Evaluation of infrared thermography compared to rectal temperature to identify illness in early postpartum dairy cows

Research in Veterinary Science 125 (2019) 315–322

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Evaluation of infrared thermography compared to rectal temperature to identify illness in early postpartum dairy cows K. Macmillan, M.G. Colazo, N.J. Cook

T

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Livestock Systems Section, Alberta Agriculture and Forestry, Edmonton, Alberta T6H 5T6, Canada

A R T I C LE I N FO

A B S T R A C T

Keywords: Body temperature Thermal imaging Transition disease Eye temperature

This study evaluated and compared infrared thermography (IRT) and rectal temperature (RT) as screening tests to identify sick transition dairy cows. Holstein cows (n = 72; 42 primiparous) had RT and IRT temperatures taken daily from 1 to 12 days in milk (DIM). Health examinations were performed daily to diagnose retained fetal membrane, milk fever and metritis, and blood was analyzed for β-hydroxybutyrate at 6 and 9 DIM to diagnose ketosis. Plasma concentrations of cortisol, interleukin-6, tumor necrosis factor α and serum amyloid A at 3, 6, 9 and 12 DIM were included as additional indicators of illness. Cows were categorized as true sick if clinically diagnosed with an illness, or if at least 2 blood parameters were above the normal range. Diagnostic test performances for RT and IRT variables were determined for each variable at a test referent value that provided the highest Youden's (J) index. The best performing screening test depended on the definition of true sickness. In general, the J index for RT was 0.15–0.17 whereas the highest J index for the IRT variables was 0.22 for the mean eye temperature and 0.19 for the mean cheek temperature. Infrared thermography was at least comparable to RT and some IRT variables performed better as a screening tests than RT. Future studies into the automation of IRT for surveillance of early postpartum diseases is warranted.

1. Introduction The transition period, 3 wk. before to 3 wk. after calving, is a stressful time for dairy cows due to re-grouping, changes in diet, calving and onset of lactation (von Keyserlingk et al., 2011). These challenges often lead to impaired metabolic and immune function, resulting in 30 to 50% of cows experiencing a metabolic or inflammatory disease soon after calving (LeBlanc, 2010), which could impair production and reproduction, and reduce productive lifespan (Liang et al., 2017). Recently, Liang et al. (2017) reported that a single case of a transition disease could cost between US$129 and US$533 depending on the type of disease. With nearly half of the fresh cows being likely to experience a transition disease, there is a negative impact on animal welfare and large economic losses for the producer. In an effort to maintain healthy cows and reduce the use of antibiotics to mitigate antimicrobial resistance, frequent and precise monitoring of transition cows is necessary for early intervention (Trevisi et al., 2014). The measuring of rectal temperature (RT) as a health screening procedure in the days after calving is the most common method of monitoring early postpartum cows, as obtaining RT is cheap, easy to implement and can aid in identifying sick cows (Smith and Risco, 2005). However, RT requires additional handling of cows and has been shown to have a low ⁎

sensitivity, with only 59% of cows with uterine infection showing fever (RT ≥ 39.5 °C; Benzaquen et al., 2007), and low specificity, with 66% of healthy cows having at least one temperature ≥ 39.5 °C (Wagner et al., 2008). A low sensitivity means that truly sick animals may be missed and not treated, resulting in reduced animal health and welfare. A low specificity means that truly healthy animals may be treated by mistake, resulting in overuse of antibiotics. All diagnostic tests exhibit an inverse relationship between test sensitivity and specificity. Thus, the trade-off for the producer is to choose a test, and a referent value for that test, that maximises the diagnostic efficacy of the test in relation to the costs of treatment, or the cost of undiagnosed sickness. Another method for identifying sick cows based on temperature is infrared thermography (IRT), which measures heat radiated from the surface of the body. The temperature information is displayed as an image, or thermogram, in which each pixel represents a measured surface temperature (Alsaaod et al., 2015). From a veterinary perspective, IRT has been used to identify areas of local inflammation such as mastitis (Metzner et al., 2014), lameness (Oikonomou et al., 2014) and foot and mouth disease (Rainwater-Lovett et al., 2009). Additionally, IRT has been used to identify a febrile response (fever) in beef calves to diagnose diseases such as bovine viral diarrhea and bovine respiratory disease; (Schaefer et al., 2004, 2007; Schaefer et al.,

Corresponding author at: Lacombe Research Centre, 6000 C E Trail, Lacombe, AB T4L 1W1, Canada. E-mail address: [email protected] (N.J. Cook).

https://doi.org/10.1016/j.rvsc.2019.07.017 Received 26 March 2019; Received in revised form 12 July 2019; Accepted 17 July 2019 0034-5288/ © 2019 Elsevier Ltd. All rights reserved.

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2012). However, to our knowledge, no studies have been published using IRT to replace RT as a screening test for disease in transition dairy cows. Previous studies have indicated that IRT is capable of identifying a febrile response in beef cattle 4 to 6 days before clinical signs of illness (Schaefer et al., 2007), and sometimes a decrease in radiated temperatures are observed prior to the onset of a febrile response (Schaefer and Cook, 2013). Measurement of eye temperature using IRT is more consistent than other anatomical regions (Poikalainen et al., 2012) and shows acceptable agreement with RT measurements in dairy cows (Hoffmann et al., 2013). Measurement of cheek temperature using IRT is correlated with metabolic indicators of performance in beef steers, including dry-matter intake, average daily gain, feed to gain ratio and residual feed intake (Montanholi et al., 2010). This indicates that the mean cheek temperature is also a useful measurement of heat production and dissipation, and is associated with the health status of the animal. The potential advantages of using IRT to measure body surface temperature is that the animals do not need to be restrained or handled in order to take measurements, and the measurement process could be automated. The objective of this proof-of-concept study was to evaluate IRT as a screening tool to identify sick early postpartum cows. We hypothesized that IRT will have a diagnostic efficacy at least equivalent to RT.

