Methodology to utilize thoracic ultrasound for respiratory pathology differentiation and improved prognosis

Abstract

Provided herein is a method for rapidly assessing a cattle patient for respiratory disease using targeted thoracic point-of-care ultrasound (TT-POCUS). This assessment begins with scanning a thoracic region of a lung of the cattle patient with an ultrasound probe to generate an ultrasound image. Subsequently, a count of A-lines and a count of B-lines are identified, along with the presence or absence of severe lung consolidation and / or abnormal pleural findings. The findings from the method can be used to distinguish interstitial pneumonia from non interstitial pneumonia, and predict future treatment success.

Description

METHODOLOGY TO UTILIZE THORACIC ULTRASOUND FOR RESPIRATORYPATHOLOGY DIFFERENTIATION AND IMPROVED PROGNOSISGOVERNMENT LICENSE RIGHTS

[0001] Not ApplicableFIELD

[0002] The present disclosure relates to methods of ultrasound to improve disease diagnosis, prognosis, management, and treatment in animals. In some forms, the disclosure provides methods for differentiating interstitial pneumonia and bronchopneumonia, as well as prognosis at initial and third treatment in cattle identified with respiratory disease.BACKGROUND

[0003] Respiratory' diseases including Bovine respiratory' disease (BRD) are a pressing health and economic challenge within the feedyard industry', recognized as a leading contributor to both morbidity' and mortality7in cattle. The economic repercussions of respiratory' diseases such as BRD are substantial, extending beyond direct treatment and mortality' costs and including indirect losses from diminished weight gain, compromised carcass quality', and overall reduced productivity7. The complexity of diseases like BRD stems from a mix of viral and bacterial pathogens, environmental stressors, and management factors, all of which complicate definitive diagnosis and prognosis. Some of the viruses that are linked to BRD include Bovine Respiratory Syncytial Virus (BRSV), Bovine Parainfluenza-3 virus (BPI3V), Infectious Bovine Rhinotracheitis (IBR), Bovine Viral Diarrhea Virus (BVDV), Parainfluenza-3 virus, bovine coronavirus, and adenoviruses. Some of the bacteria that are linked to BRD include Mannheimia haemolytica, Pasteurella multocida, Histophilus somni,Mycoplasma bovis, Arcanobacterium pyogenes, and Salmonella. Furthermore, these diseases impose significant animal welfare concerns, as affected cattle often endure distress and protracted recovery' periods.

[0004] For purposes of this disclosure, all pathogens causing bovine respiratory' diseases are contemplated as each can lead to the devastating effects of pneumonia. Two common but distinct forms of pneumonia caused by diseases like BRD are bronchopneumonia (BP) and interstitial pneumonia (IP). Bronchopneumonia is frequently associated with bacterial infections and typically involves inflammation and consolidation within the airways and alveoli. Interstitial pneumonia, by contrast, primarily affects the structural tissue framework of the lungs called the interstitium and can arise from a variety7of causes, often being idiopathic in terms of its underlying pathologic processes.

[0005] Successful and rapid differentiation between BP and IP is critical for effective cattle management. The costs, logistics, and time constraints of cattle management are typically prohibitive of expensive and time-consuming methods of assessment, diagnosis, prognosis, and treatment. Furthermore, treatment strategies differ: BP ty pically requires antimicrobial therapy to target infectious bacterial agents, whereas IP may require anti-inflammatory7or supportive care to address an underlying infection. Thus, early and accurate diagnosis to distinguish between these conditions is essential for guiding appropriate therapeutic interventions, optimizing resource allocation, and improving animal health outcomes. Misdiagnosis can lead to ineffective treatments, prolonged suffering, and increased economic losses.

[0006] Thoracic ultrasound (TUS) has emerged as a valuable non-invasive diagnostic tool in veterinary medicine for assessing respiratory diseases in cattle. It offers real-time imaging of thoracic structures, allowing for the visualization of pulmonary abnormalities that otherwise might not be as readily or accurately detectable using traditional techniques such asauscultation. While TUS has shown promise in identifying lung lesions and has been correlated with clinical respiratory scores, traditional systematic thoracic ultrasound evaluations can be time-consuming, potentially limiting the practical application of ultrasound in rapid chute-side scenarios typical of feedlots. For example, even an expert performing traditional systematic thoracic ultrasound evaluations would typically take between eight and ten minutes, and the process would be even more time-consuming without an expert.

[0007] Therefore, there exists a pressing need for a rapid, accurate, and practical on-site diagnostic technique that can reliably differentiate between conditions such as bronchopneumonia and interstitial pneumonia in feedyard cattle, particularly early in the stage of disease. Such a technique would empower veterinarians and other cattle workers to make more informed, targeted treatment decisions, improving prognoses, enhancing animal welfare, and potentially saving a great deal of labor, money, and other resources.SUMMARY

[0008] Described herein is a method of rapidly diagnosing, predicting diagnosis, and / or predicting prognosis in an animal, more preferably a mammal, and still more preferably, cattle, buffalo, swine, goats, and sheep, and most preferably cattle for respiratory disease using targeted thoracic point-of-care ultrasound (TT-POCUS). Initially, a thoracic region of a lung of the cattle subject is scanned with an ultrasound probe, thereby generating an ultrasound image. From this image, several assessments and / or predictions can be accomplished including the identification and a count of A-lines and / or B-lines and / or moth sign, and / or the presence, absence, or severity of lung consolidation. Based on these findings alone, or when combined with other factors or variables, a rapid diagnosis, diagnosis prediction, and / or prognosis for the cattle patient is determined. Specifically, in oneembodiment of the disclosure, the rapid diagnosis finds and / or predicts no respiratory disease in the scanned area when the B-line count is fewer than three and the absence of severe lung consolidation has been identified. The rapid diagnosis finds or predicts interstitial pneumonia in the scanned area when the B-line count is greater than or equal to three. Conversely, in another embodiment of the disclosure, the rapid diagnosis finds or predicts respiratory disease that is not interstitial pneumonia when the presence of severe lung consolidation has been identified. This diagnostic method can be further refined by determining a presence or absence of one or more abnormal pleural findings, where such findings may include a moth sign. Additionally, if the count of A-lines is less than three, the rapid diagnosis or prediction is considered to be one of either interstitial pneumonia or respiratory disease that is not interstitial pneumonia. The thoracic region for this (TT-POCUS) scanning can be within intercostal spaces between an 8th and an 11th rib on a right side of the cattle patient.

[0009] In various forms, a method according to the present description involves rapidly assessing a cattle patient for respiratory disease using TT-POCUS. Importantly, this assessment is much faster than previously possible using methods in the art. As noted above, a typical scan by an expert using previously-available methods might take 8-10 minutes, but the methods described herein can provide the assessment in less than 8, 7.9, 7.8, 7.7, 7.6, 7.5,7.4, 7.3, 7.2, 7.1. 7, 6.9, 6.8. 6.7, 6.6, 6.5, 6.4, 6.3, 6.2, 6.1, 6, 5.9, 5.8, 5.7, 5.6, 5.5. 5.4, 5.3,5.2, 5.1, 5, 4.9. 4.8, 4.7, 4.6. 4.5, 4.4, 4.3, 4.2, 4.1, 4, 3.9, 3.8, 3.7, 3.6, 3.5, 3.4, 3.3. 3.2, 3.1,3, 2.9, 2.8, 2.7, 2.6, 2.5, 2.4, 2.3, 2.2, 2.1, 2. 1.9, 1.8, 1.7, 1.6, 1.5, 1.4, 1.3. 1.2, and even less than 1 minute including less than 55, 5. 45, 40, 35, and 30 seconds. In one embodiment of the disclosure, this assessment begins with scanning a thoracic region of a lung of the cattle patient with an ultrasound probe to generate an ultrasound image. In some forms, a count of A-lines and / or a count of B-lines are identified. In some forms, the presence or absence of severe lung consolidation is also assessed. An ultrasound lung score (ULS), an integer-basednumerical score ranging from 1 to 5, is then assigned to the ultrasound image based on these parameters. The ULS score is 1 when the count of B-lines is fewer than three. A ULS score of 2 is assigned when the count of B-lines is three or greater and the B-lines have a thickness of less than 7 mm. If the B-lines have a thickness greater than 7 mm, the ULS score is 3. A ULS score of 4 is determined when the B-lines have a thickness greater than 7 mm and the presence of one or more abnormal pleural findings has been determined; these abnormal pleural findings may include a moth sign. Finally, a ULS score of 5 is assigned when severe lung consolidation has been determined. The thoracic region for this assessment is ty pically within intercostal spaces between an 8th and an 11th rib on a right side of the cattle patient.

[0010] The ULS-based assessment method can further include a step of rapidly generating a disease diagnosis, diagnosis prediction, and / or prognosis. If the ULS score is 1 and the A-line count is three or more, the disease diagnosis or diagnosis prediction is no respiratory disease, at least for the scanned area. If the ULS score is 2, 3. or 4, the disease diagnosis or diagnosis prediction is likely interstitial pneumonia. If the ULS score is 5, the disease diagnosis or diagnosis prediction is respiratory disease that is not interstitial pneumonia.

[0011] Furthermore, the ULS assessment method can be extended to provide prognostic information for a cattle patient displaying one or more clinical signs of respiratory’ disease, subsequent to a first treatment. This includes a first treatment failure (FTF) prognosis, which is defined as the probability that the first treatment will not successfully treat the cattle patient. If the ULS score is 1, 2, or 3, the FTF prognosis is less than 50%. If the ULS score is 4, the FTF prognosis is greater than 50%. If the ULS score is 5, the FTF prognosis is greater than 65%. Similarly, a did not finish (DNF) prognosis can be determined. The DNF prognosis is the probability that the cattle patient will die or be culled within 60 days after the first treatment. If the ULS score is 1, 2, or 3, the DNF prognosis is less than 35%. If the ULSscore is 4, the DNF prognosis is less than 45%. If the ULS score is 5, the DNF prognosis is greater than 65%.As would be expected, the accuracy of diagnosis, diagnosis prediction, and prognosis can be rendered more accurate when additional factors are considered. For example, the considerations of ULS, A line count, B line count, lung consolidation, moth sign, days on feed, sex of the animal, first treatment failure, number of treatments, and first treatment success can be combined such that one or more of these variables are included in the analysis and subsequent diagnosis, diagnosis prediction, and / or prognosis. Thus, the method may include consideration of 1, 2, 3. 4, 5. 6, 7, 8. or 9 of these variables to provide the ultimate diagnosis, diagnosis prediction, and / or prognosis.

[0012] In some forms, a variation of the TT-POCUS methodology described above is provided for diagnosing and / or predicting a diagnosis of congestive heart failure (CHF). CHF is an emerging syndrome in feedyard cattle. CHF is the cause of death in 5-7% of feedyard mortalities and this syndrome may play a contributing role in mortalities from other causes. Research illustrates this syndrome is found in many North American feedyards and common breeds and while higher altitudes may enhance the syndrome this disease is also found at low altitudes. The challenge with CHF is cattle are often not identified until the disease has progressed to end-course. There are no effective treatments for CHF and early identification allows producers to cull or remove affected animals prior to further animal welfare concerns or economic loss. Currently there is no definitive way to diagnose CHF antemortem and clinical signs are very similar to BRD cases. Distinguishing CHF from BRD is critical as several BRD therapies are available, but no effective treatments exist for CHF.

[0013] Using targeted POCUS (T-POCUS) specifically to examine hepatic vasculature provides insight into the likelihood of CHF and the stage of progression. CHF in cattle is right sided resulting in liver congestion and hepatic vessel dilation: monitoring this dilationcan be measured quickly and easily with T-POCUS as an entire liver scan does not need to be done but only the relevant subsection. Using this scanning technique the information from T- POCUS allows identification of CHF cases and can help formulate the prognosis. In general, the T-POCUS scan for CHF is accomplished using the same protocol as TT-POCUS. In brief, an ultrasound probe is utilized with fanning that targets the desired targeted area of the liver and the vascular structure thereof. Knowledge gained from the ultrasound can be used to diagnose CHF, predict CHF diagnosis, and / or prognosis.

[0014] Traumatic reticuloperitonitis or reticulopericarditis (“hardware disease”, TRP) can present in animals of any age, but is a relatively common cause of mortality in feedyard cattle. This disease is caused when foreign material is ingested, pierces the reticulum, and incites infection in the peritoneum, pleural cavity, or pericardial sac. Typically the foreign material is hard and sharp in nature (e.g. metallic objects such as a nail). This disease can cause severe impacts and ultimately result in death. Some treatments are available including antibiotics or surgical flushing; however, in most cases the prognosis is very poor. Cattle with TRP present with similar clinical signs to BRD and CHF and distinguishing them from BRD and CHF allows the facility to implement appropriate preventative, treatment, and control measures to manage cunent cases and prevent new ones.

[0015] Using T-POCUS can rapidly and efficiently identify TRP cases by quick scans in two specific areas: the peritoneal region and identification of pleuritis in the pulmonary region. Confirming a TRP diagnosis allows the feedyard to make improved management decisions.

[0016] Abdominal T-POCUS can be used to evaluate gastrointestinal (GI) functionality to identify specific common cattle diseases such as ruminitis, bloat, acidosis, enteritis, and leaky gut. Several of these diseases (ruminitis, acidosis, and subsequent bloat) may be the result of dietary change or upset and when identified in groups of cattle could lead to changes in the ration, feed delivery, or amount fed. Understanding the specific diagnosis in individuals andgroups of cattle provides valuable information to both health care and nutritional providers to adjust the ration accordingly. Abdominal T-POCUS can focus on key spots within the abdomen to evaluate GI functionality'.

[0017] Diseases such as enteritis may be infectious and spread among individuals within a group. Using T-POCUS to identify impacts within the GI tract allows early identification and subsequent treatment.

[0018] Musculoskeletal T-POCUS involves utilizing the probe to scan musculature and fat to determine several specific findings relevant to predicted performance traits. The probe will be placed perpendicular to the longissimus dorsi muscle around the 12th to 13th ribs to measure a cross section through the backfat and muscle. Key findings to measure include thickness of backfat, size of the muscle, and expected marbling. These factors can be used combined with animal characteristics to determine the optimal finish for an individual animal based on desired carcass end-point based on physiologic maturity, expected carcass transfer or incremental cost of gain.

[0019] Serial scans of individual animals throughout the growth phase also provide additional information to understand differences in rate of fat and muscle deposition allowing prediction of number of days until cattle reach optimal end point for harvest. Using musculoskeletal T-POCUS once or as a serial scan can also be used to determine how to sort individual animals into specific feeding groups or most appropriate marketing methods.

[0020] In some forms, the present disclosure provides a method of assessing the health status of lungs in a mammal comprising the steps of: performing an ultrasound procedure using an ultrasound probe in the thoracic region of the mammal to produce an ultrasound image therefrom; assessing the ultrasound image for at least one variable selected from the group consisting of an ultrasound lung score, A lines, B lines, moth sign, lung consolidation and any combination thereof, wherein said procedure and assessing is complete in less than 6minutes. In some forms the at least one variable is scored according to a standard system correlating with the condition of the lung using that variable. In some forms, the method further comprises differentiating between interstitial pneumonia (IP) and non IP respiratory7disease or infection in the mammal. In some forms, the mammal is a bovine. In some forms, the bovine is suspected of having respiratory disease or infection prior to the ultrasound procedure. In some forms, the ultrasound procedure includes fanning the probe in the thoracic region. In some forms, the thoracic region includes the intercostal spaces between an 8th and an 11th rib on a right side of the mammal. In some forms, the scoring of at least one variable is used to provide a probability7of the mammal having IP. In some forms, the scoring of more than one variable is combined using a formula to provide a more accurate probability that the mammal has or does not have IP. In some preferred forms, the mammal is cattle and wherein the formula is P(Histo_IP = 1) = 1 / (1 + exp[-(0.5696- 0.3834 • Sex_Steer+ 2.1331• DOF 43 71+ 1.6642 • DOF_>71+ 1.3601 • ULS_3+ 0.9919 • ULS_4- 2.8504 • ULS 5- 0.7569 • Bline_3_5- 1.4435 ■ Aline_>3)]), wherein, “P(Histo_IP = 1)” denotes the probability of a diagnosis of IP in the cattle patient, and wherein “Sex_Steer” refers to the sex of the cattle, with a value of ‘ 1’ denoting a steer and a value of ‘O’ denoting a heifer; DOF 43-71 refers to the cattle’s days on feed, with a value of ‘1 ’ denoting a 43-71 days on feed and a value of ‘0’ denoting otherwise; DOF_>71 refers to the cattle’s days on feed, with a value of ' 1 ’ denoting greater than 71 days on feed and a value of ‘0’ denoting otherwise; ULS_3 refers to the ultrasound lung score (ULS) as described above, with a value of T denoting a ULS of 3 and a value of ‘0’ denoting otherwise; ULS_4 refers to the ultrasound lung score (ULS) as described above, with a value of ‘ 1’ denoting a ULS of 4 and a value of ‘0’ denoting otherwise; Bline 3 5 refers to the count of B-lines, with a value of ‘ 1 ’ denoting 3-5 B-lines and a value of ‘0’ denoting otherwise; and Aline_>3 refers to the count of A-lines, with a value of ‘ 1’ denoting greater than 3 A-lines and a value of ‘0’ denoting otherwise. In someforms, the method further includes at least one variable selected from the group consisting of days on feed (DOF), body weight (BW), number of treatments for respiratory disease, and any combination thereof, and wherein the assessing provides a probability of whether or not the mammal will finish the feed stage, or whether a first or subsequent treatment for respiratory disease will be successful. In some forms, the scoring of more than one variable is combined using a formula to provide a more accurate probability that the mammal will or will not finish after a first treatment for respiratory disease within a test period. In some forms, the formula is P(DNF = 1) = 1 / (1 + exp[-(-1.5210- 0.4811 ■ Sex_Steer+ 0.7798 • DOF 43 71+ 1.0185 ■ D0F gt7 l - 0.3213 ■ LungScore_2- 0.7426 • LungScore_3- 0.5706 • LungScore_4+ 1.1356 • LungScore_5- 0.9507 ■ BW_600_800- 1.4869 • BW_800_1000- 0.3817 • BW gt I OOO- 1.5883 • Bline_gt3+ 1.1321 • Moth_Yes)]), where “P(DNF = 1)” denotes the probability' of the cattle patient not finishing within the test period; and wherein “Sex_Steer” refers to the sex of the cattle, with a value of ‘1 ’ denoting a steer and a value of ‘0’ denoting a heifer; DOF_43-71 refers to the cattle’s days on feed, with a value of ‘ 1 ’ denoting a 43-71 days on feed and a value of ‘0’ denoting otherwise; DOF_>71 refers to the cattle’s days on feed, with a value of ‘ 1’ denoting greater than 71 days on feed and a value of ‘0’ denoting otherwise; DOF_gt71 refers to the cattle’s days on feed, with a value of ‘1 ’ denoting greater than 71 days on feed and a value of ‘0’ denoting otherwise; Lung Score _3 refers to the ultrasound lung score (ULS) as described above, with a value of ‘ 1’ denoting a ULS of 3 and a value of ‘0’ denoting otherwise; Lung Score _4 refers to the ultrasound lung score (ULS) as described above, with a value of ’ I ’ denoting a ULS of 4 and a value of "O’ denoting otherwise; Lung Score 5 refers to the ultrasound lung score (ULS) as described above, with a value of ‘ 1 ’ denoting a ULS of 5 and a value of ‘0’ denoting otherwise; BW 600 800 refers to the body weight of the cattle, with a value of ‘1 ’ denoting a body weight between 600 and 799 pounds and a value of ‘0’ denoting otherwise; BW_800_1000refers to the body weight of the cattle, with a value of ‘ 1 ' denoting a body weight between 800 and 999 pounds and a value of ‘0’ denoting otherwise; BW_gt 1 ()()() refers to the body weight of the cattle, with a value of ‘ T denoting a body weight greater than 1000 pounds and a value of ‘O' denoting otherwise; Bline_gt3 refers to the count of B-lines, with a value of ‘ 1 ’ denoting greater than 3 B-lines and a value of ‘0’ denoting otherw ise; and Moth_Yes refers to the presence or absence of a moth sign, with a value of ‘ 1’ denoting the presence of a moth sign in the corresponding cattle ultrasound, and a value of ‘0’ denoting the absence of a moth sign. In some forms, the scoring of more than one variable is combined using a formula to provide a more accurate probability that the mammal will respond to a subsequent treatment after a first treatment. In some forms, the formula is P(FTS = 1) = 1 / (1 + exp[-(0.52110+ 0.30445 • Sex_Steer+ 0.09533 • DOF_43_71- 0.80601 • DOF_gt71+ 0.11039 • LungScore_2+ 0.42144 • LungScore_3- 0.11304 • LungScore_4- 0.83853 • LungScore_5+ 0.27888 • BW_600_800+ 0.95066 • BW_800_1000+ 0.33230 ■ BW_gtl000- 0.87714 • Bline_gt.3- 0.95324 • Moth_Yes)]), where “P(FTS = 1)” denotes the probability of a first treatment success for the cattle patient; and wherein '‘Sex_Steef’ refers to the sex of the cattle, with a value of ‘ 1’ denoting a steer and a value of ‘0’ denoting a heifer; DOF 43-71 refers to the cattle's days on feed, with a value of ‘1 ’ denoting a 43-71 days on feed and a value of ‘O' denoting otherwise; DOF_gl7 l refers to the cattle’s days on feed, with a value of ' 1 ’ denoting greater than 71 days on feed and a value of ‘0’ denoting otherwise; LungScore_2 refers to the lung score, with a value of T denoting a lung score of 2 and a value of '0' denoting otherwise; Lung Score _3 refers to the ultrasound lung score (ULS) as described above, with a value of ‘ 1 ’ denoting a ULS of 3 and a value of ‘0’ denoting otherwise; Lung Score 4 refers to the ultrasound lung score (ULS) as described above, with a value of ‘ 1’ denoting a ULS of 4 and a value of ‘O' denoting otherwise; Lung Score_5 refers to the ultrasound lung score (ULS) as described above, with a value of ‘1’ denoting a ULS of 5 anda value of ‘O' denoting otherwise; BW_600_800 refers to the body weight of the cattle, with a value of ‘1 ’ denoting a body weight between 600 and 799 pounds and a value of ‘0’ denoting otherwise; BW_800_1000 refers to the body weight of the cattle, with a value of ‘ 1 ’ denoting a body weight between 800 and 999 pounds and a value of ‘0’ denoting otherwise; BW gt I ()()() refers to the body weight of the cattle, with a value of ‘ 1’ denoting a body weight greater than 1000 pounds and a value of ‘0’ denoting otherwise; Bline_gt3 refers to the count of B-lines, with a value of ‘ 1’ denoting greater than 3 B-lines and a value of ‘0’ denoting otherwise; and Moth_Yes refers to the presence or absence of a moth sign, with a value of ‘ 1’ denoting the presence of a moth sign in the corresponding cattle ultrasound, and a value of ‘0’ denoting the absence of a moth sign. In some forms, the scoring of more than one variable is combined using a formula to provide a more accurate probability that the mammal will respond to a subsequent treatment after more than one treatment. In some forms, the formula is: P(DNF = 1) = 1 / (1 + exp[-(-2.2997- 0.1033 • Sex_Steer- 0.7109 • DOF 43 71+ 0.1397 • DOF_gt72+ 3.4370 • Bline_gt3+ 1.4459 • Moth_Yes)]), where “P(DNF = 1)” denotes the probability of a chronic DNF prognosis; and wherein: “Sex_Steer” refers to the sex of the cattle, with a value of ‘ 1 ’ denoting a steer and a value of ‘0’ denoting a heifer; DOF_43-71 refers to the cattle’s days on feed, with a value of ‘1 ’ denoting a 43-71 days on feed and a value of ‘0’ denoting otherwise; DOF_gt72 refers to the cattle’s days on feed, with a value of ‘ 1 ’ denoting greater than 72 days on feed and a value of ‘0’ denoting otherwise; Bline_gl3 refers to the count of B-lines, with a value of ' 1’ denoting greater than 3 B-lines and a value of ‘0’ denoting otherwise; and Moth_Yes refers to the presence or absence of a moth sign, with a value of ‘1 ’ denoting the presence of a moth sign in the corresponding cattle ultrasound, and a value of ‘0’ denoting the absence of a moth sign.

