials, anti-inflammatories, or both.
       Yes11514.4
       No68385.6
      Comorbidity
       Diarrhea364.5
       Navel infection40.5
       Arthritis10.13
       Ringworm10.13
       Mandibular mass10.13
       Eye disorders20.25
       None75494.3
      Bacteria isolated from a deep nasopharyngeal sample (250 calves)
      Mycoplasma bovis13554
      Pasteurella multocida5421.4
      Mannheimiaspp.
      Other than Mannheimia haemolytica.
      2811.2
      Mannheimia varigena104
      Mycoplasmaspp.
      Other than Mycoplasma bovis.
      93.6
      Mannheimia haemolytica31.2
      Trueperella pyogenes10.4
      Bibersteinia trehalosi10.4
       >1 bacterium4116.4
       >2 bacteria20.8
       Negative5722.8
       Contaminated31.2
       Missing20.8
      1 Antimicrobials, anti-inflammatories, or both.
      2 Other than Mannheimia haemolytica.
      3 Other than Mycoplasma bovis.

      Descriptive Statistics

      Data collection was possible on each of the 800 recruited calves.

      Clinical Signs (Predictors)

      The proportions of clinical signs in the 800 veal calves were as follows: nasal discharge (n = 77; 9.6%); cough (n = 147; 18.4%); ocular discharge (n = 142; 17.8%); ear droop or head tilt (n = 35; 4.4%); abnormal respiration (n = 32; 4.0%). A total of 39 calves had increased rectal temperature (n = 39; 4.9%). The frequencies of calves with 1 of the 3 predictors included in the VcCRS development (i.e., ear droop or head tilt, cough, and increased rectal temperature), TUS positive, and Hap positive are detailed in Figure 2.
      Figure thumbnail gr2
      Figure 2Upset plot showing the total set size and overlaps between the presence of the 3 predictors included in the developed score (ear droop or head tilt, cough, and increased rectal temperature), thoracic ultrasonography (TUS) results, and haptoglobin results in 795 veal calves. The number of positive tests is indicated on the y-axis. The shaded circles connected by solid lines in the lower panel show the intersecting positive test data sets.

      Reference Standard Tests

      Thoracic ultrasonography was performed successfully on each calf. A total of 110 calves (13.8%) had lung consolidations ≥3 cm and were deemed positive on TUS. Five samples were not analyzed to determine Hap concentration (insufficient quantity or missing identification). Consequently, 5 calves had missing Hap data. Among these 5 calves, 2 were TUS positive. A total of 133 calves (16.7%) had Hap ≥2.5 μmol/L (0.25 g/L) and were deemed positive on Hap. A total of 28 calves were TUS and Hap positive, 105 calves were TUS negative but Hap positive, 80 calves were TUS positive but Hap negative, and 582 calves were TUS and Hap negative.