free-stall were sorted after milking into a group angle squeeze for sampling. Rectal temperature was recorded using a digital thermometer (Flash Check 11,062; DeltaTRAK Inc., Pleasanton, CA, USA). The thermometer was inserted up to the display screen and held parallel to the ground. Infrared images of the right eye and cheek were taken using a FLIR E40x camera (ITM Instruments Inc., Calgary, AB, Canada). Three successive images were recorded perpendicular to the eye and cheek locations from a distance of 0.5 to 1 m. It was necessary to input ambient temperature into the IRT camera settings to achieve the best accuracy of measurement. In addition, endothermic animals, including dairy cows, thermoregulate to environmental conditions and consequently animal and environmental temperatures are often highly correlated (Franze et al., 2012). To that end, during each sampling session an environmental meter (Kestrel 5000, Kestrel Instruments, Boothwyn, PA, USA) was placed near the cows to measure atmospheric temperature, relative humidity, heat index and wind speed. All IRT images were taken indoors, out of direct sunlight and sheltered from wind. Emissivity was set in the IRT camera at 0.98, and the ambient air temperature and relative humidity were inputted to the camera settings at the beginning of each recording session. The environmental data were also used to investigate the relationship between the observed IRT temperatures and environmental conditions.

2. Materials and methods

2.3. Health diagnosis

This observational study was carried out from November 2016 to August 2017 in a commercial dairy herd with approximately 500 milking Holstein cows located in Alberta, Canada. All procedures were conducted in accordance with the guidelines of the Canadian Council on Animal Care (Canadian Council on Animal Care, 2009).

The diagnostic performance of a test depends crucially on the definition of true sick animals. Visual health examinations were performed daily by a veterinarian to diagnose health disorders. However, it was recognized that animals may be sick but asymptomatic and we therefore considered the 1 to 2 days following the clinical diagnosis as being ‘sick days’, depending on the initial diagnosis. Thus, retained fetal membranes (RFM) was diagnosed if the fetal membranes were not discharged within 24 h after calving, plus the following day was considered to be a ‘sick’ day. Metritis was defined as fetid, watery, red or pink uterine discharge within 14 days after calving. The two days following the diagnosis of metritis were also considered ‘sick’ days. Milk fever was diagnosed based on clinical signs (i.e. recumbency) and cows were treated with an intravascular infusion of 500 mL of calcium borogluconate 23% w/v (Cal Mag Phos; Citadel Animal Health, Cambridge, ON, Canada). No additional days following a diagnosis of milk fever and treatment were considered sick days. Blood samples taken on 6 and 9 DIM were analyzed cow-side for β-hydroxybutyrate (BHB) concentrations using a hand held device (FreeStyle Precision Neo ™; Abbot Diabetes Care Inc., Mississauga, ON, Canada). Cows were diagnosed with ketosis if blood BHB concentrations were ≥ 1.1 mmol/mL, as determined by Macmillan et al. (2017), and were orally treated with 300 mL of propylene glycol (Partnar Animal Health Inc., Ilderton, ON, Canada) for three consecutive days. Cows diagnosed with ketosis were also considered to be sick the following day. Cows with a RT ≥ 40 °C, and those clinically diagnosed with RFM and metritis, were treated with an intramuscular injection of ceftiofur sodium (Excenel; Zoetis Can Inc., Kirkland, QC, Canada) at 1.0 mg/kg/d for three consecutive days, as per the farms' SOP. Incidences of illness based on the above criteria was designated as Sick 1. However, it was recognized that animals may be sick but asymptomatic. In an attempt to identify these animals blood samples were obtained from all cows on 3, 6, 9, and 12 DIM and analyzed for known biomarkers of inflammation and infection. Plasma samples were analyzed for interleukin-6 (IL-6), tumor necrosis factor α (TNFα) and serum amyloid A (SAA) using commercially available ELISA kits (Cloud Clone Corp., Wuhan, China). Plasma cortisol concentrations were determined using an in-house ELISA method. The blood parameters measured were considered indicators of illness as they are associated with transition diseases (Zhang et al., 2018; Zhang et al., 2016; Dervishi et al., 2016; Pathak et al., 2015) and an inflammatory response (Zebeli et al., 2013; Trevisi et al., 2012). Cows were categorized as sick if two

2.1. Animals and management Dry cows and pregnant heifers were moved into the close-up pen approximately 21 days before expected calving, and subsequently to the maternity pen 1 to 2 days before calving. Cows were housed on deepbedded straw and fed a total-mixed ration once daily at 0800 h and feed was pushed up 12 times per day automatically. After calving, cows remained in the maternity pen for at least 3 days and were milked in a separate parlour before being moved into the milking free-stall barn at the producers' discretion. Fresh cows were housed in a free-stall barn bedded with sand. Cows were fed a total-mixed ration once daily at 0800 h and feed was pushed up 12 times daily automatically. All cows were milked 3 times daily at approximately 0600, 1400 and 2000 h and had free access to fresh water. 2.2. Data collection This study was conducted in four separate calving groups (November/January/April/July) containing 16 to 20 fresh cows, for a total of 72 animals (42 primiparous). Cows that calved during each study group were enrolled onto the study on the first day after calving, which was considered 1 day(s) in milk (DIM). Temperature measurements were made every day for 12 consecutive days. Blood samples were collected from all cows on 3, 6, 9, and 12 DIM by puncture of the coccygeal vessels using vacuum tubes containing heparin (Vacutainer; Becton Dickinson and Co., Franklin lakes, NJ). Blood samples were immediately placed on ice and within 4 h all samples were centrifuged at 3000 ×g for 20 min. Plasma samples were stored at −20 °C. Milk yield was recorded daily for all cows and these data were retrieved from the on-farm computer software program (DairyComp 305; Valley Agricultural Software, Tulare, CA). Rectal temperatures and IRT were taken once daily after the morning milking from 1 to 12 DIM, first from the cows in the milking group and then from cows in the maternity pen, and took approximately 2 h to complete (0600 to 0800). Cows in the maternity pen were restrained in head gates and cows in the milking 316