[0021] In some forms, the present disclosure provides a method of rapidly diagnosing a cattle patient for respiratory disease using targeted thoracic point-of-care ultrasound (TT-POCUS),the method generally comprises scanning a thoracic region of a lung of the cattle patient with an ultrasound probe and thereby generating an ultrasound image; identifying a count of A- lines and a count of B-lines in the ultrasound image; identify ing a presence or absence of severe lung consolidation; and determining a rapid diagnosis for the cattle patient, wherein: the rapid diagnosis is no respiratory disease when the B-line count is fewer than three and the absence of severe lung consolidation has been identified; the rapid diagnosis is interstitial pneumonia when the B-line count is greater than or equal to three; and the rapid diagnosis is respiratory disease that is not interstitial pneumonia when the presence of severe lung consolidation has been identified. In some forms, the method further comprises determining a presence or absence of one or more abnormal pleural findings. In some forms, the one or more abnormal pleural findings include a moth sign. In some forms, if the count of A-lines is less than three, the rapid diagnosis is one of either interstitial pneumonia or respiratory disease that is not interstitial pneumonia. In some forms, the thoracic region is within intercostal spaces between an 8thand an 1 1thrib on a right side of the cattle patient or subject.

[0022] In some forms, the present disclosure provides a method of determining treatment of a respiratory disease in cattle subject comprising distinguishing between IP and non-IP and treating the cattle appropriately based on the probability determined in a method described herein. In some forms, the cattle subject is diagnosed with interstitial pneumonia. In some forms, the method of treatment comprises administering anti-inflammatory and supportive care, and wherein if the cattle subject or patient is diagnosed with respiratory disease that is not interstitial pneumonia according to the methods described herein, the method of treatment comprises antimicrobial therapy.

[0023] The present disclosure further provides methods of rapidly assessing a cattle patient for respiratory disease using targeted thoracic point-of-care ultrasound (TT-POCUS), wherein the method generally comprises scanning a thoracic region of a lung of the cattle patient withan ultrasound probe and thereby generating an ultrasound image; identifying a count of A- lines and a count of B-lines in the ultrasound image; identify ing a presence or absence of severe lung consolidation; assigning a ultrasound lung score (ULS) to the ultrasound image on the basis of the count of A-lines, the count of B-lines, and the presence or absence and the severity of lung consolidation, wherein the ULS is a numerical score ranging from 1 to 5, further wherein: the ULS score is 1 when the count of B-lines is fewer than three; the ULS score is 2 when the count of B-lines is three or greater and the B-lines have a thickness of less than 7 mm; the ULS score is 3 when the B-lines have a thickness greater than 7 mm; the ULS score is 4 when the B-lines have a thickness greater than 7 mm and the presence of one or more abnormal pleural findings has been determined; and the ULS score is 5 when severe lung consolidation has been determined. In some forms the method further comprises determining a presence or absence of one or more abnormal pleural findings. In some forms, the one or more abnormal pleural findings include a moth sign. In some forms, the thoracic region is within intercostal spaces between an 8thand an 1 1thrib on a right side of the cattle patient. In some forms, the method further comprises a step of rapidly generating a disease diagnosis, wherein:if the ULS score is 1 and the A-line count is three or more, the disease diagnosis is no respiratory disease; if the ULS score is 2, 3 or 4, the disease diagnosis is interstitial pneumonia; and if the ULS score is 5, the disease diagnosis is respiratory disease that is not interstitial pneumonia.

[0024] In some forms of the present disclosure, a method of treatment of the cattle subject is provided, wherein if the cattle patient is diagnosed with interstitial pneumonia according to methods described herein, the method of treatment comprises administering antiinflammatory and supportive care, and wherein if the cattle patient is diagnosed with respiratory disease that is not interstitial pneumonia according to methods described herein, the method of treatment comprises antimicrobial therapy. In some forms, the method furthercomprises a step of a first treatment failure (FTF) prognosis after a first treatment, wherein the cattle patient is displaying one or more clinical signs of respiratory disease, further wherein: the FTF prognosis is a probability that the first treatment will not successfully treat the cattle patient; if the ULS score is 1, 2, or 3, the FTF prognosis is less than 50%; if the ULS score is 4, the FTF prognosis is greater than 50%; and if the ULS score is 5, the FTF prognosis is greater than 65%. In some forms, the method further comprises a step of a did not finish (DNF) prognosis after a first treatment, wherein the cattle patient is displaying one or more clinical signs of respiratory disease, further wherein: the DNF prognosis is a probability that the cattle patient will die or be culled within 60 days after the first treatment; if the ULS score is 1, 2, or 3, the DNF prognosis is less than 35%; if the ULS score is 4, the DNF prognosis is less than 45%; and if the ULS score is 5, the DNF prognosis is greater than 65%.DRAWINGS

[0025] FIG. 1 provides a lateral view of bovine anatomy with respiratory system in evidence. The conventional area of TUS evaluation in an adult bovine (4th to 11th ICS) is outlined in a solid line and the area only possible to be applied TUS in pre-weaned calves is outlined with a dotted line; in pre-weaned calves, it is possible to use TUS from the 1stto the 11th. In contrast. TT-POCUS in accordance with the present disclosure is performed between an 8th and an 11th rib on a right side of the cattle patient.

[0026] FIGS. 2A and 2B provide exemplary7ultrasound images with clearly visible A-lines and B-lines, respectively.

[0027] FIG. 3 a lung scoring system and exemplary7thoracic ultrasound images associated with each lung score.

[0028] FIG. 4A is a bar graph derived from a multivariable model described herein and shows the probability of an interstitial pneumonia (IP) diagnosis as a function of ultrasound lung score, particularly showing that a score of 5 is not associated with IP. Error bars marked by varying subscripts (e.g. a, b) denote significant differences between IP diagnosis probabilities.

[0029] FIG. 4B is a bar graph derived from a multivariable model described herein and shows probability of an IP diagnosis as a function of the B-line count, particularly showing that the probability of an IP diagnosis is highest with a B-hne count greater than five.

[0030] FIG. 4C is a bar graph derived from a multivariable model described herein and shows the probability of an IP diagnosis as a function of the A-line count, particularly showing that the probability of an IP diagnosis is higher when the A-line count is fewer than three.

[0031] FIG. 5A is a bar graph derived from a multivariable model described herein and shows the probability that a test animal did not finish (DNF) the feeding phase based on ultrasound lung score at the time of first treatment in an exemplary study of over 800 cattle. A lung score of 5 is particularly strongly associated with a DNF outcome.

[0032] FIG. 5B is a bar graph derived from a multivariable model described herein and shows the probability' of an animal experiencing a first treatment success (requiring no further treatments, not dying, and not being culled) as a function of ultrasound lung score, particularly showing that the probability of first treatment success declines as lung score increases.

[0033] FIG. 5C is a bar graph derived from a multivariable model described herein and shows the probability of DNF as a function of the presence of a moth sign, where ' 1 ’ denotesthe presence of a moth sign and ‘0’ denotes the absence of a moth sign, particularly showing that the presence of a moth sign is associated with a higher probability7of DNF.

[0034] FIG. 5D is a bar graph derived from a multivariable model described herein and shows the probability of DNF as a function of B-line count, particularly showing that the probability of a DNF outcome is higher when the B-line count is greater than three.

[0035] FIG. 6A shows the distribution of lung lesion scores in the U.S. cattle, particularly showing that a lung score of 0 is extremely rare and that a lung lesion score of 4 is relatively common.

[0036] FIG. 6B is a bar graph showing the probability of IP diagnosis in cattle by sex, showing that the IP diagnosis is slightly more common in steers.

[0037] FIG. 6C is a bar graph showing the probability7of an IP diagnosis in cattle as a function of days spent on feed, particularly showing that once cattle have spent more than forty days on feed, the probability of IP diagnosis becomes very7high.

[0038] FIG. 6D is a bar graph showing the probability7of an interstitial pneumonia (IP) diagnosis as a function of ultrasound lung score, particularly showing that a score of 5 is not associated with IP.

[0039] FIG. 6E is a bar graph comparing the average B-line count in ultrasounds of cattle diagnosed as not having IP versus cattle diagnosed as having IP, particularly showing that an IP diagnosis is associated with a higher B-line count, in a sample of 40 cattle with a standard error of the mean of 0.43.

[0040] FIG. 6F is a bar graph comparing the average A-line count in ultrasounds of cattle diagnosed as not having IP versus cattle diagnosed as having IP, particularly showing that an IP diagnosis is significantly associated with a lower A-line count, in a sample of 40 cattle with a standard error of the mean of 0.69.

[0041] FIG. 7A is a targeted thoracic point-of-care ultrasonography (TT-POCUS) image showing a normal lung observation, including pleural reflection (A-lines), absence of B-lines, and smooth pleura, resulting in an ultrasound lung score (ULS) of 1.

[0042] FIG. 7B is a TT-POCUS image showing merged B-lines, diffuse hyperechoic lung, with an overall ULS of 3.

[0043] FIG. 7C is a TT-POCUS image showing lung consolidation, the absence of A-lines, and an overall ULS of 5.

[0044] FIG. 7D shows raincloud-boxplot displays of the temporal distribution of the days spent on feed at time of chuteside evaluations (POC) of cattle enrolled in a study of 98 cattle. DNF cattle either died or were culled. Recovered cattle were alive at the 60-day postevaluation point. Box-plots represent the upper and lower quartiles, and the line within each box plot represents the median.

[0045] FIG. 8A shows raincloud-boxplot displays of the temporal distribution of the days spent on feed at time of chuteside evaluations (POC) of cattle enrolled in a study of 819 cattle. DNF cattle either died or were culled. Recovered cattle were alive at the 60-day postevaluation point. Box-plots represent the upper and lower quartiles, and the line within each box plot represents the median.

[0046] FIG. 8B shows raincloud-boxplot displays of the temporal distribution of the treatment success outcomes model on the days on feed at time of chuteside evaluations (POC) of cattle enrolled in a study of 819 cattle. “Tx” is short for ’‘treatment.” “First Tx Success” denotes cattle that were alive at the end of the 60-day post-evaluation period and not re-treated. “First Tx Failure” denotes cattle that had to be re-treated or were dead or culled by the end of the 60-day post-evaluation period.

[0047] FIG. 8C shows model-adjusted probabilities of DNF outcomes as a function of ULS score from a study of 819 cattle evaluated with TT-POCUS. The prevalence of DNF in the study was 23%. ULSs that do not share the same subscript (i.e. a, b, c, etc.) display evidence of significant differences (p < 0.05).

[0048] FIG. 8D shows model-adjusted probabilities of first treatment failure as a function of ULS from a study of 819 cattle evaluated with TT-POCUS. The prevalence of first treatment failure (FTF) in the study was 40%. ULSs that do not share the same subscript (i.e. a, b, c, etc.) display evidence of significant differences (p < 0.05).

[0049] FIG. 9 shows raincloud-box plots displaying temporal distribution of 62 cattle studied and grouped by histopathological diagnoses (Histopathology Dx) of IP as a function of days on feed at the time of POCUS. Non-IP indicates other respiratory syndromes without IP. The box plots overlaid within each raincloud show the interquartile range (IQR), with the thick black line representing the median days on feed (DOF) for each group (non-IP median, 28 days; IQR, 16 days; IP median, 50 days; IQR, 67 days). Individual data points are jittered within each group to show the distribution of DOF at the time of chuteside evaluation, with each dot representing one observation.

[0050] FIG. 10 shows histological photomicrographs from an animal diagnosed with IP. Box A shows an H&E stain (scale bar = 200 pm) displaying diffuse alveolar filling by proteinaceous fluid, fibrin, and inflammatory cells. Box B shows an H&E stain (scale bar = 125 pm) indicating the presence of alveolar hyaline membranes (arrowhead) and pneuymocyte type II hypertrophy (*). Box C shows an H&E stain (scale bar = 150 pm) displaying chronic interstitial fibroplasia (f) and bronchiolitis obliterans (bo). Box D shows a transthoracic right lung POCUS image displaying an absence of A-lines, merged B-lines, diffuse hyperechoic lung and ultrasound lung pneumonia. Box E shows an H&E stain (scale bar = 325 pm) displaying bronchial, bronchiolar. and alveolarfilling by hemorrhage, neutrophils intermixed with necrotic debris (n), and fibrin (f). Box F shows an H&E stain (scale bar = 65 pm) displaying fibrinonecrotizing and neutrophilic bronchiolitis and bronchiolectasis. Box G shows an H&E stain (scale bar = 65 pm) displaying neutrophilic bronchiolitis. Box H shows a transthoracic right lung POCUS image featuring lung consolidation, an absence of A-lines, and ULS 5.DETAILED DESCRIPTION

[0051] The following description is merely exemplary in nature and is in no way intended to limit the present teachings, application, of uses. Thus, variations that do not depart from the gist of that which is described are intended to be within the scope of the teachings. Such variations are not to be regarded as a departure from the spirit and scope of the teachings.

[0052] In various forms, described herein are methods of using thoracic ultrasound to make rapid chuteside evaluations of cattle. The methods described herein include, in various forms, approaches for rapid diagnosis that can identify bovine respiratory disease, differentiate interstitial pneumonia from other respiratory diseases such as bronchopneumonia. The methods described herein also include, in various forms, prognosis techniques that can rapidly assess the likelihood of a cattle patient achieving outcomes including first treatment success, first treatment failure, and culling or death within a period after the point of first treatment.

[0053] In various forms, a method of assessing cattle for bovine respiratory' disease using TT- POCUS includes a chuteside evaluation with an ultrasound probe. A method of rapidly diagnosing a cattle patient for respiratory' disease using targeted thoracic point-of-care ultrasound (TT-POCUS) involves several steps. This process is designed for practicality' in a feedyard setting, with each ultrasound examination limited to a 60-second duration toaccommodate operational routines, although other durations envisionable to one of ordinary skill in the art are considered to be within the scope of the present description. Thus, in various forms, each chuteside ultrasound examination can take less than 0.1 minutes, 0.2 minutes, 0.3 minutes, 0.4 minutes, 0.5 minutes, 0.6 minutes, 0.7 minutes, 0.8 minutes, 0.9 minutes, 1.0 minutes, 1.5 minutes, 2.0 minutes, 2.5 minutes, 3.0 minutes, 3.5 minutes, 4.0 minutes, 4.5 minutes, 5.0 minutes, 5.5 minutes, 6.0 minutes, 6.5 minutes, 7.0 minutes, 7.5 minutes, 8.0 minutes, 8.5 minutes, 9.0 minutes, 9.5 minutes, or 10.0 minutes. Alternatively, the examination can take 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 150, 255, 260, 265, 270, 275,280, 285, 290, 295, 300, 305, 310, 315, 320, 325, 330, 335, 340, 345, 350, 355, 360, 365,370, 375, 380, 385, 390, 395, 400, 405, 410, 415, 420, 425, 430, 435, 440, 450, 455, 460,465, 470, 475, 480, 485, 490, 495, 500, 505, 510, 515, 520, 525, 530, 535, 540, 545, 550,555, 560, 565, 570, 575, 580, 585, 590, 595, 600 seconds, or less. In some forms, initially, a targeted thoracic region of a lung of the cattle patient is scanned with an ultrasound probe. In various forms, the probe has a frequency range of 1 MHz to 10 MHz and a specialized 5 cm x 3 cm footprint optimized with a lung preset for imaging over the hair coat, thereby generating an ultrasound image. However, various other ultrasound probe configurations known to one of ordinary skill in the art are within the scope of the present description. In various forms, to enhance probe-to-skin contact, 70% isopropyl alcohol is applied to the cattle over the region to be probed.

[0054] An image is generated from the ultrasound scan. From this image, a count of A-lines and / or a count of B-lines are identified, and in preferred forms, the presence or absence of severe lung consolidation is also determined; these features assist in and contribute to characterizing the underlying lung pathology. FIGS. 2A and 2B show A-lines 10 and B-lines20 for reference. Based on these findings, a rapid preliminary diagnosis, prediction of diagnosis, and / or prognosis for the cattle patient can be determined. In various forms, the rapid preliminary' diagnosis or prediction of diagnosis presumes that the cattle patient has respiratory' disease and seeks then to distinguish between diagnoses finding interstitial pneumonia (IP) and diagnoses of non-IP respiratory' disease such as bronchopneumonia (BP). In some forms, the rapid preliminary diagnosis defaults by finding a probability7of IP of 50%. If the B-line count is fewer than three, then the B-line count does not increase the probability7of an IP diagnosis. Similarly, identification of the absence of severe lung consolidation does not increase the probability of IP; the above findings for B-line count and absence of severe lung consolidation ty pically correspond to a lung surface reflecting air efficiently. In various forms, the rapid diagnosis finds an increase in the probability of IP when the B-line count is greater than or equal to three, indicating increased density within the lung interstitium or alveoli. Conversely, the rapid diagnosis, diagnosis prediction, and / or prognosis finds respiratory disease that is not interstitial pneumonia when the presence of severe lung consolidation has been identified, representing a significant loss of aeration in the affected lung tissue. One non-limiting example of interstitial pneumonia that is not interstitial pneumonia is bronchopneumonia.

[0055] The above-described diagnostic method can be further refined by determining a presence or absence of one or more abnormal pleural findings, which in various forms are irregularities of the lung surface. Such findings can include a "moth sign," noted when the pleural line shows irregularities like pleural thickening and sub-pleural consolidation or indentations, deviating from a smooth, bright line. The irregular, heterogeneous pattern of the pleura indicating sub-pleural lung consolidation, ty pically characterized by hypoechoic or anechoic regions interspersed with echogenic, speckled areas, gives the appearance of fabric eaten by moths. The clinical relevance of moth sign findings suggests chronic, non-resolvingpulmonary disease and often correlates with poor prognosis or treatment failure. Therefore, the presence or absence of the moth sign may guide clinical decisions toward euthanasia or salvage. Determining the presence of one or more abnormal pleural findings, in various forms, increases the probability7of a diagnosis of IP. Additionally, if the count of A-lines is less than three, this suggests a reduction in normally aerated lung visible at the pleural surface, in various forms due to the presence of B-lines or consolidation, which in various forms also increases the probability7of IP.

[0056] In various forms, the preferred thoracic region for ultrasound probe scanning is within intercostal spaces (ICS) between an 8th and an 11th rib on a right side of the cattle patient, focusing on the right caudo-dorsal lung lobe. In various forms, the probe is placed in a shortaxis orientation and a "fanning" technique is employed to maximize the visualized area. In general, fanning refers to the controlled, angulated movement of the ultrasound transducer in a fixed location, pivoting along its long or short axis to systematically sweep through adjacent tissue planes without translating the probe’s footprint across the surface. Such a method permits the visualization of structures in different planes and obtain a comprehensive view of anatomy or pathology7in a specific region. The fanning technique generally comprises the steps of the operator holding the probe steady at one location followed by tilting or rocking the probe in a pivoting motion. Such a method allows the ultrasound beam to interrogate deeper or adjacent tissues sequentially without moving the probe from its place. With specific reference to cattle, fanning in thoracic ultrasonography helps assess the extent of pulmonary7consolidation, pleural effusion, or fibrinous adhesions. Additionally, fanning can be employed for liver or cardiac imaging. Fanning also improves visualization of borders or lesions that may he off-center from the initial scan plane, enhances lesion detection, facilitates three-dimensional understanding of anatomy, and minimizes missing small or eccentric lesions.

[0057] In various forms, rapidly assessing a cattle patient for respiratory disease using TT- POCUS preferably incorporates the use of an ultrasound lung score (ULS), a numerical score used to grade the health of lung tissue according to at least the metrics described above. In various forms, the ULS is an integer-based scoring system that ranges from a minimum to a maximum value. In various forms, the ULS ranges from 1 to 5, although one of ordinary' skill in the art can imagine an almost infinite number of practical variations on the range and specificity7of the ULS. Therefore, any ULS range with any level of granularity7is considered to be within the scope of the present description so long as it satisfies the function of grading ultrasound images on lung health according to parameters including A-line count, B-line count, pleural abnormality7presence, and lung consolidation. A ULS score is assigned to each ultrasound image, reflecting the severity of observed lung abnormalities. In various forms, the minimum ULS score is assigned to ultrasound images that represent ideally healthy7lung tissue, while the maximum ULS score is assigned to ultrasound images that represent highly consolidated lung tissue. In various forms, the minimum ULS score, such as a ULS score of 1, is assigned when the count of B-lines is fewer than three, indicating minimal to no significant ultrasonographic lung pathology. In various forms, a ULS score of 2 is assigned when the count of B-lines is three or greater and the B-lines have a thickness of less than 7 mm (thin B-lines), suggesting mild to moderate interstitial pathology. In various forms, if the B-lines have a thickness greater than 7 mm (merged B-lines), indicating more significant fluid or cellular accumulation, the ULS score is 3. In various forms, a ULS score of 4 is assigned when the B-lines have a thickness greater than 7 mm (multiple wide / merged B- lines) and the presence of one or more abnormal pleural findings, such as the above-described moth sign, has been determined, indicating more extensive disease with pleural involvement.In various forms, a maximum ULS score, such as a ULS score of 5, is assigned when severe lung consolidation has been determined. An ultrasound image assigned the maximum ULSscore can also be accompanied by pleural thickening / irregularities and effusion, representing the most severe ultrasonographic lung pathology observed.

[0058] In various forms, the above-described ULS-based assessment method can further include and / or enable a step of rapidly generating a disease diagnosis, prediction of diagnosis, and / or prognosis. If the ULS score is 1 and the A-line count is three or more, indicative of normal aeration, then no respiratory disease is identified in the scanned region, and therefore the rapid preliminary diagnosis finds a probability of IP below 50%. If the ULS score is 2, 3, or 4. reflecting varying degrees of B-line presence and pleural abnormalities, the disease diagnosis finds a higher than 50% probability of a diagnosis of or prediction of an interstitial pneumonia diagnosis. If the ULS score is 5, characterized by lung consolidation, the disease diagnosis or prediction of diagnosis finds and / or predicts respiratory disease that is not interstitial pneumonia, highlighting a distinct and severe pathological state.