      Final VcCRS

      The posterior distribution Se and Sp of TUS were estimated at 76% (95% BCI: 42, 96%) and 90% (95% BCI: 87, 95%), respectively. However, the convergence of LCM regression was possible when the lower limit of Se of TUS was truncated to 60%, compatible with what we know about Se of TUS in the literature (
      • Rabeling B.
      • Rehage J.
      • Dopfer D.
      • Scholz H.
      Ultrasonographic findings in calves with respiratory disease.
      ;
      • Ollivett T.L.
      • Caswell J.L.
      • Nydam D.V.
      • Duffield T.
      • Leslie K.E.
      • Hewson J.
      • Kelton D.
      Thoracic ultrasonography and bronchoalveolar lavage fluid analysis in Holstein calves with subclinical lung lesions.
      ;
      • Berman J.
      • Francoz D.
      • Dufour S.
      • Buczinski S.
      Bayesian estimation of sensitivity and specificity of systematic thoracic ultrasound exam for diagnosis of bovine respiratory disease in pre-weaned calves.
      ). The posterior distributions of the weight coefficients for cough, ear droop or head tilt, and increased rectal temperature are detailed in Table 3. The posterior distribution of Se and Sp of Hap were estimated at 62% (95% BCI: 34, 87%) and 87% (95% BCI: 85, 91%), respectively. The weight coefficients' posterior distributions were similar to those of the main model (overlaps of 95% BCI; Table 3, sensitivity analysis scenario).
      Table 3Posterior median distributions and 95% Bayesian credible interval regression coefficients and their weights for increased temperature, ear droop or head tilt, and cough using the sensitivity (Se) and specificity (Sp) of thoracic ultrasonography (TUS; main model) or the Se and Sp of haptoglobin concentration (Hap; sensitivity analysis scenario)
      β = beta distribution; N = normal distribution.
      ItemMain model
      PriorsPosterior distributions
      Se of TUSβ (8.0; 2.2)0.76 (0.42; 0.96)|(0.60; 0.96)
      The distribution of Se was truncated at 0.60 to promote convergence of Bayesian latent class model.
      β (5.7, 2.5)
      Sp of TUSβ (99.7; 6.2)0.90 (0.87; 0.95)β (316.6, 36.1)
      Regression parameterTemperatureEar droop or head tiltCoughWeight
       βTemperatureN (1.4, 1.7)0.59 (−0.55; 1.7)6
       βEar droop or head tiltN (1.4, 1.7)0.90 (−0.25; 1.98)9
       βCoughN (1.4, 1.7)0.96 (0.14; 1.73)10
       InterceptN (1.4, 1.7)−2.2 (−2.9; −1.7)−2.2 (−2.9; −1.7)−2.3 (−3.0; −1.8)
      Sensitivity analysis scenario
      PriorsPosterior distributions
      Se of Hapβ (9.6; 3.9)0.62 (0.34; 0.87)|(0.60; 0.87)β (5.9, 4.0)
      Sp of Hapβ (36.7; 2.9)0.87 (0.85; 0.91)β (764.5, 115.1)
      Regression parameterTemperatureEar droop or head tiltCoughWeight
       βTemperatureN (1.4, 1.7)0.43 (−0.58; 1.4)4
       βEar droop or head tiltN (1.4, 1.7)0.78 (−0.23; 1.77)8
       βCoughN (1.4, 1.7)0.79 (0.17;1.44)8
       InterceptN (1.4, 1.7)−1.7 (−2.4; −1.2)−1.7 (−2.35; −1.24)−1.8 (−2.5; −1.35)
      1 β = beta distribution; N = normal distribution.
      2 The distribution of Se was truncated at 0.60 to promote convergence of Bayesian latent class model.
      Finally, we obtained the following VcCRS:
      Specific score = 10 × Cough + 9 × Ear + 6 × Fever,


      where Cough takes a value of 1 if induced or spontaneous cough is present and 0 otherwise, Ear takes a value of 1 if a slight uni- or bilateral droop or head tilt is present and 0 otherwise, and, finally, Fever takes a value of 1 if rectal temperature ≥39.7°C and 0 otherwise.

      Optimal Cut-Off

      The MCT values for all the possible cut-off values of the VcCRS and for r = 1:1, r = 3:1, and r = 8:1 are shown in Figure 3. Whatever the value of r, the MCT values were minimized for a cut-off of 15 in our study population, with a prevalence of 5%. The cut-off of 15 was also optimal for r = 1:1 and r = 3:1 for a prevalence of 10%. For a prevalence of 25%, even if the cut-off of 15 remained acceptable for r = 1:1 but should be considered lower at 6 for r = 3:1 and r = 8:1 (Figure 3). We noticed from a practical perspective that, when using a cut-off of 15, only 2 predictors are sufficient to optimize the detection of active BRD. Thus, a calf with 2 findings among the 3 studied predictors (increased rectal temperature, ear droop or head tilt, and cough) could be considered as VcCRS positive, minimizing MCT in most scenarios.
      Figure thumbnail gr3
      Figure 3Misclassification cost-term (MCT) of the cut-offs used to detect active bovine respiratory disease in a population of 800 veal calves. A Bayesian latent class model was used to estimate the sensitivity of the clinical respiratory scoring system (SeVcCRS), its specificity (SpVcCRS), and its prevalence (pi). MCT was calculated with the following formula: MCT = (1 − pi) × (1 − SpVcCRS) + r × pi × (1 − SeVcCRS). We assumed the following plausible ranges for the relative costs of the false-negative to false-positive ratio (r): r = 1:1 (continuous line, filled circle), r = 3:1 (dashed line, empty circle), and r = 8:1 (dashed line, black diamond). The minimum value of MCT can be considered as the value that minimizes the costs.