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ELISA methods were < 10%. Sample dilutions were performed on those samples in which the measured concentration was greater than the highest standard in the assay. 2.5. Statistical analysis Statistical analyses were performed using JMP statistical software (ver. 13. SAS Inc., Cary, NC, USA). An REML mixed model including the random effect of cow was used to analyze the fixed effects of parity (1 vs. 2+), calving group (1 to 4) and health diagnosis (healthy vs. sick) and their interactions on all temperature measurements (RT, mean/max eye/cheek IRT), as well as blood (IL-6, TNFα, SAA and cortisol) and 25d and 90-d cumulative milk yield measurements. Interactions were removed from the models by the Wald statistic criterion when P > .20 and all pair-wise comparisons of means were done by Student's t-test. Pearson's correlation and linear regression analyses were used to determine the within-animal relationships between the observed animal temperature and the environmental variables of ambient air temperature, relative humidity, and heat index. A predicted animal temperature was determined from the linear regression equations of observed temperature vs. heat index. The difference between the observed and predicted temperature variables for each cow and anatomical location was the residual temperature. However, in the present study there was no significant relationships between the observed animal temperatures and environmental variables, which was likely due to the small number of sample days within animal. In addition, the diagnostic performances of the residual temperature variables did not match those of the observed variables and were subsequently excluded. Temperature variables were RT and the maximum and mean IRT image temperatures. In addition, the maximum and mean image temperatures for both the eye and cheek were combined to give an “all temperature” variable. The mean image temperature is the average of all pixel temperatures in the image or region of interest, e.g. eye. The mean image temperature is highly representative of the area as a whole but not very sensitive to change. The maximum temperature is the highest temperature in the region of interest but may only be a single pixel, i.e. it is not very representative of the region as a whole. However, the maximum image temperature has been more closely associated with improved diagnostic performance because it is the variable most likely to change during a febrile response (Schaefer et al., 2007). The objective of combining eye and cheek temperature variables into a single ‘All Temperature’ was to create an IR temperature variable that was more representative of the radiated heat losses as a whole than from single anatomical locations, while still being more sensitive to change than the mean temperature only. The RT and IRT measurements were performed on all cows on 12 consecutive days. Each individual test was considered to be independent, since it was the individual test that had actionable consequences (to treat, or not to treat). Animals may have one or more days of illness but the decision to treat was independent of the number of days of illness and depended solely on the results of that day's test. Measures of test performance were therefore based on sick days rather than sick animals. Temperature measures of diagnostic test performance (specificity, sensitivity, positive predictive value, negative predictive value, Youden's (J) index, diagnostics odd ratio (DOR), and likelihood ratio (LR) were calculated for all levels of the temperature variables. In order to make objective comparisons between tests using a single referent value as a test criterion it was necessary to choose an ‘optimum’ test referent. This was done by choosing the referent value that gave the largest J index. The J index was used to select the ‘optimum’ test referent because it represents the best compromise between sensitivity and specificity (Šimundić, 2009). The performance of all diagnostic tests were evaluated for Sick 1 and Sick 2 criteria of illness. Other measures of test performance used for comparative purposes was the DOR and LR+. The DOR is the ratio of the odds of an animal with the disease testing positive to the odds of testing positive if the animal

Fig. 1. Infrared thermogram of the right eye and cheek. The mean and maximum eye temperature was determined by placing an oval over the eye, within the upper and lower eye lashes. The mean and maximum cheek temperature was determined by placing a box (width = width of eye, length = 2*eye length) one eye length below the eye, centered with the pupil.

or more of these variables were > 1 standard deviation (SD) from the group mean. Thus, Sick 2 were all animals in the Sick 1 population plus any additional animals that exhibited plasma biomarker variables that were considered outliers. 2.4. IRT image and plasma sample analyses The IRT images were analyzed using FLIR ResearchIR Max software (ITM Instruments Inc., Calgary, AB, Canada) to determine the mean and maximum eye and cheek temperatures. The atmospheric temperature and humidity corresponding to the minute the image was taken was inputted to the image analysis program to adjust for these variables on camera accuracy. A typical example of an IRT image and the areas used to obtain temperature measurements is shown in Fig. 1. Using the shape-drawing tool in the software, the eye was delineated by an oval placed on the image, using the top and bottom eyelashes as points of reference. The temperature variables were obtained from within the oval. To obtain cheek temperature variables, the shape-drawing tool was used to place a box on the cheek. The area of the box was approximately equal to the width of the eye and twice the length of the eye, and was placed one eye-length below the bottom of the eye and centered with the iris. If the cow had her head angled up or down the box was also angled to remain parallel with the bottom of the eye. Three consecutive images for each anatomical location were analyzed for temperature variables and these were combined to give a mean, standard deviation (SD) and coefficient of variation (CV) for each set of images for each cow on each sample day. If the combined CV for the IRT measurements were > 1 SD than the mean CV for the entire dataset, one of the three images was removed to leave the pair of images with the lowest CV. This procedure removed any outlier measurements and ensured that the best estimate of temperature was obtained. Images that were out of focus or did not meet the area required for cheek measurement were removed from the analysis. This process led to the rejection of 1.93% and 1.89% of the maximum and mean image temperatures, respectively for the eye location, and 3.67% and 3.43% of the maximum and mean image temperatures, respectively for the cheek location. The performance of each of the ELISA methods for the analysis of plasma variables was assessed by intra- and inter-assay variation at concentrations that approximated to high, medium and low concentrations on the standard curves. Note that with one exception (IL-6 at 5.87 pg/mL. CV = 10.3%) the intra- and inter-assay precision for all 317