[0059] Thus, as described above, the A-line count, the B-line count, the presence or absence of severe lung consolidation, the presence or absence of one or more abnormal pleural findings, and the ULS all affect whether the rapid preliminary diagnosis finds a probability of a diagnosis of interstitial pneumonia that is higher or lower than 50%.

[0060] In various forms, the probability of a diagnosis of interstitial pneumonia is lower than 50% if the ULS score is 1;. In various forms, other variables indicative of IP can each increase the probability of a diagnosis of IP. In some forms, the increased probability is 2, 3, 4, 5, 6, 7. 8, 9, 10, 11, 12, or 13% or more. Thus, if the ULS score is 1 and the A-line count is fewer than three, absent other considerations, the probability of a diagnosis of IP increases. In some forms, the increased probability is 2. 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13% or more. In some forms, the increase brings the total probability of a diagnosis of IP to more than 50%. If the ULS score is 1 and the presence of one or more abnormal pleural findings is identified, absent other considerations, the probability of a diagnosis of IP is greater than 50%. If theULS score is 1, the A-line count is fewer than three, and the presence of one or more abnormal pleural findings is identified, absent other considerations, the probability of a diagnosis of IP is 57% or more. In various forms, if the cattle patient has spent between 43 and 71 days on feed, the probability of a diagnosis of IP increases by about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13% or more. Thus, if the ULS score is 1, the cattle patient has been on feed between 43 and 71 days, and the presence of one or more abnormal pleural findings is identified, absent other considerations, the probability of a diagnosis of IP increases. In some forms, the probability is increased to 57% or more. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13% or more. If the ULS score is 1, the cattle patient has been on feed between 43 and 71 days, the A-line count is fewer than three, and the presence of one or more abnormal pleural findings is identified, absent other considerations, the probability of a diagnosis of IP further increases. In some forms, the probability is increased to 64% or more. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13% or more.

[0061] In various forms, if the ULS score is 2, the probability of a diagnosis of IP increases. In some forms the probability increases to at least 57%. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13% or more. If the ULS score is 2 and the A-line count is fewer than three, absent other considerations, the probability of a diagnosis of IP increases. In some forms, the increase is to at least 64%. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13% or more. In some forms, if the ULS score is 2 and the presence of one or more abnormal pleural findings is identified, absent other considerations, the probability of a diagnosis of IP is increased to at least 64%. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8. 9, 10, 11, 12, or 13% or more. If the ULS score is 2, the A-line count is fewer than three, and the presence of one or more abnormal pleural findings is identified, absent other considerations, the probability of adiagnosis of IP increases to at least 71%. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13% or more. Thus, in various forms, if the ULS score is 2, the cattle patient has spent between 43 and 71 days on feed, and the A-line count is fewer than three, the probability of a diagnosis of IP increases to at least 71%. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13% or more. In various forms, if the ULS score is 2, the cattle patient has spent between 43 and 71 days on feed, and the presence of one or more abnormal pleural findings is identified, the probability of a diagnosis of IP increases to at least 71%. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, or 13% or more. In various forms, if the ULS score is 2, the cattle patient has spent between 43 and 71 days on feed, the A-line count is fewer than three, and the presence of one or more abnormal pleural findings is identified, the probability of a diagnosis of IP is increased to at least 78%. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13% or more.

[0062] In various forms, if the ULS score is 3, absent other considerations, the probability of a diagnosis of IP is increased. In some forms, the increased probability is 2, 3, 4, 5, 6, 7. 8, 9,10, 11, 12, or 13% or more. In some forms, the probability is at least 64%. If the ULS score is 3 and the A-line count is fewer than three, absent other considerations, the probability of a diagnosis of IP increases. In some forms, the probability is increased to at least71%. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. 12, or 13% or more. In various forms, if the cattle patient has spent between 43 and 71 days on feed, the probability of a diagnosis of IP increases. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, or 13% or more. Thus, in various forms, if the ULS score is 3, the cattle patient has spent between 43 and 71 days on feed, and the A-line count is fewer than three, the probability of a diagnosis of IP is increased. In some forms, the probability is at least 78%. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13% or more.

[0063] In various forms, if the ULS score is 4, absent other considerations, the probability of a diagnosis of IP is increased. In some forms, the probability is at least 71%. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13% or more. If the ULS score is 4 and the A-line count is fewer than three, absent other considerations, the probability7of a diagnosis of IP is increased. In some forms, the probability7is at least 78%. In some forms, the increased probability7is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13% or more. In various forms, if the cattle patient has spent between 43 and 71 days on feed, the probability7of a diagnosis of IP increases. Thus, in various forms, if the ULS score is 3, the cattle patient has spent between 43 and 71 days on feed, and the A-line count is fewer than three, the probability of a diagnosis of IP is at least 85%. In some forms, the increased probability7is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13% or more.

[0064] In various forms, if the ULS score is 5 and no other factors are considered, the probability of a diagnosis of interstitial pneumonia is 10% or less. Put another way, there is a 90% or higher probability that the diagnosis is not IP. In various forms, other variables more indicative of IP can each increase the probability7of a diagnosis of IP. In some forms, the increased probability is 2, 3, 4, 5. 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, or 18% or more. Thus, if the ULS score is 5 and the A-line count is fewer than three, absent other considerations, the probability of a diagnosis of IP increases. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, or 18% or more. In some forms, the probability is increased to at least 17%. If the ULS score is 5 and the presence of one or more abnormal pleural findings is identified, absent other considerations, the probability of a diagnosis of IP is increased. In some forms, the increase is to 17% or more. In some forms, the increased probability7is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, or 18% or more. If the ULS score is 5, the A-line count is fewer than three, and the presence of one or more abnormal pleural findings is identified, absent other considerations, theprobability of a diagnosis of IP is increased. In some forms, the increase is to 24% or more. In various forms, if the cattle patient has spent between 43 and 71 days on feed, the probability of a diagnosis of IP increases. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, or 18% or more. Thus, if the ULS score is 5, the cattle patient has been on feed between 43 and 71 days, and the presence of one or more abnormal pleural findings is identified, absent other considerations, the probability of a diagnosis of IP is increased to at least 24%. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, or 18% or more. If the ULS score is 5, the cattle patient has been on feed between 43 and 71 days, the A-line count is fewer than three, and the presence of one or more abnormal pleural findings is identified, absent other considerations, the probability of a diagnosis of IP is increased. In some forms, the probably is increased to 31% or more. In some forms, the increased probability is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, or 18% or more.

[0065] As can be appreciated, the above models for adjusting the probability of a diagnosis of IP as a function of multiple variables assumes a standard adjustment for each variable that is indicative of IP. However, adjustments in the value for each variable in question that are envisioned by one of ordinary skill in the art are within the scope of the present description, as are variations in the baseline probability of an IP diagnosis associated with each ULS value. Thus, in various exemplary forms, the standard adjustment for each variable that is indicative of IP can be independently selected from any value between 1% and 25%, for example 1.0%, 1.1%, 1.2%, 1.3%, 1.4%, 1.5%, 1.6%, 1.7%, 1.8%. 1.9%, 2.0%, 2.1%, 2.2%, 2.3%, 2.4%, 2.5%, 2.6%, 2.7%, 2.8%, 2.9%, 3.0%, 3.1%, 3.2%, 3.3%, 3.4%, 3.5%, 3.6%,3.7%, 3.8%, 3.9%, 4.0%, 4.1%, 4.2%, 4.3%, 4.4%, 4.5%, 4.6%, 4.7%, 4.8%, 4.9%, 5.0%,5.1%, 5.2%, 5.3%, 5.4%, 5.5%, 5.6%, 5.7%, 5.8%, 5.9%, 6.0%, 6.1%, 6.2%, 6.3%, 6.4%,6.5%, 6.6%, 6.7%, 6.8%, 6.9%, 7.0%, 7.1%, 7.2%, 7.3%, 7.4%, 7.5%, 7.6%, 7.7%, 7.8%,7.9%, 8.0%, 8.1%, 8.2%, 8.3%, 8.4%, 8.5%, 8.6%, 8.7%, 8.8%, 8.9%, 9.0%, 9.1%, 9.2%,9.3%, 9.4%, 9.5%. 9.6%, 9.7%, 9.8%, 9.9%, 10.0%. 10.1%, 10.2%, 10.3%, 10.4%, 10.5%,10.6%, 10.7%, 10.8%, 10.9%, 11.0%, 11.1%, 11.2%, 11.3%, 11.4%, 11.5%, 11.6%, 11.7%,11.8%, 11.9%, 12.0%, 12.1%, 12.2%, 12.3%, 12.4%, 12.5%, 12.6%, 12.7%, 12.8%, 12.9%,13.0%, 13.1%, 13.2%, 13.3%, 13.4%, 13.5%, 13.6%, 13.7%, 13.8%, 13.9%, 14.0%, 14.1%,14.2%, 14.3%, 14.4%, 14.5%, 14.6%, 14.7%, 14.8%, 14.9%, 15.0%, 15.1%, 15.2%, 15.3%,15.4%, 15.5%, 15.6%, 15.7%, 15.8%, 15.9%, 16.0%, 16.1%, 16.2%, 16.3%, 16.4%, 16.5%,16.6%, 16.7%, 16.8%, 16.9%, 17.0%, 17.1%, 17.2%, 17.3%, 17.4%, 17.5%, 17.6%, 17.7%,17.8%, 17.9%, 18.0%, 18.1%, 18.2%, 18.3%, 18.4%, 18.5%, 18.6%, 18.7%, 18.8%, 18.9%,19.0%, 19.1%, 19.2%, 19.3%, 19.4%, 19.5%, 19.6%, 19.7%, 19.8%, 19.9%, 20.0%, 20.1%,20.2%, 20.3%, 20.4%, 20.5%, 20.6%, 20.7%, 20.8%, 20.9%, 21.0%, 21.1%, 21.2%, 21.3%,21.4%, 21.5%, 21.6%, 21.7%, 21.8%, 21.9%, 22.0%, 22.1%, 22.2%, 22.3%, 22.4%, 22.5%,22.6%, 22.7%, 22.8%, 22.9%, 23.0%, 23.1%, 23.2%, 23.3%, 23.4%, 23.5%, 23.6%, 23.7%,23.8%, 23.9%, 24.0%, 24.1 %, 24.2%, 24.3%, 24.4%, 24.5%, 24.6%, 24.7%, 24.8%, 24.9%, or 25.0%.

[0066] Alternatively, the calculation of a probability of diagnosis of IP can consider a sex of the cattle patient, whether the cattle patient has been on feed for between 43 and 71 days, whether the cattle patient has been on feed for more than 71 days, whether the ULS is 2, 3, 4 or 5, whether the B-line count is between 3 and 5, and whether the A-line count is fewer than three. In various alternative forms, the probability of an IP diagnosis is the inverse of one plus e raised to a linear combination of the sex of the cattle patient, whether the cattle patient has been on feed for between 43 and 71 days, whether the cattle patient has been on feed for more than 71 days, whether the ULS is 2, 3, 4 or 5, whether the B-line count is between 3 and 5, and whether the A-line count is fewer than three. Example 12 below provides a specificexample of the use of the exponential described above to calculate the probability of a diagnosis of IP.

[0067] Cattle showing obvious clinical signs of respiratory disease such as labored breathing and runny mucous are typically provided initial minimally invasive treatment to aid the cattle in a natural recovery. Such treatment is referred to herein as a first treatment. In various forms, the ULS assessment method can be extended to provide prognostic information for a cattle patient displaying one or more clinical signs of respiratory disease, subsequent to the first treatment. The prognostic information includes a first treatment failure (FTF) prognosis, which is defined as the probability that the first treatment (as administered by feedyard personnel) will not successfully treat the cattle patient within a post-treatment period. A successful treatment in the context of the current description is one that does not reasonably engender further follow-up treatments or the culling or death of the cattle patient within the post-treatment period. In various forms, the post-treatment period is sufficiently long to determine the likely medium-to-long-term course of the respiratory disease. For example, in various forms, the post-treatment period is sixty days. However, one of ordinary skill in the art can envision a range of post-treatment periods that are considered to be within the scope of the present description. Without limitation, in various forms the post-treatment period can be 5 days, 10, days, 15 days, 20 days, 25 days, 30 days, 35 days, 40 days, 45 days, 50 days, 55 days, 60 days, 65 days, 70 days, 75 days, 80 days, 85 days, or 90 days.

[0068] In various forms including the embodiment described above in which the ULS score is a range of 1-5, the specific ULS score can provide a rapid prognosis for the cattle patient. If the ULS score is 1, 2, or 3, the model-adjusted FTF prognosis can be less than 50%, or between 30% and 50%, including 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, and 50%, or between approximately 35% and 41% including 35, 36, 37, 38, 39, 40, and 41%. In various forms, if the ULS score is 4, the FTF prognosis can be greaterthan 50%, or 50% to 70%, including 50, 51, 52, 53. 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, and 70%, or between 55% and 65%. In various forms, if the ULS score is 5, the FTF prognosis can be greater than 65%, or greater than 70%.

[0069] Similarly, in various forms a did not finish (DNF) prognosis can be determined, reflecting the likelihood of mortality or culling. The DNF prognosis is the probability that the cattle patient will die or be culled within 60 days after the first treatment. In various forms, if the ULS score is 1, 2, or 3, the DNF prognosis is less than 35%, or between 10% and 30%, including 10, 11, 12, 13, 14, 15, 16, 17, 18, 19. 20. 21. 22. 23. 24. 25. 26, 27, 28, 29, and 30%. or between 18% and 28%. In various forms, if the ULS score is 4, the DNF prognosis is less than 45%. or between 30% and 40% including 31, 32, 33, 34, 35, 36, 37. 38. 39. and 40%. In various forms, if the ULS score is 5, the DNF prognosis is markedly elevated and thus greater than 65%. or greater than 70% including at least 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90. 91. 92. 93. 94, 95, 96, 97, 98, 99%, and more.

[0070] In some forms, absent consideration of the ULS score, other variables such as the B- line count and / or the A-line count can also affect the probability' of a diagnosis of IP. In various forms, if the B-line count is greater than five, absent other considerations, the probability' of a diagnosis of IP is at least 70%. In various forms, if the B-line count is greater than five, absent other considerations, the probability of a diagnosis of IP is 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, or 90%. Similarly, in various forms, if the B-line count is less than three, absent other considerations, the probability of a diagnosis of IP is at least 70%. In various forms, if the B- line count is less than three, absent other considerations, the probability' of a diagnosis of IP is at least 70%, 71 %, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81 %, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, or 90%. In various forms, if the B-line count is between 3 and 5, the probability of a diagnosis of IP is at least 25%. In various forms, if the B-line countis between 3 and 5, the probability of a diagnosis of IP is at least 25%, 26%, 27%, 28%, 29%,30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45% or more. In various forms, if the A-line count is fewer than three, absent other considerations, the probability7of a diagnosis of IP is at least 70%. In various forms, if the A-line count is fewer than three, absent other considerations, the probability' of a diagnosis of IP is greater than 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, or 90%.

[0071] In various forms, if the cattle patient has had three or more treatments, factors such as the B-line count and the presence of pleural abnormalities can affect the probability of a DNF prognosis. In various forms, if the cattle patient has had three or more treatments and the 13- line count is greater than three, absent other considerations, the probability of a DNF prognosis is at least 75%. In various forms, if the cattle patient has had three or more treatments and the B-line count is greater than three, absent other considerations, the probability of a DNF prognosis is 75%. 76%. 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, or 95%. Similarly, in various forms, if the cattle patient has had three or more treatments and the B-line count is less than three, absent other considerations, the probability of a DNF prognosis is at least 30%. In various forms, if the cattle patient has had three or more treatments and the B-line count is less than three, absent other considerations, the probability of a DNF prognosis is 30%, 31%, 32%, 33%, 34%, 35%, 36%. 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, or 50%. In various forms, if the cattle patient has had three or more treatments and the presence of one or more pleural abnormalities such as a moth sign is confirmed, absent other considerations, the probability of a DNF prognosis is at least 70%. In various forms, if the cattle patient has had three or more treatments and the presence of one or more pleural abnormalities such as a moth sign is confirmed, absent other considerations, theprobability of a DNF prognosis is 70%, 71%. 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%. 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, or 90%. Similarly, in various forms, if the cattle patient has had three or more treatments and the presence of one or more pleural abnormalities such as a moth sign is not confirmed, absent other considerations, the probability7of a DNF prognosis is at least 40%. In various forms, if the cattle patient has had three or more treatments and the presence of one or more pleural abnormalities such as a moth sign is not confirmed, absent other considerations, the probability7of a DNF prognosis is 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, or 60%.

[0072] In various forms, factors such as the B-line count and the presence of pleural abnormalities can affect the probability of an FTF prognosis. In various forms, if the B-line count is less than three, absent other considerations, the probability of an FTF prognosis is at least 20%. In various forms, if the B-line count is less than three, absent other considerations, the probability of an FTF prognosis is 20%, 21%, 22%, 23%. 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%. 32%. 33%. 34%. 35%. 36%, 37%, 38%, 39%, or 40%. Similarly, in various forms, if the B-line count is three or more, absent other considerations, the probability of an FTF prognosis is at least 50%. In various forms, if the B-line count is three or more, absent other considerations, the probability of an FTF prognosis is 50%, 51%. 52%, 53%, 54%, 55%, 56%. 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, or 70%. In various forms, if the presence of one or more pleural abnormalities such as a moth sign is confirmed, absent other considerations, the probability7of an FTF prognosis is at least 45%. In various forms, if the presence of one or more pleural abnormalities such as a moth sign is confirmed, absent other considerations, the probability of an FTF prognosis is 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, or 65%. Similarly, in various forms, if the presence of one or more pleuralabnormalities such as a moth sign is not confirmed, absent other considerations, the probability of an FTF prognosis is at least 20%. In various forms, if the presence of one or more pleural abnormalities such as a moth sign is not confirmed, absent other considerations, the probability’ of an FTF prognosis is 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, or 40%.

[0073] In various forms, methods of treatment for the cattle patient can be guided by the rapid diagnoses, diagnosis predictions, and / or prognosis predictions obtained through TT- POCUS as described above. If the cattle patient is diagnosed with interstitial pneumonia, based on, in various forms, the initial diagnostic method (B-line count > three) or the ULS- based diagnostic method, the method of treatment generally comprises administering antiinflammatory’ and supportive care. Alternatively, if the cattle patient is diagnosed with respiratory disease that is not interstitial pneumonia, indicated by the presence of severe lung consolidation, or in various forms the maximum ULS score, the method of treatment comprises antimicrobial therapy.EXAMPLE 1

[0074] A specific subset of cases was evaluated for diagnostic accuracy of the thoracic ultrasound methodology. These cases were evaluated chuteside, then when mortality occurred, they were necropsied and histopathological analysis performed on pulmonary' tissue to determine the final pulmonary diagnosis. Histopathological diagnosis is considered the gold standard for interstitial pneumonia; therefore, results and evaluations are based on a known and true case outcome. In this portion of the study 40 head were evaluated for all outcomes. Several factors including ultrasound lung score, B-line count, and A-Line count were significantly associated with the likelihood of the presence of interstitial pneumonia.Combining the results of one or more of these individual tests can result in improved diagnostic ability influencing the ability' to strategically select the most relevant therapeutic options. FIGS. 4A-4C illustrates the differences in expected probability of interstitial pneumonia in each case based on the results of individual testing. FIG. 4A is a bar graph showing the probability' of an interstitial pneumonia (IP) diagnosis as a function of ultrasound lung score, particularly showing that a score of 5 is not associated with IP. Specifically, a score of 5 has a 12% likelihood of being connected to an IP outcome (p < 0.05). FIG. 4B is a bar graph showing probability' of an IP diagnosis as a function of the B-line count, particularly showing that the probability of an IP diagnosis is highest with a B-line count greater than five. Specifically, a B-line count greater than 5 is 86% likely to be IP (p < 0.05). A B-line count of 0-2 was not different from 3-5 and > 5 (p > 0.05), emphasizing the noise that merged B-lines can make. FIG. 4C is a bar graph showing probability of an IP diagnosis as a function of the A-line count, particularly showing that the probability of an IP diagnosis is higher when the A-line count is fewer than three. An A-line count of fewer than three is 83% likely to have an IP outcome (p > 0.05).

[0075] This methodology provides a significant improvement over any other available diagnostic test in the ability to diagnose interstitial pneumonia. Differentiating interstitial pneumonia from bronchopneumonia is critical as each different pathological condition requires different treatment and management considerations. Combining the results of these individual tests can result in improved diagnostic ability influencing the ability to strategically select the most relevant therapeutic options.

[0076] Prognostic information available at the time of first treatment for respiratory' disease provides valuable information to the animal health provider. Specifically, cattle with a poor prognosis may be managed or treated differently and cattle identified with chronic disease at the time of first diagnosis may indicate that further work is needed to improve fielddiagnostic methods to identify animals sooner in the disease process. Multiple methods have been used to evaluate cattle at the time of first treatment and most have focused on identifying if the animal is truly diseased: the current approach is novel as the focus is on the likelihood of subsequent outcomes (retreatment or death / culling) which directly influence the health management plan for the animal.

[0077] A pilot study was conducted utilizing over 800 head of animals at the time of first treatment. The cross-sectional study used thoracic ultrasound at the time of treatment, then followed cattle outcomes to determine potential associations of measured variables and the likelihood of retreatment or mortality / culling. Multiple variables were collected and ultrasound lung score (along with cattle demographic variables) was associated with the likelihood of an animal not finishing the feeding phase (DNF) after the initial treatment (FIG. 5 A). FIG. 5A shows the probability of an animal not finishing the feeding phase (DNF) based on ultrasound lung score at the time of first treatment in over 800 feedyard cattle.

[0078] The likelihood of first treatment success (or probability of animals requiring no further treatments, not dying, and not being culled) was also assessed. The ultrasound lung score (FIG. 5B) was also associated with the probability of an animal becoming a first treatment success. FIG. 5B shows the probability of an animal being a first treatment success (requiring no further treatment, not dying, not being culled) based on ultrasound lung score at the time of first treatment in over 800 feedyard cattle.

[0079] Thoracic ultrasound combined with cattle demographic data provided valuable prognostic information on feedyard cattle at the time of first treatment for respirators' disease. Bovine respiratory disease is the most frequent disease syndrome in feedyard cattle and it has been estimated approximately 15% of all cattle in feedyards are treated for this syndrome.Improved prognostic ability' allows appropriate resource allocation to individual animals requiring more intensive care thereby improving animal welfare, antimicrobial stewardship,and economic sustainability. This system for utilizing thoracic ultrasound provided important prognostic information and illustrated more potential than previously evaluated systems.EXAMPLE 2

[0080] Cattle previously treated multiple times represent a different population than cattle at the first treatment for respiratory disease. Understanding prognosis in this class of cattle is critical to make decisions relative to animal welfare and antimicrobial use. For cattle that are deemed unlikely to recover from the event they may need to be removed from the production system and placed in a different management environment. These types of decisions are made daily in many animals on different operations and currently there is no method to accurately prognosticate for chronically diseased animals. Thoracic ultrasound represents an opportunity to provide valuable information to cattle health care providers.