      Step 2: External Validation of the VcCRS

      The accuracy parameters (Se, Sp, PPV, NPV, LR+, and LR−) of VcCRS in the developing population and in both external populations are shown in Table 4. The performances remained similar in both population 1 and population 2. When we merged both external populations data sets (population 1 + population 2), the Se and Sp of VcCRS were 31% (95% BCI: 14, 70%) and 100% (95% BCI: 99, 100%). These latter values were further defined as the performances of VcCRS at the individual level.
      Table 4Posterior median and 95% credible intervals based on 4 Bayesian latent class models for the prevalence of active bovine respiratory disease, sensitivity (Se) and specificity (Sp), positive (PPV) and negative (NPV) predictive values, positive (LR+) and negative (LR−) likelihood ratios of the clinical respiratory score chart for veal calves (VcCRSC), and Se and Sp of thoracic ultrasonography (TUS) and concentration of haptoglobin (Hap) used to diagnose active bovine respiratory disease
      ItemDevelopment populationPopulation 1
      Population from Berman et al., 2019.
      Population 2
      Population from Morin et al., 2020.
      Populations 1 and 2
      For this analysis we used a latent class model with 2 independent populations (Pop 1 and Pop 2) but assumed similar sensitivity and specificity across populations.
      Se_VcCRSC0.30 (0.12; 0.72)0.51 (0.22; 0.87)0.33 (0.07; 0.93)0.31 (0.14; 0.70)
      Sp_VcCRSC0.99 (0.98; 1.0)1.0 (0.97; 1.0)0.99 (0.98; 1.0)1.0 (0.99; 1.0)
      PPV_VcCRSC0.53 (0.25; 0.85)0.86 (0.39; 0.99)0.58 (0.16; 0.96)Pop 1: 0.89 (0.64; 0.99)
      Pop 2: 0.65 (0.26; 0.97)
      NPV_VcCRSC0.97 (0.92; 0.99)0.97 (0.93; 1.0)0.98 (0.94; 1.0)Pop 1: 0.94 (0.87; 0.99)
      Pop 2: 0.98 (0.95; 1.0)
      LR+_VcCRSC27.1 (7.8, 110)104.8 (14.2; 3,026)59.1 (7.6; 813)74.3 (18.3; 853)
      LR−_VcCRSC0.70 (0.28; 0.89)0.49 (0.14; 0.79)0.68 (0.07; 0.94)0.69 (0.30; 0.87)
      Se_TUS0.80 (0.54; 0.96)0.88 (0.68; 0.98)0.79 (0.50; 0.96)0.86 (0.64; 097)
      Sp_TUS0.90 (0.87; 0.93)0.92 (0.89; 0.96)0.95 (0.93; 0.98)0.95 (0.93; 0.98)
      Se_Hap0.64 (0.40; 0.85)0.72 (0.55; 0.86)
      Sp_Hap0.87 (0.84; 0.90)0.95 (0.92; 0.98)
      Prevalence0.04 (0.02; 0.10)0.08 (0.04; 0.15)0.03 (0.006; 0.07)Pop 1: 0.09 (0.04; 0.16)
      Pop 2: 0.03 (0.007; 0.06)
      1 Population from
      • Berman J.
      • Francoz D.
      • Dufour S.
      • Buczinski S.
      Bayesian estimation of sensitivity and specificity of systematic thoracic ultrasound exam for diagnosis of bovine respiratory disease in pre-weaned calves.
      .
      2 Population from

      Morin, M., J. Dubuc, D. Francoz, and S. Buczinski. 2020. Randomized clinical trial of intranasal vaccination with Once PMH IN. BOVILIS Coronavirus Vaccine placebo on prevalence of lung consolidation diarrhea and health parameters in veal calves. Research report.