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between healthy and sick cows for SAA was no longer significant (Table 1). Rectal temperature was higher in sick vs. healthy cows for both Sick 1 (Table 2) and Sick 2 (Table 3) populations (P < .02). There was no significant difference between healthy and sick cows for IRT temperatures variables in either Sick 1 or Sick 2 populations. Milk yield by 90 DIM was higher for healthy cows (3709 vs. 3483 kg; P < .001) in the Sick 1 population, and in the Sick 2 population (3725 vs. 3523 kg, P < .001), but there were no difference in milk yield between healthy or sick cows by 25 DIM for either Sick 1 or Sick 2. Correlations were performed within-animal between temperature and environmental variables (atmospheric temperature, relative humidity and heat index). There was no significant relationships detected between animal and environmental temperature variables. This was likely due to the small number of data points (n = 12) within animal. The residual temperature resulting from the linear regression analyses was a very poor diagnostic indicator, and was consequently omitted from further analysis.

does not have the disease, and it is often referred to as the diagnostic effectiveness of a test (Glas et al., 2003). The DOR scales from 0 to infinity but only values > 1 are of diagnostic relevance. The higher the DOR the better the diagnostic test. Also, the positive likelihood ratio is the ratio of the probability of a positive test in subjects with the disease (sensitivity) divided by the probability of a positive test in subjects without the disease (specificity; Šimundić, 2009). The larger the LR+ the better the diagnostic test, but only tests with LR+ ≥ 10 are considered to be diagnostically important. Note that the J index, DOR and LR+ are all independent of prevalence and all are based on calculations that use test sensitivity and specificity. A P-value of 0.05 or less was considered statistically significant, and a P-value between 0.051 and 0.1 was considered a tendency. 3. Results 3.1. General Based on clinical diagnosis to identify true positive cows for illness (Sick 1), of the 72 cows in the study, 10 cows were diagnosed with a metabolic disorder (ketosis n = 9, milk fever n = 1) and 14 cows with an inflammatory disorder (RFM n = 9, metritis n = 5). Overall, 5 cows were diagnosed with more than one disorder, giving an incidence of illness of 26.4%. Using the farm's SOP to treat cows (RT threshold ≥40 °C), 50.0% of the metabolic cows, 81.8% of the inflammatory cows and 32.1% of the healthy cows had at least one temperature ≥ 40 °C and were treated with antibiotics. In total, 13 animals were true positive for disease, 17 animals were false positives and were treated unnecessarily, and 6 sick animals were false negatives and were therefore left untreated. Based on clinical diagnosis and high blood indicators of illness to identify true positive cows for illness (Sick 2), 12 cows exhibited at least two blood parameters that were designated as outliers and of these 5 were diagnosed by clinical examination (Sick 1), and 7 animals had not been identified by Sick 1 criteria. Thus, the total number of Sick 2 animals was 26, giving an incidence of 36.1%. The mean (SE) levels for each of the blood biomarkers of illness, and temperature variables within the populations of animals defined as Sick 1 and Sick 2 are given in Table 1. Note that some animals in the Sick 2 population are defined as sick precisely because they were outliers for at least two of the blood biomarkers. Consequently, higher levels of these variables in the Sick 2 group are expected. Cortisol concentrations were higher (P = .0034) in the Sick 2 group, and the levels of statistical significance increased between healthy and sick cows for IL-6 and TNFα. Also, the difference

3.2. Temperature measurements and diagnostic performance The measures of diagnostic performance at the optimized referent levels are presented for Sick 1 and Sick 2 populations in Tables 2 and 3. Note that in both Sick 1 and Sick 2 there were very little differences in the performance characteristics of the different temperature variables. In Sick 1 (Table 2), the mean eye and the mean of all IRT temperature variables had the highest J index of 0.22 at referent temperatures of 36.0 °C and 33.7 °C, respectively. In the Sick 2 population the highest J index was 0.19 for the mean cheek temperature and for the mean of all IRT temperatures at referent temperatures of 27.2 °C and 33.7 °C, respectively. The measures were slightly higher than the diagnostic test performance of the RT. Note that with the exception of the maximum eye temperature all other IRT variables exhibited higher sensitivity compared to specificity in both Sick 1 and Sick 2 populations. This pattern was reversed for RT and the IRT maximum eye temperature, which had higher sensitivity and lower specificity for both populations. Note that the DOR and LR+ was higher in all the IRT variables compared to the RT in the Sick 2 population. However, both DOR and LR+ were calculated at the referent value chosen by the highest J index and in both cases the diagnostic performance of all tests were poor. 4. Discussion It is important to dairy producers that they are able to recognize and

Table 1 Mean (SE) of blood and temperature variables from 72 cows between calving and 12 days in milk on healthy and sick days by sick categoriesa (Sick 1 and Sick 2). Variableb

Sick 1

Sick 2

Healthy

Cortisol [nmol/L] IL-6 [pg/mL] TNFα [pg/mL] SAA [pg/mL] RT [°C] Mean Eye [°C] Max Eye [°C] Mean Cheek [°C] Max Cheek [°C]