[0081] A pilot study was done on 98 animals with at least 3 previous treatments each. The cross-sectional study used thoracic ultrasound at the time of treatment, then followed cattle outcomes to determine potential associations of measured variables and the likelihood of mortality or culling. Several variables were specifically associated with death or culling including B-line count >3, B-Line Area, ultrasound lung score and the presence of moth sign. FIGS. 5C and 5D illustrate a large difference in the likelihood of not finishing the feeding phase (DNF) based on the presence of moth sign (FIG. 5C) or a B-line count greater than 3 (FIG. 5D). FIG. 5C shows the probability of DNF as a function of the presence of a moth sign, where ‘ I’ denotes the presence of a moth sign and ‘0’ denotes the absence of a moth sign, particularly showing that the presence of a moth sign is associated with a higher probability of DNF. FIG. 5D shows the probability of DNF as a function of B-line count, particularly showing that the probability of a DNF outcome is higher when the B-line count is greater than three

[0082] FIG. 5C shows the difference in the probability of not finishing the feeding phase (DNF) based on the presence of moth sign (FIG. 5C) or a B-line count > 3 (FIG. 5D) in 98 animals evaluated after at least 3 prior treatments. These results illustrate a large and meaningful difference in the likelihood of cattle not finishing the feeding phase based on these specific thoracic ultrasound procedures when evaluated after three previous treatments. This prognostic system based on thoracic ultrasound is a significant improvement over the status quo which involves subjective assessments by individuals in the operation. Information provided from this methodology can be very valuable for making culling decisions on individual operations. Combining data from each of the individual measurements into a single diagnostic algorithm would be important for final deployment in the field, the information collected improves diagnostic differentiation (interstitial vs. bronchopneumonia) and prognostic value in two classes of cattle (at the time of first treatment and chronic disease). The methodology addresses a specific need in the cattle health industry and no other available technology approaches the diagnostic accuracy of the proposed methodology.

[0083] A study was conducted on cattle sourced from a feed yard operation. Animals initially identified by feed yard personnel for respiratory disease treatment between a 19 day period (N=495) were eligible for inclusion. A specific subgroup of these animals (n=40), which underwent point-of-care ultrasound (POCUS) examination at chute-side and subsequently deceased dunng the same period formed the cohort for detailed analysis. Interstitial Pneumonia (IP) status was determined for these deceased animals.

[0084] At the time of POCUS examination, a standardized set of thoracic ultrasound variables was collected. Assessments of the pleural line included the documentation of Moth Sign, the presence of effusion, and the measured distance from the pleural line to B-lines.Lung parenchyma characteristics were evaluated by recording Integrated Density, B-LineArea, B-line count, A-Line Count, a POCUS Lung Score, and the minimum, mean, maximum, and modal gray values from the ultrasound images.

[0085] In addition to POCUS data, animal-specific information was recorded. This included animal body weight, the interval between POCUS examination and death, the number of days on feed, sex, and breed.

[0086] The distribution of POCUS lung scores within the study population was characterized. The probability of an IP diagnosis was evaluated in relation to animal sex, the duration of days on feed, and the POCUS lung score. Furthermore, mean B-line counts and mean A-line counts derived from POCUS examinations were compared between cattle diagnosed with IP and those not diagnosed with IP. FIG. 6A shows the distribution of ultrasound lung scores (ULSs) in the US cattle population, particularly indicating that a score of 4 is relatively common and that a score of zero is very uncommon. As FIG. 6B shows, the cattle population shows a higher proportion of steers than heifers. FIG. 6C is a bar graph showing the probability of an IP diagnosis in cattle as a function of days spent on feed, particularly showing that once cattle have spent more than forty days on feed, the probability of IP diagnosis becomes very high. FIG. 6D shows the probability of an IP diagnosis being made as a function of ULS, with a score of 5 contraindicating an IP diagnosis. FIG. 6E is a bar graph comparing the average B-line count in ultrasounds of cattle diagnosed as not having IP versus cattle diagnosed as having IP, particularly showing that an IP diagnosis is associated with a higher B-line count, in a sample of 40 cattle with a standard error of the mean of 0.43. FIG. 6F is a bar graph comparing the average A-line count in ultrasounds of cattle diagnosed as not having IP versus cattle diagnosed as having IP, particularly showing that an IP diagnosis is significantly associated with a lower A-line count, in a sample of 40 cattle with a standard error of the mean of 0.69. Further results are provided in Table 1 below, which gives regression data (Pseudo- R2McFadden = 0.4474, Akaike Information Criterion (AIC) =48.14, showing that the regression model has substantial explanatory' power and fits the data well.Table 1 — Regression Data on IP OutcomesEXAMPLE 3

[0087] Materials and Methods:

[0088] Experimental Design & Enrollment Criteria: This experiment was designed as a cross-sectional observational study with individual cattle as the experimental unit (n= 98). Feedyard cattle respiratory morbidities were evaluated at the time of 3rd or greater BRD treatment by the use of various POC approaches by a trained veterinarian (LF). Data collection took place for a 5 month period at one feedyard in the high plains region of the United States.

[0089] Cattle enrolled in the study were evaluated at the time of treatment for chronic respiratory disease. Chronic respiratory disease was defined as feedyard cattle that had been treated previously for any respiratory disease and the cumulative respiratory disease treatment events at time of POC was three or greater. A total of 98 animals were enrolled in the study based on this criterion, with 51% (51 / 98) 3rd BRD pull; 43% (42 / 98) 4th BRD pull; and 5% (5 / 98) 5 th BRD pull. Variables collected were: targeted thoracic POC ultrasound (TT-POCUS) at the level of the right caudodorsal lung lobe, pulse oximetry (SPO2 and pulserate per minute), pulmonary auscultation on the right cranioventral lung field and right caudodorsal lung field, and individual animal demographics (sex, body weight, and days on feed). Feedyard personnel were blinded to the POC evaluation, and any management decision regarding treatment, re-treatment and culling for the cattle observed in this cross-sectional study at time of treatment and after were made solely by feedyard personnel using existing protocols in place. Furthermore, animals enrolled in this study were not submitted to a different operational protocol, including treatment regimen, cattle monitoring, and any other management decision.

[0090] TT-POCUS: "In this study, enrolled commercial feedyard cattle underwent a TT- POCUS method, with cattle restrained in a hydraulic squeeze chute. Due to the fast-paced environment of the commercial feedyard, each ultrasound examination was limited to 60 seconds. The evaluation focused on the right caudodorsal lung lobe, with the probe positioned between the 8th and 11th ribs. The ultrasound unit had a frequency range of 1 to 10 MHz and featured a specialized point-of-care probe with a 5 cm x 3 cm footprint, the preset utilized for these evaluations was the "Lung" preset, which is optimized for lung assessments. To prepare the scanning area, 70% isopropyl alcohol was applied to the animal in the area to be scanned, and no hair trimming or shaving was utilized.

[0091] The TT-POCUS procedure was accomplished with probe placement between the ribs (short-axis orientation), starting at the 1 1th intercostal space. The probe was moved from the proximal aspect of the intercostal space, just ventral from the transverse processes, sliding the probe ventrally to visualize the liver, diaphragm, and caudal portion of the lung. This process was repeated from the 11th to 8th intercostal space. In addition to sliding the probe, a "fanning" technique was employed, adjusting the probe angle relative to the skin to enhance the visualization of pulmonary tissue between ribs.

[0092] Real-time data collection from the TT-POCUS evaluation was conducted by a trained veterinarian (LF), examples of TT-POCUS images are displayed on FIGS. 7A-7C. The collected variables included ultrasound lung scores (ULS), B-line count, A-line count, and the presence or absence of pleural effusion and pleural abnormalities (moth sign). A-line is an ultrasound artifact that is characterized by a hyperechoic horizontal line that is defined to be reverberation of the pleural line, this artifact is observed in abundance on normally aerated lung. B-line is also an artifact that presents vertically in a beam like projection, often starting at the pleura line going across the lung field, 3 or more B-lines in a lung field is generally associated with lung injury and poor aeration. The ULS were classified as follows: Score 1 - presence of fewer than 3 thin B-lines (indicative of healthy lung); Score 2 - presence of 3 or more thin B-lines; Score 3 - merged B-lines; Score 4 - multiple wide / merged B-lines with abnormal pleural findings (moth sign); Score 5 - consolidated lung. B-line count was determined by counting the number of B-lines on the lung field with the most apparent abnormalities; A-lines were counted using the same criteria as B-line count, with the only difference being A-lines are reverberation of the pleural line (horizontal artifacts), and B-line are vertical beams that goes across the lung field. Moth sign and pleural effusion were determined by having presence or absence of these abnormalities during scanning of 8th to 11th intercostal spaces. Post-hoc variables included measures of image brightness and B-line width, specifically pixel intensity density, average gray value, and B-line area (cm2). These measurements were obtained from a single frame of the 60-second video clip using ImageJ 1.54b software, focusing on the frame with the most noticeable tissue abnormalities to assess injury within the caudodorsal lung region.

[0093] Pulse Oximetry’: Cattle restrained in the squeeze chute were evaluated with a pulse oximeter unit equipped with a transflectance sensor to collect blood oxygen saturation (SPO2) and pulse per minute (PPM) data. After cleaning debris and cerumen, the sensor wasplaced midway between the ear ribs by a trained veterinarian (LF), within 15 seconds, the transfl ectance signal would stabilize to allow for data annotation. The external ear was chosen as the area for evaluation due to the safe and quick access to a bare skin.

[0094] Pulmonary7auscultation score: Feedyard cattle were restrained in a squeeze chute, and auscultation scores for examinations lasting <30 seconds for each of two fields (caudodorsal and cranioventral) were recorded. Stethoscope placement for these evaluations took place on the right caudodorsal and cranioventral lung fields. In this study, lung auscultation for both fields and all cattle were carried by the same trained veterinarian (LF). Specifically, the cranioventral pulmonary' assessment score took place between the 4th and 5th rib on the right side of the animal, caudal to the radio-ulnar joint (CVPAS). The other auscultation field, the caudodorsal assessment, took place between the 8th and 9th rib on the right side of the animal at the level of the proximal third of the intercostal space (CDPAS). Pulmonary auscultation score (PAS), was defined in a modified score system based on previous published work, where scores were condensed from the range 1 to 10, to 1 to 5 , where: 1 - normal lung sounds; 2 - presence of mild crackles / rales; 3 - presence of moderate crackles / rales ; 4 - presence of severe crackles / rales; 5 - presence of severe diffuse crackles / rales.

[0095] The primary outcome of this study was DNF, which was determined 60 days postenrollment (POC day). The three possible outcomes at 60-day were: culled, died, or lived. The DNF outcome was a combination of cattle that failed to recover from respiratory disease in the 60-day interval (culled + died). Treatment interventions or culling decisions were solely determined based on feedlot personnel assessment, which were blinded to the POC evaluations.

[0096] Statistical Analysis: Microsoft Excel (Microsoft Excel, Microsoft 365) and Rstudio(RStudio, version 2023.12.1) were utilized for data management, processing and analyses.For descriptive analysis, a table comparing the outcome variables was constructed using the“dplyr” and “tidyr” packages in RStudio. The variables body weight, days on feed, B-line count, and A-line count were categorized prior to data analysis to avoid assumption of linear relationships with the primary outcome (DNF). Body weight categories (BW) were: < 600, 600 to 799, 800 to 1000, > 1000 lb (< 272, 272 to 361, 362 to 453, and > 453 kg), and days on feed (DOF) were categorized similar to previous work into early (0 to 42), mid (43 to 71), and late (> 71) DOF. The B-line count was categorized into less-than or greater-than-or- equal-to 3 B-lines similar to previous work. Finally, A-line count categories were categorized as < 3 and > 3 similarly to B-line categories. Other categorical variables were: sex, pleural effusion, moth sign, ULS, and PAS. Variables analyzed as continuous were: Pixel intensity density, average gray value, B-line area, SPO2, and PPM.

[0097] The data analysis employed a generalized linear mixed-effects model implemented through the ‘glmer’ function from RStudio’s ‘lme4’ package. To appropriately address the binomial response variable, the ‘logit’ function in RStudio was applied, the degree of significance was set at p < 0.05. The primary outcome of interest was the 60-day status, specifically assessing the association of POC parameters with the odds of DNF. Cattle lot was used as a random effect in all models to account for a lack of independence in cattle managed and housed in similar fashions. Model development involved using the 'scope’ parameter to ensure that fixed variables (Sex and Days on Feed) were consistently included across model iterations. Stepwise selection was performed in a backward direction using the 'stepAlC’ function from the 'MASS’ package in RStudio, iteratively removing the least significant variables based on the lowest AIC values. Multicollinearity (VIF) was also assessed when final model was determined using the “car” package in RStudio. Variables were considered to have high collinearity when a coefficient was estimated to be > 2.5. The log-odds from the logistic regression model was then converted to probabilities using the package ‘emmeans’ in Rstudio.

[0098] Results

[0099] Descriptive Statistics: At the time of third or greater respiratory treatment event, 98 cattle were POC evaluated. Pull count breakdown in this population was: 51% (51 / 98) 3rd BRD pull; 43% (42 / 98) 4th BRD pull; and 5% (5 / 98) 5th BRD pull. Only 72 of the 98 cattle were evaluated with pulse oximetry' and pulmonary' auscultation due to a protocol deviation where these parameters were added to the study after 26 cattle were already enrolled. There were 61 heifers (62%), and 37 steers (38%) enrolled with descriptive statistics listed in Table 2. There were 19 cases (19%) in the early feeding phase (0 to 42 DOF) category, 39 cases (40%) in the mid feeding phase (43 to 71 DOF) category, and 40 cases (41%) in the late feeding phase ( >71 DOF) category. Seventy-five percent of the 98 cases were within the 600 to 799 lbs (272 to 361 kg) and 800 to 1000 lbs (362 to 453 kg) categories. Most cases DNF (n=63, 64%) within the 60-day post-evaluation period and 35 cases were deemed recovered (36%).

[0100] The distribution of cases enrolled by DOF at time of treatment grouped by60-day outcome indicate that DNF cases occurred dispersed throughout the feeding period (median of 66 DOF and interquartile range (IQR) 19-1 13 DOF). In contrast, recovered cases displays an apparent narrower distribution with a median of 62 DOF and IQR 36-88 DOF (FIG. 7D).

[0101] Descriptive statistics for POC measures are listed in Table 2. Assessment by TT-POCUS revealed most cases (73 / 98. 74%) were ULS 3.4. or 5. Most cases also had > 3 A-lines (61, 62%) and B-lines (78, 79.5%). Blood oxygen saturation (%) averaged 86.5% ± 1.4% for cattle that recovered and DNF cases averaged 82.2% ± 1.5%. Pulse (PPM) averaged 78 ± 4 PPM for cattle that recovered and 72 ± 3 PPM for DNF. Cranioventral PAS indicated that 10% of cattle placed in scores 1 and 2 had a DNF outcome, whereas, 56% of cattle with scores 3 and 4 DNF; 86% of cattle with cranioventral PAS of 5 had an outcome of DNF.Caudodorsal PAS indicated that 40% of cattle placed in scores 1 and 2 DNF, while, 73% of cattle with scores 3 and 4 DNF; and 87% of cattle with caudodorsal PAS of 5 DNF.

[0102] Table 2 shows descriptive statistics of cattle according to their demographics (sex, weight, and days on feed categories), real-time chute-side evaluations (ultrasound lung score, blood oxygen saturation, pulse, auscultation scores, A-line count categories, B-line count categories, pleura effusion, moth sign; and post-hoc evaluations (Flline area, gray value, intensity density). Superscript 1 denotes cattle that were alive 60-days post-evaluation; superscript 2 denotes cattle that were culled or dead within 60-day postevaluation; * denotes variables collected at chute-side real-time, f denotes moth sign and is noted when the pleural line shows irregularities like pleural thickening and indentations; $ denotes Post-hoc measurements were performed by using the ImageJ 1.54. Measurements were taken from a still image, where the frame of choice was the frame with most ultrasonographic artifacts and abnormalities. § denotes that only cattle enrolled in the fall (n= 72) were evaluated with pulse oximetry and pulmonary auscultations.Table 2 — Collected Cattle Data

[0103] The multivariate logistic regression identified only three variables associated with DNF: B-line count category, moth sign, and B-line area (p = 0.02, p = 0.03, and p = 0.04, respectively). Although, included in the model as fixed variables, sex and DOF were not significantly associated with DNF (p = 0.75 and p = 0.20, respectively). Variables not significantly associated with DNF outcome and therefore not selected by the study’s multivariate logistic regression were: BW, ULS, A-line category, presence of pleural effusion, pixel intensity density, average gray value, SPO2, pulse rate, and both PAS scores. (p > 0.05). No variables were removed from the final model due to collinearity.

[0104] Tukey-adj usted probabilities using the fitted model showed the probability of DNF was lower when B-line < 3 (34% ± 22%) compared to when B-line > 3 (84% ± 5%). Presence of moth sign displayed higher probability of DNF (78% ± 13%) compared to when moth sign was absent (43% ± 15 %). The post-hoc continuous variable B-line area showed an average of 18.7 ± 1.2 cm2for cattle that did not finish compared 9.6 ± 1.2 cm2for cattle that recovered.

[0105] Discussion

[0106] Chronic respiratory disease represents a significant challenge in cattle management including differentiating between cattle likely to recover cattle likely to be unresponsive to further treatment. This study evaluated multiple POC modalities in chronically diseased cattle to determine potential associations with the likelihood of DNF. While several POC metrics were not associated with clinical outcomes, some of the TT- POCUS variables could provide insight to improve prognostic accuracy. Accurate prognosis classification is crucial for improving animal welfare and economic sustainability.

[0107] An accurate description of, or industry-wide acceptance of, a chronically diseased respiratory case-definition is lacking. Some reports define chronic cases based on extent of pulmonary lesion as identified by ultrasonography; while other reports define chronic cases by the use of a computer-aided auscultation device, and others define chronic cases as cattle with repeated unsuccessful treatment interventions. In this study, chronic BRD disease was defined as three BRD treatments or greater. A majority (64%) of outcomes for cattle in this study population were classified as DNF, meaning enrolled cattle had either died or were culled within the 60-day evaluation period. These numbers are similar to previous work: in 2020. Blakebrough-Hall et al observed 58% mortality in cattle deemed chronic (> 3 treatments). The cunent study population was representative of chronically diseased feedyard cattle.

[0108] The results of the multivariate logistic regression analysis identified key variables significantly associated with the DNF outcome. In this multivariate logistic regression, variables were selected based on the best model fit, using Akaike Information Criterion (AIC) as the selection criterion. During the stepwise selection process, if one variable had a stronger association with the outcome, another variable with a weaker or redundant association might have been dropped from the model due to its lack of additional predictive value. This occurs because AIC -based prioritizes the optimal model, retaining only variables that significantly contribute to model fit while eliminating those with overlapping or weak effects. Among all the variables tested, the three variables associated with the model were derived from TT-POCUS: B-line category, presence of moth sign, and B-line area. The presence of more than three B-lines was associated higher likelihood of DNF compared to cattle with fewer than 3 B-lines and the results illustrated a good distinction from the baseline overall DNF risk. Results agreed with several reports illustrating the importance of B-line and pleural observation in thoracic ultrasound evaluation in both human and veterinary medicine. Studies in human medicine have similarly demonstrated that increased B-line counts are strongly correlated with severe lung diseases and poorer prognoses, which supports the findings in cattle. Utilizing a POC B-line count could provide information on prognosis in chronically diseased cattle.

[0109] The presence of moth sign was another significant indicator of poor prognosis, with a 78% probability of DNF when the moth sign was present, compared to 43% when it was absent. Moth sign is often associated with advanced pulmonary pathology, including consolidation, sub-pleural fibrosis, and severe interstitial changes, which are difficult to reverse, particularly in chronically affected cases. Finally, the post-hoc variable B- line area revealed a significant difference between catle that did not finish (DNF) and those that recovered. Merged B-lines (coalescent or fused), could be indicative of extensive lungpathology' thereby contributing to poor outcomes. Interestingly, the post-hoc B-line area did not display collinearity with B-line count categories, suggesting that B-line area can provide additional context, perhaps accounting for B-line merging. Both B-line count and B-line area provided separate pieces of information to the final multivariable model; however, B-line count remains the more feasible and easily interpretable metric for field application.

[0110] Pulse oximetry parameters, including blood oxygen saturation (SP02) and pulse rate, were not associated with the DNF outcome despite being relevant indicators of respiratory function in clinical settings. The lack of association in this study could also be attributed to the site utilized to place the sensor (external ear). In 1999, Coghe et al. evaluated the accuracy of different sites for sensor placement in cattle, where he observed that nasal septum, tail and genital mucosa in females provided stable and strong signal. In 2019, Baruck and associates also evaluated SP02 to assess bovine respiratory disease progression; where it was observed that SPO2 could provide an objective measure of lung consolidation; their creative apparatus allowed for sensor placement intranasally to reach nasal folds. Another potential limitation is that pigmented hide or skin has been often reported to be a contributing factor for inaccuracies, since majority of the cattle investigated were Angus-cross cattle with predominately black hides. The type of measurement may not be the only factor influencing the relationship between SP02 and outcomes as previous work that measured SP02 from arterial blood also displayed no relationship between SP02 and the level of lung lesions at necropsy. Findings from this work did not illustrate a relationship between SPO2 or pulse and predicting accurate prognosis 309 chronically ill cattle.

[0111] Pulmonary auscultation scores (PAS) for both the cranioventral and caudodorsal lung areas did not demonstrate a significant association with DNF outcomes in the multivariate model. Buczinski et al. (2014), tested the ability of pulmonary auscultation to detect lung consolidation and observed sensitivity of < 6% and 97% specificity. Previousresearch has also indicated that auscultation is valuable for detecting bovine respiratory- disease, although, it may lack the sensitivity needed to predict long-term outcomes in chronic cases. One reason PAS may not have been included in the final multivariable model is the inclusion of findings from TT-POCUS captured similar information as PAS related to associations with DNF.

[0112] Necropsies were not performed to verify whether mortalities were explicitly associated with BRD, representing a limitation of this study. Additionally, only variables that were available at the time of chute-side evaluation in this study were included, restricting interpretation of animal weights solely to enrollment values without context of arrival weights or average daily gain. Future research could incorporate such data to potentially strengthen predictive accuracy and contextual relevance.

[0113] This study evaluated several potential POC technologies and only TT-POCUS findings were significantly associated with DNF. While other traditional metrics such as pulmonary auscultation were not significantly associated with outcomes in this study, the strong associations observed with TT-POCUS variables suggest that these features should be prioritized in clinical evaluations. The ability7to gather this valuable information at the time of disease treatment to accurately predict DNF outcomes could significantly enhance decision-making in feedyard management; potentially improving the overall health and productivity of the herd. Further research is w arranted to refine these techniques and validate their applicability across diverse feedyard operations.Example 4

[0114] Ultrasonography serves as a reliable objective method for diagnosing lung damage, and the TUS findings are well correlated with post-mortem examinations. Using TUS is achievable in a farm setting, with calf-side evaluations being feasible and consistentlyaccurate following proper training. The diagnostic accuracy of TUS for BRD in pre-weaned dairy calves has been reported to have a sensitivity of 79.4% and a specificity7of 93.9% for one specific study.