      .
      3 For this analysis we used a latent class model with 2 independent populations (Pop 1 and Pop 2) but assumed similar sensitivity and specificity across populations.

      Step 3: Application at Batch Level

      The determination of the minimal number of calves to sample (n) is shown in Figure 4. For an HTP of 0.05 and an active BRD prevalence threshold of 15%, we found no difference in HPPV and HNPV between sampling 30, 20, or 10 calves for a k of 3. Because it is practical to sample as few calves as possible in a fattening unit, the optimal number n was therefore considered at 10 random calves to screen in the batch to be assessed.
      Figure thumbnail gr4
      Figure 4Batch-level positive (solid line) and negative (dashed line) predictive values for a bovine respiratory disease prevalence threshold of 15%, considering the true proportion of positive batches of 5%, for different batch cut-off values (k) and numbers of sampled calves (n). The error bars represent the dispersion around the median value (i.e., the 95% Bayesian credible intervals).
      The different values of herd predictive values for the different ranges of HTP, prevalence threshold, and k are presented in Figure 5. We noticed that whatever the prevalence threshold and HTP, the optimal k was 3—except for a prevalence threshold of 5%, where the optimal k was 4, meaning that 3 or more positive calves out of 10 assessed was a practical way to determine batch-level positivity (Graphical Abstract).
      Figure thumbnail gr5
      Figure 5Batch-level positive (HPPV) and negative (HNPV) predictive values (PV) for a minimal number of positive calves to consider batch as positive (k), true proportion of positive batches (HTP), and active bovine respiratory disease (BRD) prevalence when the clinical respiratory chart score is applied on 10 veal calves in a batch. The dashed line represents a PV of 0.9. The HPPV and HNPV with 95% Bayesian credible intervals are presented.