Sick

P

Mean

SE

Mean

SE

42.8 20.0 577 14,303 39.3 35.8 38.7 26.5 30.8

1.30 2.82 17.7 563 0.01 0.04 0.04 0.15 0.11

49.2 40.5 664 6763 39.5 36.2 38.8 27.8 31.9

4.94 21.3 90.1 838 0.07 0.18 0.14 0.44 0.30

0.056 0.0005 0.004 0.0002 0.016 0.25 0.79 0.13 0.11

a

Healthy

Sick

P

Mean

SE

Mean

SE

42.1 10.7 535 13,656 39.3 35.8 38.7 26.5 30.8

1.31 1.34 14.3 579 0.01 0.04 0.04 0.16 0.11

49.5 78.6 836 13,593 39.5 36.1 38.8 27.8 31.7

3.71 15.7 71.2 1401 0.06 1.05 0.12 0.40 0.27

0.003 0.0001 0.0001 0.99 0.013 0.64 0.88 0.10 0.14

Sick categories: Sick 1 were days of illness based on clinical diagnoses from calving to 12 DIM, plus any other days that the veterinarian considered the animals to be sick but asymptomatic (see Health Diagnosis); Sick 2 days were all of the Sick 1 days plus any day (DIM 3, 6, 9 and 12) in which at least 2 blood parameters were > 1 SD above the mean for the group. b IL-6 = interleukin 6; TNFα = tumor necrosis factor alpha; SAA = serum amyloid A; RT = daily rectal temperature; mean/max eye = mean and maximum daily values for infrared thermography (IRT) temperatures taken of the right eye; mean/ max cheek = mean and maximum daily values for IRT temperatures taken of the right cheek. 318

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Table 2 Measures of test performancea at the referent levels for cows categorized as Sick 1b from 72 cows studied between calving and 12 days in milk. Variablec

J Index

Referentd (°C)

Sensitivity

Specificity

PPV

NPV

DOR

LR+

RT Mean Eye Max Eye Mean Cheek Max Cheek Mean All Temp

0.17 0.22 0.13 0.18 0.18 0.22

39.6 36.0 39.8 27.1 32.2 33.7

0.34 0.70 0.25 0.66 0.59 0.63

0.83 0.52 0.88 0.52 0.59 0.59

0.16 0.12 0.17 0.12 0.12 0.13

0.93 0.95 0.92 0.94 0.94 0.94

2.55 2.50 2.40 2.11 2.10 2.38

2.02 1.45 2.05 1.38 1.45 1.55

a

Test performance = J Index (Youden's Index) indicates the combination of the highest sensitivity and specificity values for each level of the referent; PPV (positive predictive value); NPV (negative predictive value); DOR (diagnostics odd ratio) indicates the odds of a positive test result in subjects with disease relative to the odds in subjects without disease; LR+ (positive likelihood ratio) indicates how much more likely the positive test result is to occur in subjects with disease compared to subjects without disease. b Sick 1 = true positive days of illness based on day of clinical diagnoses between calving and 12 days in milk. c RT = daily rectal temperature; mean/max eye = mean and maximum image values for infrared thermography (IRT) temperatures taken of the right eye; mean/ max cheek = mean and maximum image values for IRT temperatures taken of the right cheek; mean all temp = average of mean and maximum IRT temperatures of both the eye and cheek. d The referent temperature for each variable was selected based on the highest J index for each level of the referent. Table 3 Measures of test performancea at the referent levels for cows categorized as Sick 2b from 72 cows studied between calving and 12 days in milk. Variablec

J Index

Referentd (°C)

Sensitivity

Specificity

PPV

NPV

DOR

LR+

RT Mean Eye Max Eye Mean Cheek Max Cheek Mean All Temp

0.15 0.14 0.09 0.19 0.18 0.19

39.6 35.6 39.8 27.2 32.2 33.7

0.32 0.81 0.21 0.66 0.59 0.60

0.83 0.33 0.88 0.53 0.60 0.60

0.20 0.13 0.19 0.15 0.16 0.16

0.90 0.93 0.90 0.92 0.92 0.92

0.90 2.07 1.97 2.18 2.11 2.04

1.00 1.22 1.76 1.40 1.46 1.45

a

Test performance = J Index (Youden's Index) indicates the combination of the highest sensitivity and specificity values for each level of the referent; PPV (positive predictive value); NPV (negative predictive value); DOR (diagnostics odd ratio) indicates the odds of a positive test result in subjects with disease relative to the odds in subjects without disease; LR+ (positive likelihood ratio) indicates how much more likely the positive test result is to occur in subjects with disease compared to subjects without disease. b Sick 2 = true positive days of illness based on day of clinical diagnosis between calving as 12 days in milk and/or days of at least 2 blood parameters > 1 SD above the mean. c RT = daily rectal temperature; mean/max eye = mean and maximum daily values for infrared thermography (IRT) temperatures taken of the right eye; mean/ max cheek = mean and maximum daily values for IRT temperatures taken of the right cheek; mean all temp = average of mean and maximum IRT temperatures of both the eye and cheek. d The referent temperature for each variable was selected based on the highest J index for each level of the referent.

different stages in the inflammatory response. Interleukin-6 and TNFα are pro-inflammatory cytokines, SAA is an acute phase protein associated with the inflammatory response, and cortisol is an anti-inflammatory steroid hormone. Circulating concentrations of IL-6, TNFα and SAA are all elevated in early postpartum cows diagnosed with metritis (Dervishi et al., 2016), ketosis (Zhang et al., 2016) and milk fever (Zhang et al., 2018). Additionally, cows with a large inflammatory response have elevated IL-6 in the first 4 weeks after calving (Trevisi et al., 2012). Cortisol concentrations are increased in cows experiencing RFM as well as in those with induced endotoxemia (Pathak et al., 2015; Zebeli et al., 2013). In the present study, concentrations of cortisol, IL-6 and TNFα were higher in sick vs. healthy cows for both Sick 1 and Sick 2, indicating that elevated blood variables were diagnostically relevant. Thus, the Sick 2 population of illness was probably the more accurate representation of the level of true positive illness. Concentrations of SAA were lower in Sick 1 cows, which was the opposite of what was expected. However, in the Sick 2 cows the statistically significant difference in SAA concentrations between sick and healthy cows was eliminated. This was probably due to an increase in the number of sick days from 24 in Sick 1 to 45 in Sick 2. The additional days increased the mean SAA concentration of the Sick 2 population. Including the biomarkers in the definition of Sick 2 identified additional animals that were not detected by clinical examination. The effect was to increase the incidence of disease in the test population of animals from 26% to 36%, which falls within the estimated range of 30 to 50% of cows experiencing disease around the time of calving (LeBlanc, 2010). Rectal temperature is the most common method to monitor health