[0115] Cattle TUS techniques have evolved significantly over time, with key advancements in probe selection, scanning protocols, and standardization of anatomical landmarks. Typically, a 3.5 to 5 MHz linear or convex probe is used, allowing for adequate penetration for imaging lung structures while maintaining sufficient resolution. Several studies have highlighted the use of intercostal spaces from the 4th to 11th ribs in adult cattle, and pre- weaning calves can be evaluated with TUS starting on the first ICS, which offer the best likelihood for detecting lung and pleural abnormalities. These spaces are examined from dorsal to ventral regions to ensure comprehensive coverage of the lung fields.

[0116] Probe orientation is critical; it is typically held parallel to the ribs, with the ultrasound beam perpendicular to the pleural surface to maximize the detection of abnormalities such as consolidation or pleural fluid. Buczinski et al. (2015) and Flock (2004) elaborated on the importance of visualizing the pleural line, which provides a key diagnostic marker for lung pathologies, including consolidations and pleuritis. Additionally, ultrasonographic methods have been adapted for rapid chute-side assessments, as demonstrated in studies where quick evaluations of lung consolidation were performed in high-nsk feedyard cattle, 14 dairy calves, and veal calves.

[0117] One key advancement in ultrasonographic techniques has been the refinement of a pleural space assessment. Techniques such as "fanning", where the probe angle is dynamically altered to capture different planes of lung tissue, are commonly used to identify lesions between the ribs. Additionally, coupling agents like isopropyl alcohol are often used in field conditions to optimize probe-skin contact, especially when time constraints prevent extensive preparation, like hair shaving or clipping.

[0118] The use of TUS is limited due to logistics at the time and location of examination. Young calves are ty pically easier to restrain and manipulate the animal position to allow for a thorough examination. On the other hand, TUS in adult cattle is more challenging. The challenge wi th ultrasound in adult bovine starts with restraining, that you need at least a head-gate to restrain the animal, and at times a squeeze chute (which may cover the area of interest). In addition, adult animals are harder to manipulate due to weight and strength, which at times might not allow for a full lung evaluation.

[0119] Time constraints are a major limiting factor for the application of thoracic ultrasonography. Published reports have shown that a full TUS evaluation can take from 7 to 45 min. While the TUS procedure itself typically takes less than 7 min, the overall time required is often extended by animal handling, which depends on factors such as age, available personnel, and facilities. Improving farm infrastructure to facilitate safe and efficient handling not only enhances welfare but also promotes the safety of veterinarians and workers, ensuring the effective application of TUS. Hence, there is a need to develop new methods to strategically evaluate specific areas of the lung and also specific populations at risk of bovine respiratory disease. Studies such as those by Timsit et al. (2019) highlight the efficiency of chute-side ultrasonography when used strategically, where rapid region-targeted assessments can be completed within minutes (fourth to sixth mid to ventral intercostal spaces), particularly in high-throughput feedyard operations. Another study from Adams and Buczinski (2016) evaluated if a 2 min fixed-time thoracic ultrasound assessment would yield valuable information about dairy’ calves, and the results showed that a single timepoint 2 min evaluation was associated with health outcomes in these heifers. Another strategic TUS method reported by Pardon (2019) used a simplified scan starting from the caudodorsal tip of the lung to moving cranially in a single motion diagonally up to the fourth intercostal space;this strategic method reduces evaluation time and also provides valuable information about pulmonary health.

[0120] The balance between speed and accuracy is critical, and in most cases, an extensive and focused examination can be accomplished swiftly without compromising diagnostic accuracy. The advancements in portable ultrasonography devices and improved operator training have contributed to reducing the time needed for comprehensive examinations, making ultrasound a practical tool for both clinical and field settings.

[0121] Respiratory Disease Diagnosis: Normal lung tissue is challenging to visualize via ultrasonography due to air content. Ultrasound waves do not propagate through air; hence, the image of a properly aerated lung is usually dark (low echogenicity), with several pleural line reverberations (artifacts), called A-lines. A-lines appear as echogenic bands parallel to the pleural surface and are consistently observed in a healthy lung. In normal lungs, parietal and visceral pleura often appear as a single, smooth hyperechoic line between the lung tissue and thoracic wall muscles. Lung movement, synchronous with respiration, is normal, and a lack of movement, especially in the pleural line, can indicate pathology (pneumothorax).

[0122] As respiratory disease develops, there are findings that indicate that the lung and pleura are undergoing injury. The most commonly reported finding is parenchymal consolidation; consolidation refers to non-aerated lung tissue and can manifest in various forms. Hepatization, characterized by lung tissue taking on a liver-like echogenicity, represents one form of consolidation. However, hepatization does not always indicate the most severe lung injury, as it can occur in both active and inactive bronchopneumonia. Inactive bronchopneumonia may present with smaller, localized areas of hepatization. In contrast, fluid alveolograms are typically associated with active bacterial bronchopneumonia, which indicates ongoing inflammation and is generally considered to be more severe.Therefore, the severity of lung injury should be assessed not only by the presence of consolidation but also by the specific characteristics, such as the presence of fluid alveolograms, which denote active disease processes. Consolidation can appear as wedges from the pleural line, nodules, or can affect the entire lung lobe. Another common abnormal finding is pleural effusion, which is easy to identify as it shows fluid accumulation between the parietal pleura and visceral pleura. One artifact usually observed in respiratory disease is the B-line, also called the "comet tail". This artifact is a vertical beam that often starts at the pleural line and prolongates across the image field. B-lines are usually perceived as a normal artifact if they are rare (2 or less per field). A greater number and width of B-lines are associated with more severe injury. Studies consistently show- that TUS can detect diseases such as bronchopneumonia, which often manifests as areas of lung consolidation. For example, Buczinski et al. (2014) demonstrated that ultrasound identified lung lesions in 29% of cattle that appeared healthy based on the clinical signs. Furthermore, consolidation depths of more than 3 cm were commonly associated with bronchopneumonia, particularly in cattle suffering from Mannheimia haemolytica-induced infections. In addition, bronchopneumonia findings are often found with more severe or developed injury of the cranioventral lobe.

[0123] The pleura can also present abnormalities. Most common is pleural thickening associated with pleuritis. Another pleural abnormality is called "moth sign"; this abnormality shows an irregular pleural line, with indentations (moth eaten), associated with sub-pleural consolidation. These possible TUS findings or combinations can support respiratory disease detection and prognosis. The use of ultrasound to detect pleural effusion, another common manifestation of BRD, has also been widely documented. Studies by Tharwat et al. (2011), Babkine and Blond (2009), and Masset et al. (2022) confirm the sensitivity of ultrasound for detecting fluid accumulation in the pleural cavity, a feature that is not easily identified using traditional auscultation methods. In more severe cases ofpleuropneumonia, ultrasonography has been instrumental in visualizing both pleural fluid and consolidation that extends deep into the lung tissue. This is a crucial advancement, as these conditions are typically difficult to diagnose without invasive procedures.

[0124] The accurate diagnosis of bovine respiratory disease (BRD) is critical to the effective management and health of cattle, particularly in feedyard operations where BRD is one of the most prevalent and economically significant diseases. The early and precise identification of BRD allows for a timely intervention, reducing the risk of disease progression, relapse, and mortality. Accurate diagnosis not only improves animal welfare by preventing chronic illness but also optimizes treatment protocols, reducing the overuse of antimicrobials and enhancing antimicrobial stewardship. Additionally, studies like those of Buczinski et al. (2014) have highlighted that ultrasonography can reduce the reliance on empirical treatments by offering a clear picture of the severity of lung involvement. Overall, accurate BRD diagnosis is vital for improving herd health, productivity', and the sustainability of livestock operations.

[0125] The comparative accuracy of ultrasonography versus other diagnostic methods has been a key focus in the literature. For instance, studies by Scott et al. (2013) and Buczinski et al. (2014) indicate that ultrasonography significantly outperforms auscultation and clinical scoring for identifying both subclinical and advanced lung pathology. Auscultation, while a cornerstone of veterinary physical examination, has been shown to miss substantial pulmonary abnormalities, particularly in early disease stages. Furthermore, Buczinski et al. (2015) revealed that ultrasonography offers a higher diagnostic specificity than rectal temperature or respiratory scoring alone, emphasizing its role in confirming suspected BRD cases.

[0126] Respiratory Disease Prognosis: One of the most promising aspects of thoracic ultrasonography in cattle is its ability to predict outcomes in cattle with respiratorydiseases. Numerous studies have highlighted TUS prognostic utility, particularly in identifying cattle that are likely to relapse or experience poor growth performance after treatment. Timsit et al. (2019) showed that cattle with greater lung consolidation depths (>5 cm) at the first diagnosis of bronchopneumonia had a significantly higher risk of relapse and lower average daily gain (ADG) compared to cattle with less severe lung lesions. This highlights the potential for ultrasonography to guide not only diagnostic decisions but also treatment plans by identifying animals that may require more aggressive or prolonged treatment.

[0127] Rademacher et al. (2014) demonstrated that TUS findings can predict mortality risk with significant accuracy. In these studies, large areas of lung consolidation were associated with poor outcomes, including death or culling. Cattle with consolidations exceeding 5 cm were more likely to die or be culled, emphasizing the importance of early detection and the potential to alter treatment protocols based on ultrasound findings.

[0128] Furthermore, TUS has been shown to be a valuable tool for monitoring treatment efficacy. Serial assessments of lung consolidations during the treatment period provide real-time feedback on whether lesions are resolving or worsening. A study by Wolfger et al. (2015) demonstrated that ultrasound can track the progression or regression of lung lesions, offering an objective measure of treatment success. This capability is particularly useful in field settings, where traditional follow-up methods may be limited by logistical constraints. The ability to monitor animals through non-invasive imaging allows veterinarians to make informed decisions about treatment interventions. This not only optimizes animal health outcomes but also contributes to antimicrobial stewardship by reducing the overuse of antibiotics.

[0129] Thoracic Ultrasonography Training: The literature reviewed emphasizes the importance of proper training for veterinarians and technicians performing thoracicultrasonography in cattle, noting that while the technique can be highly accurate, operator experience plays a crucial role in ensuring diagnostic consistency. Buczinski et al. (2013) highlight that with adequate training, even novice operators can perform thoracic ultrasonography with consistency and accuracy in detecting lung consolidations and pleural pathologies in calves. Basic instruction in probe handling and recognition of key anatomical landmarks can allow for less experienced individuals to perform thoracic ultrasound evaluations. Similarly, Ollivett et al. (2011), Scott (2013), Ollivet and Buczinski (2016), and Pardon (2019) stressed that thoracic ultrasound is not only highly accessible but can be performed efficiently in field conditions given proper training. The training focuses on understanding the placement of the probe in intercostal spaces and interpreting common findings like pleural effusions and lung consolidation, which are critical for diagnosing bovine respiratory disease (BRD). These studies collectively highlight the necessity of training to maximize the diagnostic potential of TUS, making it a viable and effective tool for diagnosing and managing BRD in cattle.Example 5

[0130] Experimental Design and Enrollment Criteria: This research was designed as a cross-sectional observational study, with individual feedyard cattle serving as the experimental units. The study aimed to evaluate pulmonary health at the time of first treatment for bovine respiratory disease (BRD) using various point-of-care (POC) diagnostic tools. Data collection occurred from summer to fall of 2023 at a commercial cattle operation located in the high plains of the United States and was performed by a trained veterinarian (LF).

[0131] Enrolled cattle were selected based on their first treatment for respiratory disease. Animals were selected from home pens to be treated and were treated by feedyardpersonnel. None of the POC evaluations or data were shared with feedyard personnel at the time of treatment to avoid influencing their management decisions.

[0132] Demographic data, including sex, breed, bodyweight (BW), and days on feed (DOF), were recorded for each animal at time of treatment. The study focused on identifying associations between POC diagnostic parameters and 60-day post-enrollment outcomes: first treatment failure (FTF) and did not finish (DNF). First treatment failure refers to an animal that was re-treated, was culled, or died before the 60-day post-enrollment marker. The criteria for any treatment interv ention (1st or other) was decided by the feedyard's herd health personnel. The study investigators were not engaged in this and had no impact on their decision regarding the drug of choice or decision to treat an animal. DNF refers to animals that did not complete the feeding phase due to mortality or culling. Cattle that were deemed finished (decided by the commercial operation) and were sent to the abattoir before the end of the 60-day evaluation period were excluded from the analysis.

[0133] Chute-side point-of-care evaluation parameters were collected by the use of pulse oximetry technology, which is capable of measuring blood oxygen saturation (SP02) and pulse per minute (PPM); lung auscultation targeted two thoracic locations on the right lung only (cranioventral and caudo-dorsal); and the last was targeted thoracic ultrasound (TT- POCUS) at the caudo-dorsal level. This evaluation was also carried only on the right lung. Pulse oximetry and pulmonary auscultation were evaluated on a subset of 443 cattle due to a protocol deviation where these parameters were late to be added to the study, and were added after 376 cattle were enrolled already.

[0134] Pulse Oximetry: Pulse oximetry was performed by a trained veterinarian(LF) on a subset of cattle (n = 443), using a pulse oximeter unit equipped with a transfl ectance sensor. The sensor was placed on the external ear (between the cartilage ribs) after cleaning to remove debris, and data were recorded once the signal stabilized, which tookaround 15 s. The measurements included blood oxygen saturation (SPO2) and pulse per minute (PPM).

[0135] Pulmonary Auscultation Score: Pulmonary auscultation was conducted on the same subset of cattle (n = 443), with evaluations performed for less than 30 s per animal. The stethoscope was placed on the right caudo-dorsal lung lobe and the cranio-ventral lung field. Specifically, the cranio-ventral assessment was conducted between the 4th and 5th ribs caudal to the olecranon (elbow), while the caudo-dorsal assessment took place on the 8th intercostal space (ICS). Pulmonary auscultation scores (PASs) were assigned on a scale from 1 to 5, based on the presence and severity of abnormal lung sounds, following a modified scoring system derived from the work of DeDonder et al. (2010). Score was defined as follows: 1 — normal lung sounds; 2 — presence of mild crackles / rales; 3 — presence of moderate crackles / rales; 4 — presence of severe crackles / rales; 5 — presence of severe diffuse crackles / rales.

[0136] Targeted Thoracic Point-of-Care Ultrasound: A targeted thoracic ultrasonography (TT-POCUS) procedure was performed on all enrolled cattle (n = 819) while they were restrained in a hydraulic squeeze chute. The ultrasound device of choice had a frequency range of 1 MHz to 10 MHz and was equipped with a specialized POC probe featuring a 5 cm x 3 cm footprint, with an optimized preset (lung preset) for lung imaging and usage over hair coat, not requiring shaving or trimming. Isopropyl alcohol 70% was utilized to enhance probe-to-skin contact. Each ultrasound examination was limited to a 60 s duration to accommodate the feedyard operation routine. The ultrasound area of interest was in the thoracic region correspondent to the right caudodorsal lung lobe. The ultrasound probe was placed in a short-axis orientation in the ICS between the 8th and 11th ribs on the right side to scan all areas of interest. The "Tanning” technique (movement of the probe in a side-to-side fashion) was applied to maximize the area scanned and enhance the visualization of lung tissue.

[0137] Ultrasound lung scores (ULSs) were assigned based on the observed lung abnormalities, and were categorized as follows: score 1 — fewer than 3 thin (<7 mm) B-lines; score 2 — 3 or more thin (<7 mm) B-lines; score 3 — merged (>7 mm) B-lines; score 4 — multiple wide / merged (>7 mm) B-lines with abnormal pleural findings (e.g., moth sign); score 5 — consolidated lung with pleural thickening / irregularities and effusion. In addition, the B-line count category (0 to 2 and >3), A-line count category' (0 to 2 and >3), and the presence of pleural abnormalities were recorded (moth sign). Post hoc analysis included measurement of the B-line area (cm2 ) using ImageJ 1.54g software from a still frame from the 60 s clip, where the image of choice was the frame with the most pulmonary abnormalities observed. This post hoc measurement was carried on a sub-sample of 710 cases out of the 819 total collected for this study due to availability to saved ultrasound files.

[0138] Statistical Analysis: Data management and statistical analyses were conducted using Microsoft Excel and RStudio 12.1g software. Descriptive statistics were summarized using the "dplyr" and "tidyr" packages in RStudio. The variables, including bodyweight, days-on-feed, B-line count, and A-line count, were categorized for analysis. The BW was categorized as <272, 272 to 361, 362 to 453, and >453 kg. Days-on-feed (DOF) were categorized based on recent published data. Feeding phases were epidemiologically categorized as early -phase (0 to 42 days), mid-phase (43 to 71 days), and late-phase (>71 days) DOF based on the incidence of respiratory disease for each feeding phase. B-line count was categorized as <3, and >3, based on established thresholds indicative of pulmonary injury’. The A-line count was categorized as <3 and >3; the rationale behind this cutoff was of an exploratory nature. The other categorical variables included sex (steers and heifers), ULS(1-5), PAS (1-5), and moth sign (yes or no). Continuous data included SPO2, pulse rate, andB-line area (cm2).

[0139] Generalized linear mixed-effects models were implemented using the"glmer" function from the "lme4" package in RStudio, with a binomial response variable (logit function). The model included fixed effects of sex, BW and DOF, and random effects of cattle lot. Stepwise model selection was conducted using the "stepAIC" function from the "MASS" package, with variables removed based on the least significance and model fitness using AIC values. Multicollinearity was assessed using variance inflation factors (VIFs), with a VIF > 2.5 considered indicative of high collinearity. The significance level was set at p < 0.05.

[0140] Two outcomes of interest, DNF (cull / dead) and FTF (re-treat / cull / dead), were tested to measure their association with POC evaluation parameters data. Each model was tested separately, and model-adjusted probabilities were calculated based on the final model for each outcome of interest. Following model estimation, the log-odds were transformed into probabilities to facilitate interpretation. Transformation was performed using the "emmeans" package in RStudio, enabling the computation of probabilities for each dependent variable included in the model.

[0141] Results

[0142] Descriptive Statistics - DNF Model: At the time of the first respiratory treatment event, 819 cattle were POC evaluated. Of those. 474 were heifers (58%) and 345 were steers (42%) (Table 3). Overall. 70% of cases were in the early feeding phase (0 to 42 DOF; n = 573) category, 13% of cases were in the mid-feeding phase (43 to 71 DOF; n = 104) category, and 17% cases were in the late feeding phase (>71 DOF; n = 142) category.Seventy-five percent of cases were within the 272 to 361 kg (n = 392) and 362 to 453 kg (n =224) weight categories. Within the 60-day post-evaluation period, most cases were classifiedas recovered after the first treatment (77%; n = 628) and 23% (n = 191) cases were culled or died, accounting for the inherent basal prevalence of DNF in this studied population.Table 3 -- Descriptive Statistics

[0143] In Table 3 above,1denotes animals that were alive for the 60-day postevaluation period;2denotes animals that were culled or dead within the 60-day postevaluation period. * denotes variables collected at chute-side real time (n = 819).f Moth sign is noted when the pleural line shows irregularities like pleural thickening and sub-pleural consolidation (indentations). J denotes post hoc measurement (n = 710) was performed using ImageJ 1.54. Measurements were taken from a still image, where the frame of choice was the frame with most ultrasonographic artifacts and abnormalities. § denotes that only animals enrolled in the fall (n = 443) were evaluated with pulse oximetry and pulmonary auscultations.

[0144] The distribution of cases enrolled by DOF at the time of first treatment grouped by treatment success (60-day outcome) indicates that DNF cases occurred later on the feeding phase and w ere dispersed throughout the feeding period (median of 48 DOF and interquartile range (IQR) 91 DOF). In contrast, the recovered cases display an apparent narrower distribution, with a median of 21 DOF and IQR of 33 DOF (FIG. 8A).

[0145] Assessment by TT-POCUS revealed that most cases (82.4%; n = 675 / 819) were classified as ULS 1, 2, or 3. The majority of the cases that displayed 0-2 B-line count (482, 88%) recovered after the first treatment. Most cases also had >3 A-lines (459, 56%), had no effusion (664, 81%), and did not present moth signs (569, 69%). The B-line area average was 19.7 ± 18.2 cm2 for cattle that DNF and 16.11 ± 13.1 cm2 for cattle that recovered. Blood oxygen saturation averaged 83.1% ± 9.4% for cattle that DNF and 85.7% ± 8.5% for cattle that recovered. The pulse averaged 75.9 ± 25 per minute for cattle that DNF and 74.5 ± 22 PPM for cattle that recovered. Cranioventral PAS indicated that 92% of cattle given scores 1. 2, and 3 recovered, whereas 24.2% of cattle with scores 4 and 5 did not finish(DNF) treatment. Nevertheless, only 35% of cattle that presented a cranioventral PAS of 5did not finish. When caudo-dorsal PAS was considered, 9.5% of cattle received scores of 1,2, and 3 DNF, while 50% of cattle FTS with PAS 5.

[0146] First Treatment Failure Model: At the time of the first respiratory' treatment event, 819 cattle were POC evaluated, although only 443 cattle were evaluated with pulse oximetry' and pulmonary' auscultation due to a protocol deviation where these parameters were added to the study after 376 cattle were already enrolled. There were 474 heifers (58%) and 345 steers (42%) enrolled with the descriptive statistics listed in Table 4 below. There were 573 cases (70%) in the early feeding phase (0 to 42 DOF) category', 104 cases (13%) in the mid feeding phase (43 to 71 DOF) category, and 142 cases (17%) in the late feeding phase (>71 DOF) category. Seventy-five percent of the 819 cases were within the272 to 361 kg and 362 to 453 kg categories. Most cases obtained success after the first treatment (n = 492, 60%) within the 60-day post-evaluation period and 327 cases exhibited first treatment failure (40%, inherent basal prevalence of FTF in this studied population).Table 4 - Descriptive Statistics, First Treatment Outcome Model|

[0147] In Table 4 above,1denotes animals that were alive for the 60-day postevaluation period;2denotes animals that were culled or dead within the 60-day postevaluation period. * denotes variables collected at chute-side real time (n = 819).+ Moth sign is noted when the pleural line shows irregularities like pleural thickening and sub-pleural consolidation (indentations). J denotes post hoc measurement (n = 710) was performed using ImageJ 1.54. Measurements were taken from a still image, where the frame of choice was the frame with most ultrasonographic artifacts and abnormalities. § denotes that only animals enrolled in the fall (n = 443) were evaluated with pulse oximetry and pulmonary auscultations.

[0148] The distribution of cases enrolled by DOF at time of first treatment grouped by treatment success at 60-day outcome indicates that the occurrence of FTF cases was dispersed throughout the feeding period (median of 28 DOF and interquartile range (IQR) 57 DOF). In contrast, recovered cases displays an apparent narrower distribution with a median of 22 DOF and IQR 36 DOF (FIG. 8B).