      DISCUSSION

      The objective of this study was to develop and validate a new CRSC to detect active BRD in veal calves housed individually, and to describe its accuracy for identifying individuals and groups of veal calves to treat. We found that calves with 2 of the following findings—cough, unilateral or bilateral ear droop or head tilt, or rectal temperature ≥39.7°C—were considered as positive, with an individual Se and Sp of 31% (95% BCI: 14, 70%) and 100% (95% BCI: 99; 100%), respectively, for active BRD detection. At the batch level, we found that ≥3 positive calves among 10 calves sampled 2 wk after arrival at the fattening unit allowed us to detect batches with active BRD prevalence ≥10% with positive and negative batch-level predictive values ≥94% and ≥95%, respectively.
      Interestingly, our score differs from the previously developed CRSC in pre-weaning dairy-breed calves by the choice of clinical signs included (
      • McGuirk S.M.
      Disease management of dairy calves and heifers.
      ;
      • Love W.J.
      • Lehenbauer T.W.
      • Kass P.H.
      • Van Eenennaam A.L.
      • Aly S.S.
      Development of a novel clinical scoring system for on-farm diagnosis of bovine respiratory disease in pre-weaned dairy calves.
      ;
      • Buczinski S.
      • Fecteau G.
      • Dubuc J.
      • Francoz D.
      Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework.
      ). Previously, all reported respiratory clinical signs (i.e., nasal discharge, ocular discharge, cough, ear droop or head tilt, abnormal respiration) and increased rectal temperature were grouped for the development of a CRSC (
      • McGuirk S.M.
      Disease management of dairy calves and heifers.
      ;
      • Love W.J.
      • Lehenbauer T.W.
      • Kass P.H.
      • Van Eenennaam A.L.
      • Aly S.S.
      Development of a novel clinical scoring system for on-farm diagnosis of bovine respiratory disease in pre-weaned dairy calves.
      ;
      • Buczinski S.
      • Fecteau G.
      • Dubuc J.
      • Francoz D.
      Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework.
      ). In contrast, we first selected the clinical signs to include in the CRSC according to their reliability when used by different persons involved in veal calves' health monitoring (producers, technicians, and veterinarians;
      • Berman J.
      • Francoz D.
      • Abdallah A.
      • Dufour S.
      • Buczinski S.
      Evaluation of inter-rater agreement of the clinical signs used to diagnose bovine respiratory disease in individually housed veal calves.
      ). This a priori selection has the advantages of removing most operator variability from the CRSC used and being applied in a population-based approach, where the first-line diagnosis is generally not performed by a veterinarian.
      Additionally, this selection of robust clinical signs limited the number of predictors to assess [only 3 predictors vs. 5 or 6 in WiCRSC and (Vc)CaCRSC, respectively], which could have affected the Se of VcCRS (Se of 31%). Moreover, this selection implied that the 3 predictors included in VcCRS are less specific of lower respiratory tract disorders than other clinical signs such as dyspnea. However, comparison of Se and Sp of VcCRS and VcCRS + dyspnea did not show any difference on Se and Sp (data shown in Supplemental File S2). Therefore, this selection simplifies the use of the VcCRS in the context of veal calves in a large fattening unit. Even simpler, we showed that the presence of only 2 findings among cough, ear droop or head tilt, and increased rectal temperature is necessary to consider a calf positive. After this initial assessment, induced cough or increased rectal temperature are tested a second time, which limits the handling of calves and the potential biosecurity issues (e.g., transmission of disease). Interestingly, we showed that randomly sampling 10 calves from a batch of calves (e.g., batches varying from 100 to 800 calves) was sufficient to apply our score at the batch level, limiting time consumption. The VcCRS is therefore an easy, simple, and quick score to use on the individual and group levels in the veal calf industry.
      Estimating VcCRS performances at both the individual and the group level contrasts with previously reported CRSCs. Using VcCRS at the group level had the advantage of providing information on whether a group of calves needs to be treated with antimicrobials or not, and would help to rationalize the metaphylactic treatment approach (
      • Baptiste K.E.
      • Kyvsgaard N.C.
      Do antimicrobial mass medications work? A systematic review and meta-analysis of randomised clinical trials investigating antimicrobial prophylaxis or metaphylaxis against naturally occurring bovine respiratory disease.
      ). We showed that 3 calves with positive individual VcCRS scores among 10 randomly sampled in a group allowed us to detect batches with active BRD prevalence ≥10%, with batch-level predictive values >0.90. Concretely, if we consider the prevalence of risk as defined at 21% in
      • Leruste H.
      • Brscic M.
      • Heutinck L.F.
      • Visser E.K.
      • Wolthuis-Fillerup M.
      • Bokkers E.A.
      • Stockhofe-Zurwieden N.
      • Cozzi G.
      • Gottardo F.
      • Lensink B.J.
      • van Reenen C.G.
      The relationship between clinical signs of respiratory system disorders and lung lesions at slaughter in veal calves.
      , being VcCRS-positive at the batch level (i.e., 3 individual VcCRS-positive calves among 10 sampled) would imply that the batch has a 99% chance of having active BRD prevalence ≥21%; otherwise, the batch has a 95% chance of not having active BRD prevalence ≥21%. However, it is difficult to define a universally accepted active BRD prevalence threshold in veal calves, as such as threshold may depend on various factors, including the calves (price paid in the auction market, quality, origin), the anticipated meat marketing context, specific farm risk factors (ventilation, caretakers), the season, or the agent involved. That is why, in this study, we reported the VcCRS performance at the batch level for a range of active BRD prevalence scenarios (Figure 5). With this approach, one could refer to this figure to adjust the intervention in a specific group under specific circumstances. At a predefined active BRD prevalence threshold, our score could be used in the future to guide accurate metaphylactic group treatments and, ultimately, reduce antimicrobial use (
      • Baptiste K.E.
      • Kyvsgaard N.C.
      Do antimicrobial mass medications work? A systematic review and meta-analysis of randomised clinical trials investigating antimicrobial prophylaxis or metaphylaxis against naturally occurring bovine respiratory disease.
      ).
      At the individual level, the Se of VcCRS was 31% and, therefore, lower than the Se of previous CRSC (range: 46–83%;
      • Buczinski S.
      • Ollivett T.L.
      • Dendukuri N.
      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.
      ,
      • Buczinski S.
      • Fecteau G.
      • Dubuc J.
      • Francoz D.
      Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework.
      ;
      • Love W.J.
      • Lehenbauer T.W.
      • Van Eenennaam A.L.
      • Drake C.M.
      • Kass P.H.
      • Farver T.B.
      • Aly S.S.
      Sensitivity and specificity of on-farm scoring systems and nasal culture to detect bovine respiratory disease complex in preweaned dairy calves.