treat illness in their animals as early as possible. To this end, many farms screen early lactation animals daily by measuring the animal's core temperature (Smith and Risco, 2005). Animals exhibiting a RT greater than a specific referent value are treated with antibiotics. Obtaining a RT is a low-tech procedure requiring only a thermometer; it is inexpensive and easy to perform. However, it requires that animals are captured and restrained, and this handling component usually limits measurement to once per day. Infrared thermography offers an alternative method of assessing animal temperature. Infrared imaging is a much more sophisticated technology and has the advantage that it can be performed remotely and without the need to restrain animals. Many measurements can be made within a short time frame, and the technology has the potential to be automated (Franze et al., 2012). However, IRT must take into consideration consistent image angle and distance to the subject, as well as ambient temperature, wind speed and direct sunlight (Church et al., 2014). The objective of this study was to evaluate and compare the performance of RT and IRT measurements to identify cows suffering from a health disorder in early lactation. Health examinations were conducted for common transition diseases (RFM, metritis, ketosis and milk fever). However, it was recognized that a crucial aspect of measuring diagnostic test performance is the identification of true positive animals. In this regard, we expected that some cows would likely have a health disorder that did not present with clinical symptoms. Thus, in order to best determine true positive sickness we included four biomarkers and defined sick animals as those having at least two biomarkers with concentrations that were defined as outliers. A combination of biomarkers were chosen that represented 319

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may have induced a relatively faster increase in the maximum eye temperature than the other IRT variables. This would have resulted in a higher rate of false positive test results for the eye max variable, thereby reducing the test sensitivity but increasing specificity. Infrared variables tended to have higher sensitivity and lower specificity than RT or max eye temperature, and therefore were more likely to detect true sick animals. The trade-off is that these variables were also more prone to false positives, leading to unnecessary treatment. Using the IRT cheek variables, the unwanted cost to the producer is in the form of wasted antibiotics due to relatively higher rate of false positive tests, which contributes to the over-treatment of livestock causing potential problems with antibiotic resistance (Trevisi et al., 2014). Conversely, diagnosing sick transition cows using RT or maximum eye temperature had relatively lower sensitivity and higher specificity. This would result in less sick cows being detected but would significantly reduce the rate of false positive tests and reduce the amounts of antibiotics being used. The trade-off is that relatively more sick cows would remain untreated. Thus, the choice of temperature variable comes down to the farm's assessment of the costs associated with treating as many animals as possible but including many animals that are treated unnecessary, or increasing the confidence in treating sick animals but paying the price of missing more truly sick animals. Based on the only moderate accuracy of both RT and IRT, it is important to consider other disease symptoms and indicators of illness, i.e. milk yield, feed intake, attitude, etc., before treating animals with antibiotics. In which case, a screening test with a higher sensitivity may be more valuable to producers to identify animals at risk of disease that should be further evaluated or monitored before treating. Previous studies have shown higher diagnostic test accuracy with IRT compared to the current study. When using IRT max eye temperature to identify a febrile response and diagnose BRD in beef calves, the sensitivity and specificity were 100 and 97% in a small sample size (n = 9; Schaefer et al., 2012) and 68 and 87% in a larger sample size (n = 65; Schaefer et al., 2007), respectively. However, in those beef cattle studies a RT = 40 °C also outperformed RT in the current study, with a sensitivity and specificity of 100 and 97% (Schaefer et al., 2012), and 80 and 100% (Schaefer et al., 2007), respectively. The weaker performance of RT and IRT in the current study is likely due to the general nature of transition diseases, as opposed to a specific and highly inflammatory disease such as BRD. Unfortunately, there were insufficient numbers of sick animals in the infectious and metabolic diseases categories to evaluate the diagnostic efficacy of the temperature variables. Also, although metabolic disease are much less likely to be associated with a febrile response than infectious diseases or inflammation, screening of transition cows using RT on farm does not make that distinction. While including metabolic diseases may reduce diagnostic efficacy, cheek temperature was included because of its association to metabolic status and increases in temperature due to additional inflammatory processes or stress from disease cannot be discounted. The effect of combining infectious and metabolic diseases into a single ‘sick’ category was to increase the variance in the IRT measurement and thus to reduce the chances of finding a difference in IRT variables between sick and healthy animals. Nevertheless, the combination of illnesses in the transition cows in the present study reflected the on-farm reality. Another possibility is that the lack of differences in IRT variables between healthy and sick animals was due to the imaging procedure being performed only once per day. There is inherently greater variation in radiated temperature compared to core temperature and single time-point measurements probably do not adequately capture the individual animal's thermoregulatory state. This situation would be improved if multiple images could be taken at each milking time. The effect of restraining and handling the animals in the current study to take both RT and IRT measurements cannot be overlooked, as a stress response may result in increased radiated temperature, particularly of the eye (Cook et al., 2000). Previous studies have shown an