[0149] The descriptive statistics for POC measures in the treatment success model are listed in Table 4. Assessment by TT-POCUS revealed that most cases (688 / 819, 84%) were given ULSs of 2, 3, or 4. The majority of the cases that displayed three or more B-line count (156, 58%) resulted in treatment failure. Most cases also had >3 A-lines (459, 56%), lacked effusion (664, 81%), and did not present moth signs (569, 69%). The B-line area average for DNF was 19.7 ± 18.2 cm2 and 16.11 ± 13.1 cm2 for the recovered outcome. Blood oxygen saturation (%) averaged 85.9% ± 8.3% for FTS cattle and FTF cases averaged 84.1% ± 9.2%. Pulse (PPM) averaged 74.6 ± 22 PPM for FTS cattle and 75.1 ± 23 PPM for FTF. Cranioventral PAS indicated that 87.4% of cattle with scores of 1, 2, and 3 displayed first treatment success, whereas 43.2% of cattle with scores of 4 and 5 presented treatment failure; 51% of cattle with cranioventral PAS of 5 had a first treatment failure outcome. Caudo-dorsal PAS indicated that 29% of cattle placed in scores 1, 2, and 3 failed the first treatment, while 49.5% of cattle FTS with PASs of 4 and 5.

[0150] Logistic Regression - DNF Model: The DNF multivariate logistic regression detected evidence of five variables to be associated with DNF; two variables related to cattle demographics, DOF and BW (p = 0.001, and p = 0.007, respectively); and three related to TT-POCUS, B-line count category, moth sign, and ULS (p = 0.001, p = 0.001, and p = 0.04, respectively). Although it was included in the model as a fixed variable, sex was not significantly associated with DNF (p = 0.22). In contrast, the variables that were not significantly associated with DNF outcome and therefore were not selected by the study's multivariate logistic regression were A-line category, presence of pleural effusion. B-line area, SPO2, pulse rate, and both PASs (p > 0.05). No variables needed to be removed from the final model due to collinearity. The VIFs were 1.1 (sex), 1.9 (DOF), 1.5 (ULS), 2.0 (BW),1.3 (B-line count), and 1.2 (moth sign).

[0151] In Table 5, Tukey-adjusted probabilities obtained using the fitted model showed that cases that received first treatment for respirator}' disease mid DOF (43 to 71 DOF) or late DOF (>71 DOF) were more likely to DNF (32 ± 7%, and 64 ± 6%, respectively). In contrast, cattle that were treated for respiratory' disease for the first time early in the feeding phase (0 to 42 DOF) were less likely to DNF (18 ± 3%). The probabilities of DNF by BW showed that cattle <272 kg and >453 kg were more likely to DNF (61 ± 10% and 48 ± 8%, respectively), while 272 to 361 kg (34 ± 6%) and 362 to 453 kg (25 ± 5%) were less likely to DNF. The probability7of DNF was higher when B-line > 3 (60 ± 6%) compared to when B-line 0 to 2 (24 ± 4%). The presence of moth signs was associated with a higher probability7of DNF (51 ± 5%) compared to when moth sign was absent (24 ± 4%). The probabilities of DNF in association with ULSs are displayed in FIG. 8C. The probabilities of ULSs of 1 (18 ± 5%), 2 (24 ± 4%), 3 (28 ± 4%), and 4 (38 ± 6%) were not different amongst them (p > 0.05). However, ULS 5 (74 ± 8%) showed evidence of significant difference from all other scores (p = 0.001), showing the greatest probability of DNF.Table 5 -- Probabilities of Variables Associated with Outcome of Interest

[0152] In the Table 5 above,1denotes animals that were alive at the end of the60-day post-evaluation;2denotes animals that were culled or died within the 60-day postevaluation period; * denotes that two distinct logistic regression models were used to assess the two different outcomes: treatment success outcome (n = 819), and the latter for the didnot finish outcome (culled or dead) (n = 819). The log-odds from the logistic regression models w ere converted to probabilities using the package “emmeans” in RStudio.

[0153] First Treatment Failure model: The FTF multivariate logistic regression also detected evidence of five variables to be associated with FTF, DOF and BW (p = 0.007, and p = 0.01, respectively), and three TT-POCUS variables: B-line count category, moth sign, and ULS (p = 0.001, p = 0.001, and p = 0.03, respectively). Although it was included in the model as fixed variable, sex was not significantly associated with DNF (p = 0.23). In contrast, variables that were not significantly associated with FTF outcome and therefore were not selected by the study’s multivariate logistic regression were A-line category, presence of pleural effusion, B-line area, SPO2, pulse rate, and both PASs (p > 0.05). No variables were removed from the final model due to multicollinearity. The VIFs were 1.0 (sex), 1.8 (DOF), 1.3 (ULS), 1.9 (BW), 1.2 (B-line count), and 1.1 (moth sign).

[0154] In Table 5, Tukey-adjusted probabilities obtained using the fitted model showed that cases that received their first treatment for respiratory disease late in the feeding phase (>71 DOF) were more likely to exhibit FTF (66 ± 6%). In contrast, cattle that were treated for respiratory disease for the first time early on the feeding phase (0 to 42 DOF) to mid DOF (43 to 71 DOF) were less likely to exhibit FTF (43 ± 4%, and 44 ± 7%, respectively). Probabilities of FTF by BW showed that cattle weighing <272 kg and >453 kg were more likely to exhibit FTF (60 ± 8% and 53 ± 6%, respectively), while those weighing 272 to 362 kg (53 ± 5%) and 362 to 453 kg (38 ± 5%) were less likely to exhibit FTF. The probability of FTF was higher when the number of B-lines was > 3 (64 ± 5%) compared to when the number of B-lines was 0 to 2 (43 ± 4%). The presence of moth sign was associated with a higher probability of DNF (65 ± 4%) compared to when moth sign was absent (36 ± 4%). The probabilities of FTF by ULS are displayed in FIG. 8D. The probabilities of ULSs of 1 (36 ± 6%), 2 (37 ± 4%). and 3 (41 ± 4%) did not differ (p > 0.05). However, 4 (57 ± 6%)and ULS 5 (75 ± 7%) showed evidence of significant difference from all other scores (p = 0.006), showing a greater probability ofFTF.

[0155] Associations with First Treatment Failure and Did Not Finish Outcomes:This study aimed to investigate the potential prognostic value of various chute-side diagnostic tools, specifically targeted thoracic ultrasonography, pulse oximetry', and pulmonary' auscultation, in predicting the outcomes of first treatment failure and unfinished treatment in feedyard cattle with bovine respiratory' disease at the point of the first treatment. The findings shows that certain chute-side diagnostic parameters, such as TT-POCUS variables, are significantly associated with the likelihood of these negative outcomes. These associations offer valuable information for improving the prognosis of cattle treated for BRD and can contribute to better management practices.

[0156] The logistic regression models identified significant associations between both FTF and DNF outcomes and key parameters, such as days on feed (DOF), bodyweight (BW), B-line count category, ultrasound lung score (ULS), and the presence of the moth sign. The ULS and B-line count emerged as particularly important variables in both models, underscoring the value of TT-POCUS as a prognostic tool for BRD. Specifically, cattle with ULSs of 5, indicating more severe lung pathology, had a significantly higher probability of FTF and DNF compared to those with lower ULSs.

[0157] The association between DOF and BW and both FTF and DNF outcomes highlights the influence of feeding stage and cattle size on the prognosis of BRD. Similar results investigating epidemiological associations with mortality risk were also observed by Babcok and colleagues. Cattle treated later in the feeding phase (mid DOF and late DOF) showed a greater probability of both FTF and DNF. suggesting that cattle in these stages may be at increased risk due to cumulative stressors and more advanced disease progression.Similarly, cattle with a BW < 272 kg also displayed a higher probability of DNF and FTF.This could be atributed to the fact that lighter catle may be more vulnerable to BRD due to their transport and adaption stressors. Increased mortality risk in lighter catle has been observed in multiple reports.

[0158] Ultrasound Lung Score, B-Line Count, and Moth Sign: The TT-POCUS variables were found to be strongly associated with both outcomes of interest. The probability of DNF and FTF was significantly higher in catle with ULSs of 5, with a ULS of 4 also demonstrating a greater probability for FTF compared to lower scores. B-line count was another important factor, with cattle showing >3 B-lines having an elevated likelihood of both DNF and FTF. The presence of the moth sign, a pleural abnormality observed during ultrasound, further increased the probability of poor outcomes. These results support the utility of TT-POCUS in assessing lung pathology and guiding prognosis in BRD cases.

[0159] Interestingly, other TT-POCUS variables, such as A-line count and pleural effusion, were not significantly associated with DNF or FTF. This suggests that while certain ultrasonographic findings are highly relevant for establishing a prognosis, others may have limited predictive value for these specific outcomes. The use of ULSs, moth sign and B-line count as prognostic markers in chute-side setings provides a practical approach to help veterinarians to make informed management decisions, enabling more targeted treatment and potentially improving animal welfare and feedyard productivity.

[0160] These findings align with previous research suggesting that severe lung consolidation is indicative of poorer outcomes in catle with respiratory disease. In 2019, Timsit and collaborators showed that catle with lung consolidation (>5 cm) at the first diagnosis of bronchopneumonia had a significantly higher risk of treatment failure compared to catle with less severe lung lesions. Another similar finding was reported by Rademacher and colleagues in 2014. where mortality risk was increased when lung consolidation was present. B-line count and moth sign (sub-pleural consolidation and pleural thickening) areoften described in POC evaluations in small animals, where the presence of moth sign and a greater number of B-lines is linked to greater pulmonary' insult. In human medicine, B-line evaluation is the core artifact POC assessed for pulmonary' diagnostics.

[0161] Pulse Oximetry and Pulmonary' Auscultation: Pulse oximetry' and pulmonary' auscultation have been reported successfully in cattle due to their viability' and association yvith lung disease. Although pulse oximetry' and pulmonary' auscultation yvere included as part of the chute-side evaluations, their parameters yvere not significantly associated yvith either DNF or FTF in the final models. Blood oxygen saturation (SP02) and pulse per minute (PPM) values showed no clear differences between recovered and DNF or FTF and FTS cases, and similarly, cranioventral and caudo-dorsal pulmonary' auscultation scores (PASs) were not associated with these outcomes. The lack of significant associations may be attributed to several factors, including the variability in SP02 readings based on sensor placement (external ear), yvhich was a different placement than in previous research, or the potential for subclinical lung pathology not detectable through auscultation.Example 6

[0162] Enrollment Design and Criteria: This experiment was designed as a cross sectional observational study. Feedyard cattle respiratory morbidities were evaluated at the time of treatment using TT-POCUS by a trained veterinarian (LFBBF) for a 6 month period at 1 feedyard located in the High Plains region of the US. Respiratory morbidity was defined by a treatment event for yvhich an animal was pulled for either BP or AIP based on visual clinical signs (depression, nasal discharge, dyspnea, progressive weight loss) assessed by the feedyard personnel, including first pulls and relapsed treatments. Systematic autopsies were performed by trained technicians (MJ, LC, and LFBBF) supervised by a veterinarian (BJW) to collect pulmonary samples for histopathologic evaluation yvhen mortality occurred in casespreviously evaluated by TT-POCUS. Study enrollment required both TT-POCUS evaluation and a histopathologic diagnosis. Animals were not included in the study if mortality7did not occur following TT-POCUS or if pulmonary7samples could not be collected or were not evaluated histopathologically. Technicians were only present for necropsy on weekdays; therefore, all cattle that died following TT-POCUS may not have been included.

[0163] The categories of days on feed (DOF) were determined based on previous reported work that categorizes the temporal epidemiological characteristics of respiratory morbidities in feedyard cattle, where 0 to 42 days are considered early DOF, 43 to 1 midDOF, and > 71 late DOF.25 Individual animal body weight (BW) was also recorded at the time of treatment; animal BW was categorized as < 272, 272 to 362, 362 to 453, and > 453 kg.

[0164] TT-POCUS Method: Commercial feedyard cattle were evaluated with TT-POCUS where animals were restrained in a hydraulic squeeze chute, and ultrasound lung scans were no longer than 60 seconds to maintain commercial feedyard work pace. Probe placement for TT-POCUS allowed evaluation of only the right caudodorsal pulmonary lobe assessing between the 8th and 11th rib. The same ultrasound probe unit was utilized for all ultrasonographic evaluations in this study (iQ + Vet; Butterfly Network Inc; 1 MHz to 10 MHz), a point-of-care specialized probe with a 5 X 3-cm footprint set on the optimized preset for lung evaluation. Presets configured the probe array to linear and allowed for up to 30- cm depth, and overall gam was set at 65%. Preparation of the scanning area was performed using 70% isopropyl alcohol without trimming / shaving the hair coat.

[0165] Scanning procedures involved probe placement placed parallel to the ribs, specifically starting at the 11th intercostal space (ICS) and moved from the dorsal aspect of the ICS (identified by visualization of the ventral part of longissimus dorsi), then sliding the probe ventrally until visualizing the liver, diaphragm, and caudal tip of the lung, also calledas “curtain sign."’ Due to animal variability, the “curtain sign” may not be visible at the 11th ICS, requiring movement of the probe cranially to the next ICS (10th). Each ICS from 11th to 8th was evaluated dorsally to ventrally similarly. Probe “fanning” was also performed, where the angle relative to the skin (side-to-side angle) is dynamically changed to facilitate the visualization of the pulmonary' tissue between the bone structures (ribs).

[0166] Data collection from TT-POCUS evaluation was carried out in real-time and post hoc analysis by a trained veterinarian. Real-time variables collected included: ultrasound lung score (ULS), B-line count, A-line count, presence of pleural effusion, and presence of pleural abnormalities (moth sign [pleural indentations] and pleural thickening). A l-to-5 scale was used for ULS, similar to previous research, with ULS scores defined as: 1, presence of < 3 thin B-lines (healthy lung); 2, presence of 3 or more thin B-lines; 3, presence of merged B-lines; 4, presence of several wide / merged B-lines and abnormal pleural (moth sign); and 5, consolidated lung (presence of consolidation with > 1 cm of depth) and might be accompanied with moth sign and effusion based on description of lung injury; consolidated lung is defined by an ultrasound image of parenchymal organs like, the liver, commonly called hepatization. The B-line count was based on a representative count where B-lines were defined as hyperechoic vertical artifacts resembling comet tails. B-line count was categorized (0 to 2, 3 to 5, and > 5) based on the theorized degree of severity, where more B-lines could translate to greater tissue injury. A-lines were defined as hyperechoic horizontal artifacts (reflections of the pleural line) and were categorized in 2 groups: 0 to 2 A-lines and > 3 A- lines. These groups were based on the goal of evenly dividing the population. Both A-line counts and B-line counts were determined based on the lung field with the most abnormalities. The presence / absence of pleural effusion was determined by the presence of a hypoechoic / anechoic layer in pleural space, and pleural thickening / irregularity was recorded as present or not.

[0167] Other variables were generated post hoc using the software ImageJ, version 1.54 (US NIH); measurements were taken from a single frame from the 60-second clip, where the frame of choice was the frame with the most tissue abnormalities, allowing a comparison based on tissue injury7within the caudodorsal region. The post hoc variables included image brightness (defined as pixel intensity7density7= average gray value X pixel count), average gray value (the mean intensity of pixels from the image histogram), and B- line area (cm2). Image brightness was measured using a 7-cm linear selection, starting at the pleural line and extending diagonally across the image, from top to bottom. The average gray value was also determined using the same 7-cm selection to evaluate the intensity units within the image. Total B-line area in cm2was calculated by the use of the polygon selection tool, where all the B-lines in the image would be measured, and the sum of these areas would equate to the total affected B-line area in cm2, with the objective to account for potential differences in B-line width and merged B-lines. It is important to report that A-line evaluation has not been reported in the literature. On the other hand, B-line count has been reported extensively in the literature. B-line count is often categorized into normal (0 to 2) versus abnormal (> 3). In the present study, a category was created for B-line counts > 5 based on the theory7of higher tissue injury with higher B-line counts.

[0168] Histopathology: Pulmonary tissue samples collected for histopathologic diagnosis were collected as a 1-cm2tissue section from the right cranioventral lobe and right caudodorsal lung lobes. Samples were fixed by immersion in 10% neutral-buffered formalin, paraffin embedded, and then processed routinely by the histology7laboratory at the Kansas State Veterinary Diagnostic Laboratory (KSVDL). Four-micrometer-thick tissue sections were cut to prepare slides prior to routine staining with H&E using a Leica ST5010 Autostainer XL (Leica Biosystems) and then digitized using a Leica Aperio AT2 (Leica Biosystems) digital slide scanner and evaluated by board-certified veterinary pathologists(AF and BLP) who were blinded to TT-POCUS evaluation and animal records. This study did not differentiate AIP cases from BIP anatomically; for example, lesions of IP in either section of lung resulted in categorization as IP for any individual case. Key histopathologic lesions indicative of IP included: alveolar fibrin exudation, alveolar hyaline membranes, or septal necrosis supportive of (acute IP); type 2 pneumocyte hyperplasia and hypertrophy supportive of (subacute) IP; and interstitial fibroplasia and bronchiolitis obliterans supportive of (chronic) IP. Without these histopathologic features supportive of IP in either sample, cases were allocated to the non-IP group.

[0169] Excel (Microsoft Corp) and R Studio (R Studio, version 2023.12.1; R CoreTeam) software were utilized for data handling. For descriptive purposes, a table comparing the outcome variables was built using the packages 'dplyr' and 'tidyr' in R Studio. A generalized linear model was utilized to analyze data using the 'glm' with 'logit' link function of the native 'stats' package of R Studio. Histopathological diagnosis was the outcome variable of interest, where the diagnosis was classified binomially as IP (1) for IP or non-IP (0) for BP and all other respiratory syndromes without IP. The model-building process included using the 'scope' parameter to always include the fixed variables (sex and DOF) in the model iterations. The variables sex (heifer vs steer) and DOF at time of chuteside evaluation (DOF) categories (0 to 42, 43 to 71, and > 71) were fixed in the model at the experimental design phase of the study due to previous studies describing the association of sex and late DOF in AIP. The stepwise selection with the backward direction using the 'stepAIC function in the 'MASS' package of R Studio, where all the variables collected were included (BW, ULS, B-line categories. A-line categories, pleural effusion, pleural abnormality, image brightness, average gray value, and B-line area) and least significant variable is removed at each selection step based on best model fitness assessed by lowest Akaike information criterion coefficient. Multicollinearity amongst variables was assessedusing the 'car' package in R Studio, where calculates the variance inflation factor (VIF), variable was considered to have considerable collinearity VIF coefficient when > 2.5, therefore removed from the model. The log-odds ratio from the logistic regression model was then converted to probabilities to facilitate interpretation by using the package 'emmeans' in R Studio.

[0170] A total of 1,200 animals were evaluated using TT-POCUS at the time of respiratory treatment, with 66 animals resulting in mortalities on days technicians were present for necropsy and pulmonary sample collections. Four cases were excluded for incomplete data or missing histopathologic diagnosis, resulting in 62 cases enrolled in the study. The population included 60% (37 of 62) of the cases histopathologically diagnosed with IP and 40% (25 of 62) diagnosed with non-IP. There were 38 of 62 heifers (61%) and 24 of 62 steers (39%) enrolled, with descriptive statistics listed in Table 4. The distribution of cases enrolled by DOF at the time of treatment grouped by histopathologic diagnosis indicates that IP cases occurred dispersed throughout the feeding period (median, 50 DOF; IQR, 67 DOF). In contrast, non-IP case distribution showed a tighter distribution of cases, with a median of 28 DOF and an IQR of 16 DOF (FIG. 9). In addition to DOF, the interval in days from when the animal was evaluated at chuteside to the day of death can also be displayed with the raincloud plot and box-plot combination (not shown), where IP cases had a shorter day interval window from chuteside evaluation to death, with a median of 4 days and an IQR of 5 days. Non-IP cases displayed a longer interval window from chuteside evaluation to death, with median of 8 days and an IQR of 6 days. Multivariate logistic regression was utilized to identify potential associations between variables of interest (sex, DOF. BW. ULS, B-line count category, A-line count category, pleural effusion, moth sign, pixel intensity density, average gray value, and B-line area cm2) and an outcome response of a diagnosis of IP using a stepwise backward selection of variables. The fixed variable sex wasnot associated with the outcome IP in this study (P = .59), but DOF category was significantly associated with IP (P = .01). The final model included several variables significantly associated with the probability of IP: ULS (P = .02), B-line count category (P = .01), and A-line count category7(P = .03). The BW variable w as removed from the model due to considerably high VIF (4.4).Table 6 -- Descriptive Statistics (n=62)

[0171] In Table 6 above.adenotes cases diagnosed as non-IP by histopathology.bdenotes cases diagnosed with IP by histopathologic evaluation.cdenotes variables collected by real-time targeted thoracic point-of-care ultrasound.ddenotes post hoc measurements were performed using ImageJ, version 1.54. Measurements were taken from a single frame from the 60-second clip, where the frame of choice was the frame with the most tissue abnormalities, allowing a comparison based on tissue injury within the caudodorsal region.

[0172] Tukey-adjusted probabilities using the fit model showed the probability ofIP was higher (P < .05) when animals were 43 to 71 DOF (87 ± 11%) and > 71 DOF (81 ±16%) compared to 0 to 42 DOF (29 ± 16%). An ultrasound lung score of 1 was not present in any of the 62 cases; thus, the probabilities were only calculated for scores 2, 3, 4, and 5. A ULS of 5 was the least likely to be confirmed as IP by histopathology', with 12 ± 16% likelihood of a diagnosis of IP amongst all the scores (P < .05). Ultrasound lung scores of 2, 3, and 4 did not differ in the probabilities of IP (P > .05; 72 ± 18%, 92 ± 6%, and 85 ± 8%, respectively). A B-line count category of > 5 B-lines in an ultrasound image was associated with an 86 ± 11% likelihood to be diagnosed with IP, whereas animals with 3 to 5 B-lines were associated with a significantly lower probability of a confirmed diagnosis of IP (38 ± 14%; P < .05). A B-line count category of 0 to 2 showed no statistical difference amongst the other 2 categories (75 ± 15% likelihood of IP; P > .05). The probabilities of A-line count categories 0 to 2 versus > 3 A-lines were 83 ± 10% and 51 ± 15%, respectively (P < .05).

[0173] Discussion

[0174] Respiratory disease is a critical factor contributing to feedyard morbidity and mortality', and accurate classification of disease types at the time of treatment is essential to inform effective management and treatment strategies. Results indicate that TT-POCUS can be a valuable diagnostic tool for identifying IP in feedyard cattle morbidities, and similar promising results in identifying IP have been shown in human medicine during the COVID- 19 pandemic, where bedside point-of-care ultrasound was utilized to evaluate IP.

[0175] Lung evaluation using ultrasonography in veterinary medicine has been reported for respiratory disease detection and prognosis. However, this study proposed a novel evaluation method, where the evaluation time was < 60 seconds, by targeting the caudodorsal lobe of the right lung and was able to decrease evaluation time drastically to find associations of real-time variables (ULS, B-line count. A-line). In contrast to the study that developed the TT-POCUS, the traditional pulmonary ultrasound evaluation is more comprehensive and could take 7 to 45 minutes; this significant time constraint is a limitationto deploying the method within a commercial operation, and the accessibility to the cranioventral acoustic window can also be challenging and time consuming.