      ). However, the individual Sp of VcCRS was almost perfect and higher than in previous studies (range: 74–91%;
      • Buczinski S.
      • Ollivett T.L.
      • Dendukuri N.
      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.
      ,
      • Buczinski S.
      • Fecteau G.
      • Dubuc J.
      • Francoz D.
      Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework.
      ;
      • Love W.J.
      • Lehenbauer T.W.
      • Van Eenennaam A.L.
      • Drake C.M.
      • Kass P.H.
      • Farver T.B.
      • Aly S.S.
      Sensitivity and specificity of on-farm scoring systems and nasal culture to detect bovine respiratory disease complex in preweaned dairy calves.
      ). The almost perfect Sp of VcCRS had the advantage of limiting false-positive calves and, therefore, useless individual antimicrobial treatment and economic loss (
      • Theurer M.E.
      • White B.J.
      • Larson R.L.
      • Schroeder T.C.
      A stochastic model to determine the economic value of changing diagnostic test characteristics for identification of cattle for treatment of bovine respiratory disease.
      ). The selection of reliable clinical signs and the better definition of respiratory disease as active BRD could explain the Sp superiority of VcCRS versus previous clinical scores in individual pre-weaning dairy-breed calves (
      • Buczinski S.
      • Ollivett T.L.
      • Dendukuri N.
      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.
      ,
      • Buczinski S.
      • Fecteau G.
      • Dubuc J.
      • Francoz D.
      Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework.
      ;
      • Love W.J.
      • Lehenbauer T.W.
      • Van Eenennaam A.L.
      • Drake C.M.
      • Kass P.H.
      • Farver T.B.
      • Aly S.S.
      Sensitivity and specificity of on-farm scoring systems and nasal culture to detect bovine respiratory disease complex in preweaned dairy calves.
      ). Although the individual performances seem low, with Se of 31%, the goal of this study was to implement a diagnostic tool to target treatment not at the individual level but at the group level (metaphylactic). The high Sp of VcCRS permits us to have a high HPPV at the group level while avoiding false-positive batches and, therefore, unnecessary treatment of the entire batch.
      Our study differs from others that have developed CRSC in that its design follows a robust statistical approach, as recommended by medical guidelines (
      • Moons K.G.M.
      • Altman D.G.
      • Reitsma J.B.
      • Ioannidis J.P.
      • Macaskill P.
      • Steyerberg E.W.
      • Vickers A.J.
      • Ransohoff D.F.
      • Collins G.S.
      Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and elaboration.
      ). First, we externally validated our score, in contrast with previous CRSC in calves (
      • McGuirk S.M.
      Disease management of dairy calves and heifers.
      ;
      • Love W.J.
      • Lehenbauer T.W.
      • Kass P.H.
      • Van Eenennaam A.L.
      • Aly S.S.
      Development of a novel clinical scoring system for on-farm diagnosis of bovine respiratory disease in pre-weaned dairy calves.
      ). This validation increases the robustness, credibility, applicability, and generalization of VcCRS across veal calf settings. Second, WiCRSC and CaCRSC used the combination of multiple tests (a composite reference test) to elaborate their scores and, therefore, used an inaccurate definition of respiratory disease to define cases (
      • McGuirk S.M.
      Disease management of dairy calves and heifers.
      ;
      • Love W.J.
      • Lehenbauer T.W.
      • Kass P.H.
      • Van Eenennaam A.L.
      • Aly S.S.
      Development of a novel clinical scoring system for on-farm diagnosis of bovine respiratory disease in pre-weaned dairy calves.
      ). In this study, we used Bayesian LCM, as recommended by the World Organization for Animal Health, in the absence of a gold standard to limit classification bias and ensure better accuracy estimation (
      • Cheung A.
      • Dufour S.
      • Jones G.
      • Kostoulas P.
      • Stevenson M.
      • Singanallur N.
      • Firestone S.
      Bayesian latent class analysis when the reference test is imperfect.
      ). Spectrum bias was also limited by selecting prospectively random calves from commercial veal facilities that represented wide spectrums of active BRD severity (ranging from healthy calves to calves with mild and moderate active BRD to calves with severe active BRD;
      • Buczinski S.
      • O'Connor A.M.
      Specific challenges in conducting and reporting studies on the diagnostic accuracy of ultrasonography in bovine medicine.
      ). In contrast, the case-control designs used in
      • McGuirk S.M.
      Disease management of dairy calves and heifers.
      and
      • Love W.J.
      • Lehenbauer T.W.
      • Kass P.H.
      • Van Eenennaam A.L.
      • Aly S.S.
      Development of a novel clinical scoring system for on-farm diagnosis of bovine respiratory disease in pre-weaned dairy calves.
      may have included cases at the ends of the spectrum of disease (either healthy calves or calves with severe active BRD), resulting in an overestimation of their scores' coefficients.
      This study has some limitations, however. First, the posterior median active BRD prevalence was low (5% vs. 20% expected for sample size calculation). This low prevalence is likely because we sampled calves before the main peak of active BRD incidence to promote early detection and limit the proportion of treated calves. Consequently, sensitivity 95% BCI estimates were wide. This uncertainty prevented us from adequately estimating the regression coefficients and forced us to truncate the lower limit of Se of the 95% BCI of TUS to promote convergence of our statistical model. However, this low active BRD prevalence implied large variation in the individual CRSC Se (median value of 31%, but 95% BCI from 14 to 70%) but not in accurately estimated Sp (median of 100%, with 95% BCI from 99 to 100%), which is reported to have the most influence on active BRD diagnosis (
      • Theurer M.E.
      • White B.J.
      • Larson R.L.
      • Schroeder T.C.
      A stochastic model to determine the economic value of changing diagnostic test characteristics for identification of cattle for treatment of bovine respiratory disease.
      ). Second, we chose by convenience a temporal external validation; that is, we used participant data collected by the same investigators, using the same predictors and target condition definitions and measurements, but sampled from an earlier period (
      • Moons K.G.M.
      • Altman D.G.
      • Reitsma J.B.
      • Ioannidis J.P.
      • Macaskill P.
      • Steyerberg E.W.
      • Vickers A.J.
      • Ransohoff D.F.
      • Collins G.S.
      Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and elaboration.
      ). Transportability from one setting to another could have been increased by using a database collected by other investigators in another country (broad validation), in other types of patients (dairy calves), or considering the probable influence of infectious agents (high prevalence of M. bovis in this study). However, our study represents the first step in the development of a VcCRS, and other external validations could be performed in the future to tailor its use in other specific contexts.