in transition dairy cows and aid in the management of disease, particularly inflammatory disease (Smith and Risco, 2005). The SOP of the participating farm in this study used a referent RT of ≥40 °C. However, data from the present study suggests that a RT ≥ 39.6 °C would be more diagnostically efficient. Regardless of sick definition, RT exhibited a mid-range J index compared to the IRT temperature variables, although the differences among temperature variables was very small. Based on the Sick 2 population, RT had a sensitivity of 32% and a specificity of 83%. This compared favourably with the maximum eye temperature, which had a sensitivity and specificity of 21% and 88%, respectively. However, all other IRT variables exhibited the opposite phenomena with relatively high sensitivity and low specificity. We do not have a definitive explanation of why the levels of test sensitivity and specificity for the maximum eye temperature should be the opposite of all other IRT variables. However, it could be due to the maximum eye temperature responding rapidly to the stress of handling and restraint to obtain the temperature measurements, thereby increasing the numbers of false positive tests and effectively increasing test specificity but reducing sensitivity. In fact, none of the temperature variables performed very well from a diagnostic perspective and it is possible that the opposite effects noted above was due to discrepancies in the IRT variables for a few animals. There is a large degree of variation between cows for RT, in which Wenz et al. (2011) determined a range of 37.9 to 39.6 °C in healthy dairy cows. Wagner et al. (2008) reported that 17% of healthy early lactation cows had at least one RT of 39.7 °C or greater, i.e. false positive animals, which the author attributed to tissue damage from calving. Additionally, Benzaquen et al. (2007) reported a test sensitivity of 59% for RT in cows with uterine infection showing fever, which may have been due to the degree of variation in temperature increase between cows. This is higher than in the present study in which the test sensitivity was 32% for RT. However, uterine infection is more likely to stimulate a febrile response than the diseases included in the present study. It should also be noted that this test sensitivity is reasonably typical of the IRT variables in the present study, in which the average test sensitivity was 53.7%. Thus, approximately 50% of sick animals were not detected by the tests and not treated, which can have consequences for reproduction, production and productive lifespan (Liang et al., 2017). Infrared thermography has been used to detect specific inflammatory diseases in cattle, by identifying areas of inflammation, i.e. in the udder (Metzner et al., 2014) or hoof (Rainwater-Lovett et al., 2009), or identifying a febrile response, i.e. from bovine respiratory disease or bovine viral diarrhea infection (Schaefer et al., 2004, 2007). To the best of our knowledge, there are no studies that have compared RT with IRT to identify general illness in early postpartum dairy cows. In order for IRT to replace RT, it must be rapid, minimize handling of animals and effectively identify animals that are sick and requiring attention. In the current study, IRT was performed in a similar way as RT in order to make a direct comparison. According to the mixed model analysis, there was no difference in IRT temperature variables between healthy and sick cows. This may be due to the large amount of variation in IRT measurements. However, in terms of diagnostic performance, the IRT cheek variables had a higher J index than RT, particularly for the mean eye and the cheek IRT variables. It is noteworthy that the mean eye, max cheek and mean cheek IRT variables exhibited higher sensitivity than specificity but the opposite occurred for the maximum eye temperature. The similarities in performance between the mean eye, max cheek and mean cheek variables may have been due to the mean eye temperature including areas of the skin around the eye, and was therefore similar to measuring the skin temperature of the cheek. The lower test sensitivity and higher specificity exhibited by the max eye variable compared to the opposite pattern shown by the other IRT variables may be an artefact of the much more rapid response time of the maximum eye temperature to stress events. A stress response to handling and restraint in order to make the temperature measurements 320

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5. Conclusion

association between increased eye temperature and more invasive handling procedures, such as catheterization (Stewart et al., 2007) and transport (Lecorps et al., 2018). Conversely, Gómez et al. (2018) did not find any response of radiated eye temperature to restraint and claw trimming of diary cattle. In the current study, no animals were isolated and animals were habituated to the handling and restraint procedure. Also, care was taken to minimize the time for sampling. We do not consider the restraint in the current study to be particularly onerous or to have caused a stress response of sufficient magnitude to increase radiated temperatures. Nevertheless, we cannot definitively rule out the possibility that an increase in radiated temperatures, particularly of the eye, may have been due to the handling and restraint procedures. The evidence from the present study suggests that IRT is as effective at identifying sick early postpartum cows as RT, but neither method is a particularly good screening test. Unfortunately, the diagnostic efficacy of the technique was limited by once daily measurements. The infrequency of RT measurements, i.e. one per day, was reported by Suthar et al. (2011) to provide insufficient accuracy to identify sick cows, and a more frequent measurement protocol would very likely improve the diagnostic performance. However, more frequent RT measurements would increase the time and handling required, making this method less desirable. In the current study IRT measurements were made in a similar way to RT measurements in order to make direct comparisons, but this is not the most effective use of IRT. The remote nature of IRT measurements enables multiple measurements to be made automatically. Future studies evaluating IRT to identify sick animals during the transition period should take advantage of the capacity of IRT to be automated. An IR camera could be fixed at a location that measures the animal's temperatures in real time and without the need to cause stress from handling or restraint, likely increasing the diagnostic performance. For example, IR cameras could be located in an automated milking unit, or in the alley leading to, or away from, the milking parlour. In this way radiated temperature could be recorded multiple times per animal when the animal passes the location of the camera. Thus, temperature measurements could be made multiple times during each milking, which may be two or three times per day. This would increase confidence that the measured temperatures were truly representative of the animal at the time of recording, and provide an estimate of the daily average temperature that may be more representative of the animal's thermoregulatory processes. Hoffmann et al. (2013) reported reduced variance in temperature measurements when the IRT camera was in a fixed position facing an automated milking unit with a reference temperature plate fixed opposite, confirming IRT to be an effective method to remotely monitor temperature of dairy cows. Note that the reference temperature plate used by Hoffmann et al. (2013) was used to measure the radiated background temperature. This can be used firstly as a proxy for ambient air temperature and inputted into the camera settings for the purpose of maximizing camera accuracy, and secondly to account for thermoregulatory responses of the animals to environmental conditions. In the current study there was no evidence to suggest that the observed animal's temperatures were significantly related to ambient conditions. However, this was most likely due to insufficient sampling to characterize this relationship. Infrared thermography has shown to be effective at predicting illness before clinical signs (Schaefer et al., 2007) and that rate of change in temperature over time and the degree of variation in an individual, using itself as a control, may be a valuable indicator of illness (Schaefer et al., 2012). This would be possible if the IRT camera was set up to record multiple images each time the animal was in the milking system. While the current study generated evidence that IRT is comparable to RT, the potential to automate IRT for remote disease surveillance may allow IRT to surpass RT and warrants further investigation in dairy cows.

In conclusion, RT and several IRT variables performed similarly in diagnosing sick transition cows. The best performing IRT measurements were mean eye, mean cheek, mean of all cheek temperatures and mean of all IRT temperatures. In general, RT had a low sensitivity and high specificity, indicating an increased risk of missing cows that were truly sick, while IRT had a more moderate sensitivity and specificity, indicating an increased risk to mistakenly treat healthy cows. Overall, when IRT was used in the same way as RT, it was comparable to RT at identifying sick cows that required attention. However, the tests of diagnostic performance indicated that both temperature measurements were not particularly good as screening tests. The ability to automate IRT measurements would likely increase the accuracy of identifying sick cows in a way that does not increase time and handling of cows and warrants further study. Acknowledgements The project was financially supported by Agriculture and Agri-Food Canada, Growing Forward 2 (Grant # MC827471). The authors thank Breevliet Ltd, Wetaskiwin, Alberta for permitting this project on their dairy farm. References Alsaaod, M., Schaefer, A.L., Buscher, W., Steiner, A., 2015. The role of infrared thermography as a non-invasive tool for the detection of lameness in cattle. Sensors 15, 14513–14525. Benzaquen, M.E., Risco, C.A., Archbald, L.F., Melendez, P., Thatcher, M.J., Thatcher, W.W., 2007. Rectal temperature, calving-related factors, and the incidence of puerperal metritis in postpartum dairy cows. J. Dairy Sci. 90, 2804–2814. Canadian Council on Animal Care, 2009. Guide to the Care and Use of Experimental Animals, 2nd Edn. 1 CCAC, Ottawa, ON. Church, J.S., Hegadoren, P.R., Paetkau, M.J., Miller, C.C., Regev-Shoshani, G., Shaefer, A.L., Schwartzkopf-Genswein, K.S., 2014. Influence of environmental factors on infrared eye temperature measurements in cattle. Res. Vet. Sci. 96, 220–226. Cook, N.J., Schaefer, A.L., Warren, L., Burwash, L., Anderson, M., Baron, V., 2000. Andrenocortical and metabolic responses to ACTH injection in horses: an assessment by salivary cortisol and infrared thermography of the eye. Can. J. Anim. Sci. 81, 621 (Abstract). Dervishi, E., Zhang, G., Hailemariam, D., Goldansaz, S.A., Deng, Q., Dunn, S.M., Ametaj, B.N., 2016. Alterations in innate immunity reactants and carbohydrate lipid metabolism precede occurrence of metritis in transition dairy cows. Res. Vet. Sci. 104, 30–39. Franze, U., Geidel, S., Heyde, U., Schroth, A., Wirthgen, T., Zipser, S., 2012. Investigation of Infrared Thermography for Automatic Health Monitoring in Dairy Cows. Möglichkeiten Des Einsatzes der Infrarot-Thermographie Zur Automatischen gesundheitsüberwachung Bei milchkühen 84. pp. 158–170. Glas, A.S., Lijmer, J.G., Prins, M.H., Bonsel, G.J., Bossuyt, P.M.M., 2003. The diagnostic odds ratio: a single indicator of test performance. J. Clin. Epidemiol. 56, 1129–1135. Gómez, Y., Bieler, R., Hankele, A.K., Zähner, M., Savary, P., Hillmann, E., 2018. Evaluation of visible eye white and maximum eye temperature as non-invasive indicators of stress in dairy cows. Appl. Anim. Behav. Sci. 198, 1–8. Hoffmann, G., Schmidt, M., Ammon, C., Rose-Meierhöfer, S., Burfeind, O., Heuwieser, W., Berg, W., 2013. Monitoring the body temperature of cows and calves using video recordings from an infrared thermography camera. Vet. Res. Commun. 37, 91–99. LeBlanc, S., 2010. Health in the transition period and reproductive performance. WCDS Adv. Dairy Technol. 22, 97–110. Lecorps, B., Kappel, S., Weary, D.M., von Keyserlingk, M.A.G., 2018. Dairy calves’ personality traits predict social proximity and response to an emotional challenge. Sci. Rep. 8. https://doi.org/10.1038/s41598-018-34281-2. Liang, D., Arnold, L.M., Stowe, C.J., Harmon, R.J., Bewley, J.M., 2017. Estimating US dairy clinical disease costs with a stochastic simulation model. J. Dairy Sci. 100, 1472–1486. Macmillan, K., López Helguera, I., Behrouzi, A., Gobikrushanth, M., Hoff, B., Colazo, M.G., 2017. Accuracy of a cow-side test for the diagnosis of hyperketonemia and hypoglycemia in lactating dairy cows. Res. Vet. Sci. 115, 327–331. Metzner, M., Sauter-Louis, C., Seemueller, A., Petzl, W., Klee, W., 2014. Infrared thermography of the udder surface of dairy cattle: characteristics, methods and correlation with rectal temperature. Vet. J. 199, 57–62. Montanholi, Y.R., Swanson, K.C., Palme, R., Schenkel, F.S., McBride, B.W., Lu, D., Miller, S.P., 2010. Assessing feed efficiency in beef steers through feeding behavior, infrared thermography and glucocorticoids. Animal 4, 692–701. Oikonomou, G., Trojacanec, P., Ganda, E.K., Bicalho, M.L.S., Bicalho, R.C., 2014. Association of digital cushion thickness with sole temperature measured with the use of infrared thermography. J. Dairy Sci. 97, 4208–4215. Pathak, R., Prasad, S., Kumaresan, A., Kaur, M., Manimaran, A., Dang, A.K., 2015.

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