      CONCLUSIONS

      We developed a simple batch-level score that is reliable across examiners and performs effectively in the detection of active BRD in veal calves. According to the requirements of the industry, the active BRD prevalence threshold could be defined. The positive VcCRS group could then be treated metaphylactically or investigated. The implementation of this VcCRS chart in the veal calf industry would promote the elaboration of a protocol to tailor antimicrobial use. A prospective cohort study comparing antimicrobial use and production outcomes of groups applying the VcCRS chart (exposed) and groups without (non-exposed) would be the next step to validate this CRSC and justify its large-scale implementation in veal calves.

      ACKNOWLEDGMENTS

      Funding was received from the Producteurs Bovins du Québec (Longeuil, Québec, Canada), Programme Innov'Action Agro-alimentaire (Project IA117743: Amélioration des veaux lourds souffrant de pneumonies) du Ministère de l'Agriculture, des Pêcheries et de l'Alimentation du Québec (MAPAQ, Saint-Hyacinthe, Québec, Canada), Délimax Veaux Lourds Ltée (Saint-Hyacinthe, Québec, Canada), Aliments Prolacto Inc. (Villeroy, Québec, Canada), and FQRNT (Fonds Québécois de Recherche en Nature et Technologie; Montréal, Québec, Canada). The authors thank Luke Palder from Proofreading Services (Raleigh, NC) for the English language proofreading. The authors have not stated any conflicts of interest.

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