[0176] Notably, the study demonstrated that real-time collected variables were the only ones significantly associated with histologic diagnosis of IP, suggesting that TT-POCUS can be effectively used chuteside for immediate diagnostic and therapeutic decision making. Although post hoc calculated variables were tested, none of them showed significant associations with IP, further highlighting the practical utility of real-time ultrasound evaluations in the field. In addition, performing TT-POCUS in a 60-second window increases the feasibility for application of this method in commercial operations where the time spent in diagnosis is a major determining factor for adoption. The ability of adopting TT-POCUS in a commercial operation can enhance diagnostic accuracy in real-time, improve treatment outcomes in feedyard cattle, and aid informed management decisions.

[0177] Histopathologic analysis revealed that the majority of cases in the study were diagnosed with IP (37 of 62 [60%]), whereas 40% were diagnosed with non-IP. Higher IP prevalence observed in this study may be best explained by the inclusion of both BIP and AIP in contrast to other surveillance studies. Furthermore, another significant factor that could have contributed to higher-than-expected prevalence is the increased sensitivity due to the histologic evaluation of 2 lung samples per animal. A previous epidemiologic publication reported the prevalence of IP (AIP plus BIP) mortalities in feedyard to be approximately 60.4% of the all the respiratory mortalities, whereas 40.4% of the respiratory mortalities were classified as non-IP.

[0178] Interstitial pneumonia cases were more dispersed across the feeding period, with a median DOF of 50 days and an IQR of 67 days, indicating a wide distribution of cases. In contrast, the non-IP cases had a narrower distribution, with a median DOF of 28 days and an IQR of 16 days (FIG. 9). Additionally, the interval from chuteside evaluation todeath was shorter for IP cases (median, 4 days; IQR, 5 days) compared to non-IP cases (median, 8 days; IQR, 6 days); this shorter time interval from clinical diagnosis to death is the theorized quick development of IP, also described as acute.

[0179] This variability7in interval from TT-POCUS evaluation to death (necropsy) may influence the findings related to pulmonary7lesions as longer intervals could allow for progression or resolution of certain lung pathologies, which may obscure the initial TT- POCUS findings. Specifically, the rapid progression of IP cases may mean that TT-POCUS findings at the time of diagnosis align more closely with the acute lesions observed on histopathology7, whereas non-IP cases may have had more time for lesion development or alteration.

[0180] No epidemiologic association was identified in this study between IP and sex. The existing literature often reports a higher susceptibility of heifers to AIP and a higher proportion of steers presenting with BIP. Data did not show a statistically significant difference between heifers and steers in terms of IP probability, and the absence of a significant difference in the study may be attributed to classifying BIP and AIP cases as IP, specific management practices, sample size, or environmental factors unique to the study population. Although this study did not differentiate between AIP and BIP, which could have affected the differences between heifers and steers, the decision to group these conditions together was based on their overlapping pathology presentations at the caudodorsal level. Future studies with larger sample sizes and clearer differentiation of these subtypes are warranted to explore potential sex-related differences in IP prevalence.

[0181] The distribution of cases based on DOF showed that most animals diagnosed with non-IP were in the 0 to 42 DOF category, which is considered the earlyfeeding phase. This temporal pattern was observed by previous studies, where non-IP including BP had higher morbidity7early in the feeding phase (< 42 DOF). In contrast,animals diagnosed with IP were distributed across the 3 time categories: 40% (15 of 37) were observed in the 0 to 42 DOF category, while 60% (22 of 37) were found in the combined categories of 43 to 71 DOF and greater than 71 DOF. These DOF patterns for non-IP (BP and other respiratory7diseases not IP related) and IP are similar to a recent epidemiologic study showing that IP occurred throughout the feeding phase. Although IP distribution was dispersed throughout the feeding period in the current study, the model-adjusted probabilities indicate that IP is more likely to happen mid and late (> 42 DOF). These probabilities are corroborated by field data indicating that IP is often described as a late-day disease.

[0182] The inclusion of epidemiological data in this study enhances the understanding of how TT-POCUS findings could be interpreted alongside other demographics to improve IP diagnosis. While TT-POCUS provides valuable information on lung pathology7, integrating this data with demographic factors, such as DOF or BW, could improve the diagnostic accuracy. Hence, DOF and sex were kept in the model.

[0183] Ultrasound lung score indicated that animals with a score of 5 had higher prevalence (5 of 6 of all ULS 5 [83%]) in cattle in the non-IP group. Hence, the probability of these animals with confirmed IP was very low among those animals with ULS 5 from TT- POCUS (12 ± 16% likelihood), reinforcing that pulmonary' tissue consolidation identified during ultrasound evaluation is not likely to be associated with a confirmed diagnosis of IP (FIG. 10). The B-line count category was also associated with IP outcome, where animals with more than 5 B-lines identified at TT-POCUS displayed the greatest likelihood to be diagnosed with IP (86 ± 11%). The B-line is the artifact most evaluated in pulmonary ultrasonography, and its relevance in the context of IP in the study is that more B-lines appear in the image when there are areas of decreased air content. The use of these real-time variables in differentiating respiratory diseases have not been reported in other studies. In contrast, thoracic ultrasonography has been used extensively in critical care for respiratorydisease detection and assessment of disease severity. One limitation to B-line evaluation count is that as disease develops and pulmonary injury worsen, B-lines have the potential to merge (FIG. 10), making absolute quantification difficult. In an attempt to evaluate the potential association of merged B-lines, this study analyzed the post hoc variable B-line area to account for B-line merging. However, the statistical analysis revealed no evidence of an association of B-line area with an ability to differentiate of IP from non-IP. It was theorized that the merging of B-lines was most likely accounted for in the statistical model using ULS.

[0184] The A-line count category is a novel variable not described as useful for respiratory disease differentiation. Evidence of statistical association was found, where A-line count category lower than 3 displayed a higher probability of IP diagnosis (< 3 vs > 3 A-lines were 83 ± 10% and 51 ± 15% probability of IP). Normal lungs are expected to exhibit several A-lines due to the reflection of the ultrasound waves by the pleural line, which is caused by the air-filled lung. A-lines appear as hyperechoic horizontal artifacts, and their presence typically indicates a healthy lung with normal air content. In contrast, lower A-line counts in IP cases can be attributed to the pathologic changes occurring in the lung tissue. Interstitial pneumonia involves the infiltration of inflammatory cells into the interstitium and accumulation of fluid within the alveolar spaces, which disrupts the normal air-tissue interface. This disruption reduces the reflective surface area available to produce A-lines. As a result, the number of detectable A-hnes decreases in association with decreased lung aeration.

[0185] Collectively, several features, including a ULS of 2, 3. and 4; higher B-line counts; and lower A-line counts, occur significantly more often in IP cases compared to nonIP cases, suggesting that these markers could serve as useful diagnostic indicators in veterinary medicine for identifying severe interstitial lung disease. By incorporating ULS. B- line, and A-line counts into TT-POCUS evaluations, field veterinarians can improve theirability to differentiate IP from other respiratory diseases, leading to more targeted and early- effective treatment strategies. However, the study was limited in scope (only mortalities); TT- POCUS shows promise as an adjunct diagnostic tool in commercial settings when used in conjunction with routine health monitoring.

[0186] The TT-POCUS requires some level of training; the simplicity- and speed of the caudodorsal lung evaluation make it a promising tool for adoption by trained operators in commercial feedyards. In 2013, Buczinski et al highlighted that with adequate training, even less experienced operators can perform thoracic ultrasonography with consistency and accuracy in detecting lung pathologies in cattle. Variables, such as B-line and A-line counts, are relatively straightforward to learn with adequate training and could provide valuable diagnostic insights when integrated into routine health assessments.

[0187] Furthermore, this study did not assess the impact of specific therapeutic interventions, but it is plausible that treatment outcomes might affect lesion progression or resolution over time. Future research should explore these factors further to clarity the relationships between TT-POCUS findings, treatment effects, and histopathologic outcomes in feedyard cattle.

[0188] The findings of this study highlight the substantial potential of TT-POCUS as a diagnostic modality for the identification of IP in feedyard cattle. Significant associations were observed between the histopathologic diagnosis of IP and imaging parameters, including B-line count. ULS, and A-line count. These associations show the potential diagnostic efficacy of TT-POCUS in differentiating interstitial lung disease from other respiratory diseases in a timely manner. This diagnostic method can potentially enhance management decisions in cattle with findings compatible with IP, leading to potential improved health outcomes for feedyard cattle morbidities. Integrating TT-POCUS intoroutine sick animal diagnostics can streamline health monitoring and disease management processes.EXAMPLE 7 - TT-POCUS for Screening Cattle

[0189] The same methodology of targeted point-of-care ultrasound (TT-POCUS) can be used to collect information on non-diseased animals as a screening tool. This methodology7would be applied to a different population and same basic data recorded, but differences are present in the type of decision that would be made and how this decision would be implemented.

[0190] Individual cattle can be screened to identify subclinical illness when no overt signs of disease are displayed. These cattle could be screened on arrival at the feedyard or as a portion of a pen to test for potential subclinical illness. Using combination of TT- POCUS attributes allows decisions on the individual animal for improved therapeutic outcome. The individuals may also be evaluated to determine the future potential for this individual to succumb to illness which may modify the preventative therapy or monitory program.

[0191] The individuals or a subset of the population may be individually scanned to determine the need for group level interventions. For example on arrival a group of cattle could be screened and based on TT-POCUS findings, if at least 20%, more preferably at least 30%. still more preferably at least 40, 50, 60, 70, 80, 90, 95, or even 100% of the group is currently exhibiting signs of respiratory disease (clinical or subclinical) a treatment of the entire group with an antimicrobial may be warranted to limit the potential outbreak. Antimicrobial stewardship is important and this tool can be used to help direct mass treatment events to groups of cattle that will benefit from the therapy.EXAMPLE 8 - TT-POCUS Evaluations For IP Modeling, DNF from 1stTreatment, FTS from 1stTreatment, and Chronic DNF

[0192] A number of exemplary algorithms were developed for predictive modeling based on targeted thoracic point-of-care ultrasound image interpretation as well as a number of variables related to cattle health. These are presented to demonstrate how the TT- POCUS methods described herein can be used in combination with easily obtainable clinical data to provide robust predictive algorithms.

[0193] Each algorithm provided below pertains to a particular predictive model and incorporates a number of binary variables whose values are either ' I ' or ‘0,’ engendering ease of data collection and use. The variables used in these models are as follows. “Sex_Steer” refers to the sex of the cattle, with a value of ‘1 ’ denoting a steer and a value of ‘0’ denoting a heifer. DOF 43-71 refers to the cattle’s days on feed, with a value of ‘ 1 ’ denoting a 43-71 days on feed and a value of ‘0’ denoting otherwise. DOF_>71 refers to the cattle’s days on feed, with a value of ‘ 1 ’ denoting greater than 71 days on feed and a value of ‘0’ denoting otherwise. DOF_gt71 refers to the cattle’s days on feed, with a value of ‘ 1’ denoting greater than 71 days on feed and a value of ‘0’ denoting otherwise. DOF_gt72 refers to the cattle’s days on feed, with a value of ‘ 1 ’ denoting greater than 72 days on feed and a value of ‘0’ denoting otherwise. ULS_3 refers to the ultrasound lung score (ULS) as described above, with a value of T denoting a ULS of 3 and a value of ’0’ denoting otherwise. ULS 4 refers to the ultrasound lung score (ULS) as described above, with a value of 4’ denoting a ULS of 4 and a value of ‘0’ denoting otherwise. ULS_5 refers to the ultrasound lung score (ULS) as described above, with a value of ‘ 1 ’ denoting a ULS of 5 and a value of ‘0’ denoting otherwise. LungScore (or ULS) 2 refers to the lung score, with a value of ‘ 1’ denoting a lung score of 2 and a value of ‘0‘ denoting otherwise. LungScore_3 refers to the lung score, with a value of ‘ 1 ’ denoting a lung score of 3 and a value of ‘0’denoting otherwise. LungScore_4 refers to the lung score, with a value of ‘1’ denoting a lung score of 4 and a value of ‘0’ denoting otherwise. LungScore_5 refers to the lung score, with a value of ‘ 1’ denoting a lung score of 5 and a value of ‘0’ denoting otherwise. BW_600_800 refers to the body weight of the cattle, with a value of ‘ 1’ denoting a body weight between 600 and 799 pounds and a value of ‘0’ denoting otherwise. BW_800_1000 refers to the body weight of the cattle, with a value of ‘ 1’ denoting a body weight between 800 and 999 pounds and a value of ‘0’ denoting otherwise. BW gtI OOO refers to the body weight of the cattle, with a value of ‘1 ’ denoting a body weight greater than 1000 pounds and a value of ‘0’ denoting otherwise. Bline_3_5 refers to the count of B-lines, with a value of ‘1 ’ denoting 3-5 B-lines and a value of ‘0’ denoting otherwise. Bline_gt3 refers to the count of B-lines, with a value of ‘1’ denoting greater than 3 B-lines and a value of ‘0’ denoting otherwise. Aline_>3 refers to the count of A-lines, with a value of ‘ 1’ denoting greater than 3 A-lines and a value of ‘0’ denoting otherwise. In some forms, the B line counts are grouped as 0-2, 3-5, and >5. Moth_Yes refers to the presence or absence of a moth sign, with a value of ‘1 ’ denoting the presence of a moth sign in the corresponding cattle ultrasound, and a value of ‘0’ denoting the absence of a moth sign.

[0194] For an Interstitial Pneumonia Model, the following equation can be used with regard to the above variables: P(Histo_IP = 1) = 1 / (1 + exp[-(0.5696- 0.3834 • Sex_Steer+ 2.1331 • DOF_43_71+ 1.6642 • DOF_>71+ 1.3601 • ULS_3+ 0.9919 • ULS_4- 2.8504 • ULS_5- 0.7569 • Blme_3_5- 1.4435 • Aline_>3)]), where T(Histo_lP = I)7’ denotes the probability of a diagnosis of IP in the cattle patient. The LateX / mathtype code for this formula is denoted as P(Histo IP = 1) = 1 / (1 + exp[-(0.5696- 0.3834 • Sex Steer+ 2.1331 • DOF 43 71+ 1.6642 • DOF >71+ 1.3601 • ULS 3+ 0.9919 • ULS 4- 2.8504 • ULS 5- 0.7569 • Bline_3_5- 1.4435 • Aline_>3)]). This can also be depicted as:

[0195] Table 7 below provides exemplary data of 100 cattle whose probability of a diagnosis of IP is calculated by the IP model described above.0 0 0 0 0 0 0 0 63.8670872%0 0 1 0 0 0 1 1 50.8349224%1 1 0 0 0 1 1 1 6.1140003%0 1 0 0 0 0 0 1 77.8888361%0 0 1 0 0 1 0 0 35.0555123%1 0 1 0 0 0 0 0 86.4174061%1 0 0 1 0 0 1 0 68.7702484%0 0 0 0 1 0 0 0 82.6568488%0 0 0 0 0 0 1 1 16.3720799%1 1 0 0 1 0 1 1 75.2278226%0 0 0 1 0 0 1 1 43.2735254%0 1 0 0 0 1 0 1 16.9215132%1 1 0 0 0 0 0 1 70.5951123%1 0 0 0 0 1 0 0 6.5119176%1 1 0 1 0 0 1 0 94.8947550%1 1 0 0 0 0 1 1 52.9690030%1 0 0 0 1 0 1 1 26.4579631%1 0 1 0 0 1 1 1 3.9150673%0 0 1 0 0 0 0 0 90.3243967%1 0 0 0 0 0 0 1 22.1439032%0 0 1 1 0 0 1 0 94.4642793%1 1 0 0 1 0 0 0 96.4811044%1 0 0 0 0 0 1 0 36.1075319%1 1 0 0 0 0 1 0 82.6697468%0 0 0 0 0 0 1 0 45.3311412%1 0 0 0 1 0 0 1 43.4036735%0 0 1 0 1 0 0 1 85.5968309%0 0 1 0 1 0 1 0 92.1925434%0 0 0 0 0 0 1 1 16.3720799%1 0 0 0 0 1 0 1 1.6179475%0 0 0 0 1 0 1 0 69.0957605%0 0 0 0 0 1 1 0 4.5751480%0 1 0 0 0 0 1 1 62.2999687%0 0 1 1 0 0 0 1 89.5706149%1 0 0 0 0 1 0 1 1.6179475%0 0 1 0 0 1 1 0 20.2054957%0 0 1 1 0 0 1 0 94.4642793%1 0 1 0 1 0 0 0 94.4919292%0 0 1 0 0 1 0 0 35.0555123%1 1 0 0 0 1 0 1 12.1895659%0 0 1 0 1 0 1 0 92.1925434%1 0 1 1 0 0 1 1 73.3039719%1 0 0 0 0 0 0 1 22.1439032%1 0 0 0 0 1 0 0 6.5119176%1 0 0 1 0 0 0 1 52.5677391%1 1 0 1 0 0 1 0 94.8947550%69 1 0 1 1 0 0 1 1 73.3039719%70 0 0 1 0 0 0 0 1 68.7895743%71 0 0 0 0 0 1 0 0 9.2725629%72 1 1 0 0 0 0 1 0 82.6697468%73 0 0 1 0 0 0 1 1 50.8349224%74 1 1 0 0 0 0 0 0 91.0462893%75 1 0 1 0 0 0 0 0 86.4174061%76 0 1 0 0 1 0 1 1 81.6707833%77 0 0 1 0 1 0 0 1 85.5968309%78 0 1 0 0 1 0 1 0 94.9678926%79 1 0 1 0 0 0 1 1 41.3382421%80 1 0 1 0 1 0 0 1 80.1993398%81 1 0 0 0 0 0 1 0 36.1075319%82 0 1 0 0 0 1 1 0 28.8106114%83 1 0 0 0 0 0 0 1 22.1439032%84 1 1 0 0 0 0 0 0 91.0462893%85 1 0 0 0 0 0 1 1 11.7720056%86 1 1 0 0 0 1 1 0 21.6191524%87 0 0 0 1 0 0 0 1 61.9210842%88 1 0 1 0 0 0 1 0 74.9040221%89 1 0 0 1 0 0 0 0 82.4378694%90 0 0 0 1 0 0 0 1 61.9210842%91 0 1 0 0 1 0 0 1 90.4745377%92 0 1 0 1 0 0 0 0 98.3090075%93 1 0 1 1 0 0 1 0 92.0824314%94 0 1 0 0 0 0 1 1 62.2999687%95 1 1 0 0 1 0 1 1 75.2278226%96 0 1 0 0 0 0 1 1 62.2999687%97 1 0 0 1 0 0 0 0 82.4378694%98 0 0 0 1 0 0 0 0 87.3216211%99 1 0 0 0 1 0 0 0 76.4606007%100 1 1 0 0 0 1 0 1 12.1895659%

[0196] For an DNF after 1stTreatment model, the following equation can be used with regard to the above variables: P(DNF = 1) = 1 / (1 + exp[-(-1.5210- 0.481 1 • Sex_Steer+ 0.7798 • DOF 43 71+ 1.0185 • DOF_gt71- 0.3213 LungScore_2- 0.7426 • LungScore_3- 0.5706 • LungScore_4+ 1.1356 • LungScore_5- 0.9507 • BW_600_800- 1.4869 • BW_800_1000- 0.3817 • BW_gtl000+ 1.5883 • Bline_gt3+ 1.1321 • Moth_Yes)]), where “P(DNF = 1)?’ denotes the probability of the cattle patient not finishing within the test period, due for example to being culled or dying. The LateX / mathtype code for this formula is:

[0197] P(DNF = 1) = 1 / (1 + exp[-(-1.5210- 0.4811 • Sex_Steer+ 0.7798DOF 43 71+ 1.0185 • D0F_gt7 l- 0.3213 • LungScore_2- 0.7426 • LungScore_3- 0.5706 • LungScore_4+ 1.1356 • LungScore_5- 0.9507 • BW_600_800- 1.4869 • BW_800_1000- 0.3817 • BW_gt l 000+ 1.5883 • Bline_gt3+ 1.1321 ■ Moth_Yes)]). It may also be depicted as follows:

[0198] Table 8 below provides exemplary' data of 100 cattle whose probability of a DNF outcome after 1sttreatment is calculated by the DNF after 1stTreatment model described above.1 0 1 0 0 0 0 1 0 0 0 0 12.6275399%1 0 1 0 1 0 0 0 0 0 0 0 15.1074287%0 0 1 1 0 0 0 0 0 1 1 0 59.4548251%1 1 0 0 0 0 0 1 0 0 1 0 35.7851842% 1 1 0 1 0 0 0 0 0 0 0 0 17.6012550% 0 0 1 0 1 0 0 1 0 0 0 0 10.0128285%1 1 0 1 0 0 0 0 0 0 1 0 51.1173140% 1 0 0 0 0 0 0 0 0 1 1 0 31.0988934%0 1 0 0 0 0 1 0 0 0 1 0 87.8968690%0 0 0 1 0 0 0 0 0 O i l 70.6428340%0 1 0 0 0 0 0 0 0 1 1 1 83.1668686%1 0 1 1 0 0 0 0 0 0 0 1 45.6907175%0 0 1 0 0 0 1 0 1 0 1 0 67.5791989%0 1 0 1 0 0 0 0 0 1 0 0 19.0895797%0 0 1 0 0 0 1 0 1 0 1 0 67.5791989%0 0 1 1 0 0 0 0 0 O i l 86.9506229%1 0 1 0 0 0 1 0 0 1 0 0 44.2826163%1 1 0 1 0 0 0 0 1 0 0 1 13.0289670% 0 1 0 0 1 0 0 1 0 O i l 57.0992090%0 1 0 0 0 0 1 0 0 0 0 0 59.7341459%1 0 1 0 1 0 0 0 1 0 1 0 16.4543955%0 0 1 0 0 1 0 0 1 0 1 0 27.4541786%1 0 1 1 0 0 0 0 0 0 1 0 57.0379581%0 0 0 0 0 0 1 0 1 0 1 0 42.9473397%1 1 0 0 0 0 0 1 0 O i l 63.3532161% 0 1 0 1 0 0 0 0 0 O i l 83.9955902%0 1 0 0 0 1 0 0 0 0 0 0 21.2185795%1 0 1 0 0 1 0 0 0 1 0 0 12.6098976%0 1 0 0 1 0 0 0 0 1 0 0 13.4063269%0 0 0 0 0 0 0 1 0 0 0 0 7.7866082%1 0 1 0 0 1 0 0 0 1 0 0 12.6098976%0 0 0 0 1 0 0 0 0 1 0 1 18.0465037%1 0 1 0 0 0 0 0 0 1 0 1 44.1962778%1 1 0 0 1 0 0 0 1 0 1 0 13.4295620% 0 1 0 1 0 0 0 0 0 0 0 1 51.7392979%1 1 0 0 1 0 0 0 0 1 1 0 31.9015468% 1 0 0 0 0 0 0 1 0 0 0 0 4.9604342%0 1 0 1 0 0 0 1 0 0 0 1 29.2949884%1 0 0 0 0 1 0 0 0 1 0 0 4.9528967%0 1 0 0 1 0 0 0 1 O i l 43.7749660%1 1 0 0 0 1 0 0 0 0 1 0 44.9027689% 1 0 0 1 0 0 0 0 0 0 1 0 32.4076567%1 0 0 1 0 0 0 1 0 0 0 0 3.6470479%1 1 0 1 0 0 0 0 0 O i l 76.4371948% 0 0 1 0 0 0 1 0 1 O i l 86.6064379%1 1 0 0 0 0 1 0 0 0 1 0 81.7812989%0 0 1 1 0 0 0 0 0 0 1 1 86.9506229% 1 0 1 0 0 0 1 0 0 0 1 0 85.0725167% 0 1 0 0 0 0 1 0 0 1 1 1 93.8951775% 0 0 1 0 0 0 0 0 0 1 0 1 56.1659552% 1 0 1 0 0 0 1 0 1 0 1 1 79.9872872% 0 0 0 0 0 1 0 0 1 0 0 1 7.9702209% 0 0 0 0 0 0 0 0 0 1 1 1 69.3747896% 0 0 1 1 0 0 0 0 1 0 1 0 32.6864706% 1 0 1 0 1 0 0 1 0 0 1 0 25.1881999% 0 0 1 1 0 0 0 0 0 0 1 0 68.2329934% 0 0 1 1 0 0 0 0 1 0 0 0 9.0240660% 1 1 0 1 0 0 0 0 0 0 1 0 51.1173140% 0 0 1 0 0 0 1 1 0 0 0 0 42.1260757% 1 1 0 1 0 0 0 0 1 0 0 0 4.6066850% 1 1 0 0 0 0 1 0 0 1 1 1 90.4822911% 0 1 0 0 0 0 0 0 0 1 0 1 50.2299984% 0 0 0 0 0 0 0 0 0 0 1 0 51.6818652% 1 0 0 0 1 0 0 0 1 0 0 0 1.4321053% 1 1 0 0 0 0 0 0 1 0 1 1 50.2799971% 0 0 1 0 0 0 0 0 0 0 1 0 74.7590009% 1 0 0 0 0 1 0 0 1 0 1 1 20.7641649% 1 0 0 0 1 0 0 1 0 0 0 0 2.4235565% 0 0 0 0 0 1 0 1 0 0 1 1 42.0212773% 0 0 1 0 0 1 0 0 0 1 0 1 42.0017878% 0 0 1 1 0 0 0 0 0 0 0 0 30.4957621% 1 0 0 0 1 0 0 0 1 0 0 0 1.4321053% 0 0 1 0 0 1 0 0 1 0 1 1 54.0014246% 0 0 0 0 0 1 0 0 1 0 1 0 12.0235705% 0 1 0 0 0 0 1 1 0 0 0 1 64.0100415% 1 0 0 0 0 0 0 0 0 1 1 0 31.0988934% 0 0 0 0 0 1 0 0 0 1 1 1 56.1462584% 0 0 0 1 0 0 0 0 0 1 0 1 25.1260665% 1 0 1 0 1 0 0 0 0 0 1 1 72.9916702% 0 0 1 0 0 1 0 0 0 0 1 1 83.8525804% 1 0 1 0 0 0 1 0 0 0 1 0 85.0725167% 0 0 1 0 0 0 1 0 1 0 1 0 67.5791989% 0 0 1 0 1 0 0 0 1 0 1 0 24.1641684% 0 0 1 0 0 0 0 0 0 0 0 0 37.6953340% 1 1 0 0 0 0 1 1 0 0 1 1 84.3301372% 1 0 1 1 0 0 0 0 1 0 0 1 15.9802272% 0 0 0 0 1 0 0 0 0 0 0 1 24.3884387% 0 1 0 0 0 0 0 0 0 0 0 1 59.6499337% 1 0 0 1 0 0 0 1 0 0 1 0 15.6328980% 1 0 1 0 0 0 1 0 0 1 0 0 44.2826163% 1 1 0 1 0 0 0 0 0 1 0 1 31.1481981%

[0199] For a FTS after 1stTreatment model, the following equation can be used with regard to the above variables: P(FTS = 1) = 1 / (1 + exp[-(0.52110+ 0.30445 ■ Sex_Steer+ 0.09533 ■ DOF_43_71- 0.80601 • DOF_gt71+ 0.11039 ■ LungScore_2+ 0.42144■ LungScore_3- 0.11304 ■ LungScore_4- 0.83853 ■ LungScore_5+ 0.27888 • BW_600_800+ 0.95066 ■ BW_800_1000+ 0.33230 ■ BW_gtl000- 0.87714 • Bline_gt3- 0.95324 •Moth_Yes)]), where “P(FTS = 1)” denotes the probability of a first treatment success for the cattle patient. The LateX / mathtype code for this formula is:P(FTS = 1) = 1 / (1 + exp[-(0.52110+ 0.30445 • Sex_Steer+ 0.09533 ■ DOF_43_71- 0.80601' DOF_gl7 l + 0.11039 ■ LungScore_2+ 0.42144 • LungScore_3- 0.11304 • LungScore_4- 0.83853 • LungScore_5+ 0.27888 ■ BW_600_800+ 0.95066 • BW_800_1000+ 0.33230 • BW_gtl000- 0.87714 ■ Bline_gt3- 0.95324 ■ Moth_Yes)]) and may also be depicted by:Table 9 below provides exemplary7data of 100 cattle whose probability of a FTS outcome aftersttreatment is calculated by the FTS after 1stTreatment model described above.0 0 0 0 0 0 0 1 0 0 0 0 68.9970203% 1 0 1 0 0 1 0 0 0 1 0 0 55.9417907%0 0 0 0 1 0 0 0 0 1 0 1 57.9714135% 1 0 1 0 0 0 0 0 0 1 0 1 35.4023461%1 1 0 0 1 0 0 0 1 0 1 0 80.4685431% 0 1 0 1 0 0 0 0 0 0 0 1 44.3635593%1 1 0 0 1 0 0 0 0 1 1 0 68.9435171% 1 0 0 0 0 0 0 1 0 0 0 0 75.1089234%0 1 0 1 0 0 0 1 0 0 0 1 51.3111993%1 0 0 0 0 1 0 0 0 1 0 0 73.9777032%0 1 0 0 1 0 0 0 1 O i l 53.9455298%1 1 0 0 0 1 0 0 0 0 1 0 48.2681930% 1 0 0 1 0 0 0 0 0 0 1 0 51.4695766% 1 0 0 1 0 0 0 1 0 0 0 0 77.1150681%1 1 0 1 0 0 0 0 0 O i l 31.0215931% 0 0 1 0 0 0 1 0 1 O i l 11.8871541%1 1 0 0 0 0 1 0 0 0 1 0 31.1141090% 0 0 1 1 0 0 0 0 0 O i l 11.8689413%1 0 1 0 0 0 1 0 0 0 1 0 15.4971385%0 1 0 0 0 0 1 0 0 1 1 1 15.1847980%0 0 1 0 0 0 0 0 0 1 0 1 28.7849806%1 0 1 0 0 0 1 0 1 O i l 15.4633822%0 0 0 0 0 1 0 0 1 0 0 1 60.0003574%0 0 0 0 0 0 0 0 0 1 1 1 27.3491428%0 0 1 1 0 0 0 0 1 0 1 0 47.4771443%1 0 1 0 1 0 0 1 0 0 1 0 46.0760855%0 0 1 1 0 0 0 0 0 0 1 0 25.8906463%0 0 1 1 0 0 0 0 1 0 0 0 68.4847600%1 1 0 1 0 0 0 0 0 0 1 0 53.8456399% 0 0 1 0 0 0 1 1 0 0 0 0 30.0575265%1 1 0 1 0 0 0 0 1 0 0 0 87.8886751% 1 1 0 0 0 0 1 0 0 1 1 1 19.5331858% 0 1 0 0 0 0 0 0 0 1 0 1 49.8872502%0 0 0 0 0 0 0 0 0 0 1 0 41.1918507%1 0 0 0 1 0 0 0 1 0 0 0 90.0038282%1 1 0 0 0 0 0 0 1 O i l 51.0288548% 0 0 1 0 0 0 0 0 0 0 1 0 23.8294989%1 0 0 0 0 1 0 0 1 O i l 45.8294625%1 0 0 0 1 0 0 1 0 0 0 0 82.1401242%0 0 0 0 0 1 0 1 0 O i l 24.1689333%0 0 1 0 0 1 0 0 0 1 0 1 26.5243671%0 0 1 1 0 0 0 0 0 0 0 0 45.6480401%1 0 0 0 1 0 0 0 1 0 0 0 90.0038282%0 0 1 0 0 1 0 0 1 O i l 21.7947102%0 0 0 0 0 1 0 0 1 0 1 0 61.8120899%0 1 0 0 0 0 1 1 0 0 0 1 28.9778511%85 1 0 0 0 0 0 0 0 0 1 1 0 56.9720281%86 0 0 0 0 0 1 0 0 0 1 1 1 25.1614512%87 0 0 0 1 0 0 0 0 0 1 0 1 50.2637476%88 1 0 1 0 1 0 0 0 0 0 1 1 19.9503561%89 0 0 1 0 0 1 0 0 0 0 1 1 9.7235136%90 1 0 1 0 0 0 1 0 0 0 1 0 15.4971385%91 0 0 1 0 0 0 1 0 1 0 1 0 25.9240463%92 0 0 1 0 1 0 0 0 1 0 1 0 55.2320273%93 0 0 1 0 0 0 0 0 0 0 0 0 42.9250437%94 1 1 0 0 0 0 1 1 0 0 1 1 18.7071844%95 1 0 1 1 0 0 0 0 1 0 0 1 53.1794541%96 0 0 0 0 1 0 0 0 0 0 0 1 49.7325026%97 0 1 0 0 0 0 0 0 0 0 0 1 41.6584574%98 1 0 0 1 0 0 0 1 0 0 1 0 58.3626857%99 1 0 1 0 0 0 1 0 0 1 0 0 38.0673628%100 1 1 0 1 0 0 0 0 0 1 0 1 60.1167004%

[0200] For a Chronic DNF model, the following equation can be used with regard to the above variables: P(DNF = 1) = 1 / (1 + exp[-(-2.2997- 0.1033 • Sex_Steer- 0.7109 • DOF 43 71+ 0.1397 • DOF_gt72+ 3.4370 • Bline_gt3+ 1.4459 • Moth_Yes)]), where “P(DNF = 1)” denotes the probability of a chronic DNF prognosis. The LateX / mathtype code for the formula is: P(DNF = 1) = 1 / (1 + exp [-(-2.2997- 0.1033 • Sex_Steer- 0.7109 • DOF 43 71+ 0.1397 • DOF_gt72+ 3.4370 • Bline_gt3+ 1.4459 • Moth_Yes)]) and may also be depicted by:

[0201] Table 10 below provides exemplary data of 100 cattle whose probability0 0 1 0 1 32.8693524% 1 0 1 0 1 30.6315846% 1 1 0 0 0 4.2537521% 0 0 1 0 1 32.8693524% 0 0 1 0 0 10.3400451% 1 0 0 0 1 27.7459199% 1 0 0 1 0 73.7690644% 1 1 0 1 0 58.0079560% 1 1 0 1 0 58.0079560% 1 0 1 0 1 30.6315846% 1 1 0 1 0 58.0079560% 1 0 0 1 1 92.2720667% 0 0 1 0 0 10.3400451% 0 0 0 1 0 75.7183571% 0 1 0 0 1 17.2973262% 1 1 0 1 0 58.0079560% 1 0 0 0 1 27.7459199% 1 1 0 0 0 4.2537521% 1 0 1 0 0 9.4208392% 0 0 1 1 0 78.1938677% 1 0 1 1 1 93.2112399% 1 0 1 0 0 9.4208392% 0 0 1 1 0 78.1938677% 0 1 0 1 0 60.5013695% 1 0 0 0 1 27.7459199% 0 1 0 0 0 4.6949291% 1 0 0 1 1 92.2720667% 1 0 1 1 1 93.2112399% 1 0 0 1 0 73.7690644% 1 1 0 0 1 15.8691013% 1 0 0 0 0 8.2944217% 0 1 0 1 0 60.5013695% 1 1 0 0 0 4.2537521% 1 0 1 0 1 30.6315846% 1 0 0 0 0 8.2944217% 1 0 1 0 0 9.4208392% 1 0 0 1 1 92.2720667% 0 0 0 0 0 9.1147810% 1 0 1 1 1 93.2112399% 0 1 0 1 0 60.5013695% 1 1 0 0 0 4.2537521% 0 0 0 0 0 9.1147810% 0 1 0 0 1 17.2973262% 0 1 0 1 0 60.5013695% 1 0 1 1 1 93.2112399% 0 1 0 1 1 86.6724182%80 1 1 0 0 0 4.2537521%81 1 0 1 1 0 76.3813157%82 1 1 0 1 o 58.0079560%83 1 0 1 1 1 93.2112399%84 0 0 1 1 1 93.8364473%85 1 0 1 1 0 76.3813157%86 1 0 1 1 0 76.3813157%87 0 1 0 1 0 60.5013695%88 0 1 0 1 1 86.6724182%89 0 1 0 1 1 86.6724182%90 1 1 0 1 1 85.4333267%91 1 0 1 0 0 9.4208392%92 0 1 0 0 0 4.6949291%93 0 0 1 0 0 10.3400451%94 0 0 0 0 0 9.1147810%95 1 0 0 1 0 73.7690644%96 1 1 0 0 0 4.2537521%97 1 0 1 1 0 76.3813157%98 0 0 0 1 1 92.9772503%99 0 0 0 1 0 75.7183571%100 1 0 1 0 1 30.6315846%EXAMPLE 9 - Using Targeted POCUS (T-POCUS) for Congestive Heart Failure Identification

[0202] Congestive heart failure (CHF) is an emerging syndrome in feedyard cattle. CHF is the cause of death in 5-7% of feedyard mortalities and this syndrome may play a contributing role in mortalities from other causes. Research illustrates this syndrome is found in many North American feedyards and common breeds and while higher altitudes may enhance the syndrome this disease is also found at low altitudes. The challenge with CHF is cattle are often not identified until the disease has progressed to end-course. There are no effective treatments for CHF and early identification allows producers to cull or remove affected animals prior to further animal welfare concerns or economic loss. Currently there is no definitive way to diagnose CHF antemortem and clinical signs are very similar to BRD cases. Distinguishing CHF from BRD is critical as several BRD therapies are available, but no effective treatments exist for CHF.

[0203] Using T-POCUS specifically to examine hepatic vasculature provides insight into the likelihood of CHF and the stage of progression. CHF in cattle is right sided resulting in liver congestion and hepatic vessel dilation: monitoring this dilation can be measured quickly and easily with T-POCUS as an entire liver scan does not need to be done but only the relevant subsection. Using this scanning technique the information from T- POCUS allows identification of CHF cases and can help formulate the prognosis. In general, the T-POCUS scan is accomplished using the same protocol as TT-POCUS. In brief, an ultrasound probe is utilized with fanning that targets the desired targeted area of the liver and the vascular structure thereof. Knowledge gained from the ultrasound can be used to diagnose CHF, predict CHF diagnosis, and / or prognosis.EXAMPLE 10- Using T-POCUS to Identify Traumatic Recticuloperitonitis

[0204] Traumatic reticuloperitonitis or reticulopericarditis (“hardware disease”,TRP) can present in animals of any age, but is a relatively common cause of mortality in feedyard cattle. This disease is caused when foreign material is ingested, pierces the reticulum, and incites infection in the peritoneum, pleural cavity, or pericardial sac. Typically the foreign material is hard and sharp in nature (e.g. metallic objects such as a nail). This disease can cause severe impacts and ultimately result in death. Some treatments are available including antibiotics or surgical flushing; however, in most cases the prognosis is very poor. Cattle with TRP present with similar clinical signs to BRD and CHF and distinguishing them from BRD and CHF allows the facility to implement appropriate preventative, treatment, and control measures to manage current cases and prevent new ones.

[0205] Using T-POCUS can rapidly and efficiently identify TRP cases by quick scans in two specific areas: the peritoneal region and identification of pleuritis in thepulmonary region. Confirming a TRP diagnosis allows the feedyard to make improved management decisions.EXAMPLE 11 - Using T-POCUS to Identify Gastrointestinal Functionality

[0206] Abdominal T-POCUS can be used to evaluate gastrointestinal (GI) functionality to identify7specific common cattle diseases such as ruminitis, bloat, acidosis, enteritis, and leaky gut. Several of these diseases (ruminitis, acidosis, and subsequent bloat) may be the result of dietary change or upset and when identified in groups of cattle could lead to changes in the ration, feed delivery, or amount fed. Understanding the specific diagnosis in individuals and groups of cattle provides valuable information to both health care and nutritional providers to adjust the ration accordingly. Abdominal T-POCUS can focus on key spots within the abdomen to evaluate GI functionality.

[0207] Diseases such as enteritis may be infectious and spread among individuals within a group. Using T-POCUS to identify’ impacts within the GI tract allows early identification and subsequent treatment.EXAMPLE 12 - Musculoskeletal T-POCUS to Determine Performance Parameters

[0208] Musculoskeletal T-POCUS involves utilizing the probe to scan musculature and fat to determine several specific findings relevant to predicted performance traits. The probe will be placed perpendicular to the longissimus dorsi muscle around the 12th to 13th ribs to measure a cross section through the backfat and muscle. Key findings to measure include thickness of backfat, size of the muscle, and expected marbling. These factors can be used combined with animal characteristics to determine the optimal finish for an individual animal based on desired carcass end-point based on physiologic maturity, expected carcass transfer or incremental cost of gain.

[0209] Serial scans of individual animals throughout the growth phase also provide additional information to understand differences in rate of fat and muscle deposition allowing prediction of number of days until cattle reach optimal end point for harvest. Using musculoskeletal T-POCUS once or as a serial scan can also be used to determine how to sort individual animals into specific feeding groups or most appropriate marketing methods.

Claims

CLAIMSWhat is claimed is:

1. A method of assessing the health status of lungs in a mammal comprising the steps of: performing an ultrasound procedure using an ultrasound probe in the thoracic region of the mammal to produce an ultrasound image therefrom; assessing the ultrasound image for at least one variable selected from the group consisting of an ultrasound lung score. A lines, B lines, moth sign, lung consolidation and any combination thereof, wherein said procedure and assessing is complete in less than 6 minutes.

2. The method of claim 1, wherein the at least one variable is scored according to a standard system correlating with the condition of the lung using that variable.

3. The method of claim 1. further comprising differentiating between interstitial pneumonia (IP) and non IP respiratory disease or infection in the mammal.

4. The method of claim 1. wherein said mammal is a bovine.

5. The method of claim 1. wherein said bovine is suspected of having respiratory- disease or infection prior to the ultrasound procedure.

6. The method of claim 1. wherein said ultrasound procedure includes fanning the probe in the thoracic region.

7. The method of claim 1, wherein the thoracic region includes the intercostal spaces between an 8th and an 11th rib on a right side of the mammal.

8. The method of claim 2, wherein the scoring of at least one variable is used to provide a probability of the mammal having IP.

9. The method of claim 8, wherein the scoring of more than one variable is combined using a formula to provide a more accurate probability7that the mammal has or does not have IP.

10. The method of claim 9, wherein the mammal is cattle and wherein the formula is P(Histo_IP = 1) = 1 / (1 + exp[-(0.5696- 0.3834 ■ Sex_Steer+ 2.1331 ■DOF 43 71+ 1.6642 • DOF_>71+ 1.3601 • ULS_3+ 0.9919 ■ ULS_4- 2.8504 ■ULS 5- 0.7569 • Bline_3_5- 1.4435 ■ Aline_>3)]), wherein, “P(Histo_IP = 1)” denotes the probability of a diagnosis of IP in the cattle patient, and wherein “Sex_Steer” refers to the sex of the cattle, with a value of ‘ 1 ’ denoting a steer and a value of ‘0’ denoting a heifer;DOF_43-71 refers to the cattle’s days on feed, with a value of ‘ 1 ’ denoting a 43- 71 days on feed and a value of ‘0’ denoting otherwise;DOF_>71 refers to the cattle’s days on feed, with a value of ‘ 1’ denoting greater than 71 days on feed and a value of ‘0’ denoting otherwise;ULS_3 refers to the ultrasound lung score (ULS) as described above, with a value of ‘ 1 ’ denoting a ULS of 3 and a value of ‘0’ denoting otherwise;ULS_4 refers to the ultrasound lung score (ULS) as described above, with a value of ‘ 1 ’ denoting a ULS of 4 and a value of ‘0’ denoting otherwise;Bline_3_5 refers to the count of B-lines, with a value of T denoting 3-5 B-lines and a value of ‘0’ denoting otherwise; andAline_>3 refers to the count of A-lines, with a value of ‘ 1’ denoting greater than 3 A-lines and a value of "O’ denoting otherwise.

11. The method of claim 1, further including at least one variable selected from the group consisting of days on feed (DOF), body weight (BW), number of treatments for respiratory disease, and any combination thereof, and wherein the assessingI l lprovides a probability of whether or not the mammal will finish the feed stage, or whether a first or subsequent treatment for respiratory disease will be successful.

12. The method of claim 11 wherein the scoring of more than one variable is combined using a formula to provide a more accurate probability that the mammal will or will not finish after a first treatment for respiratory7disease within a test period.

13. The method of claim 12, wherein the formula is P(DNF = 1) = 1 / (1 + exp[-(- 1.5210- 0.4811 • Sex_Steer+ 0.7798 ■ DOF_43_71+ 1.0185 • DOF_gl71 - 0.3213 ■ LungScore_2- 0.7426 • LungScore_3- 0.5706 ■ LungScore_4+ 1.1356 • LungScore_5- 0.9507 • BW_600_800- 1.4869 • BW_800_1000- 0.3817 • BW_gtl 000+ 1.5883 • Bline_gt3+ 1.1321 ■ Moth_Yes)]), where “P(DNF = 1)” denotes the probability of the cattle patient not finishing within the test period; and wherein■‘Sex_Steer” refers to the sex of the cattle, with a value of 'I ’ denoting a steer and a value of ‘0’ denoting a heifer;DOF_43-71 refers to the cattle’s days on feed, with a value of ‘ 1 ’ denoting a 43- 71 days on feed and a value of 'O’ denoting otherwise;DOF_>71 refers to the cattle’s days on feed, with a value of ‘ 1’ denoting greater than 71 days on feed and a value of ‘0’ denoting otherwise;DOF_gt7 l refers to the cattle’s days on feed, with a value of ‘ 1’ denoting greater than 71 days on feed and a value of ‘0’ denoting otherwise;Lung Score 3 refers to the ultrasound lung score (ULS) as described above, with a value of ' I ' denoting a ULS of 3 and a value of "O’ denoting otherwise;Lung Score _4 refers to the ultrasound lung score (ULS) as described above, with a value of ‘ 1 ’ denoting a ULS of 4 and a value ofL0’ denoting otherwise;Lung Score_5 refers to the ultrasound lung score (ULS) as described above, with a value of ‘ 1 ’ denoting a ULS of 5 and a value of ‘0’ denoting otherwise;BW_600_800 refers to the body weight of the cattle, with a value of ‘ T denoting a body weight between 600 and 799 pounds and a value of ‘O' denoting otherwise;BW_800_1000 refers to the body w eight of the cattle, with a value of ‘ 1 ’ denoting a body weight between 800 and 999 pounds and a value of ‘0’ denoting otherwise; BW_gt 1000 refers to the body w eight of the cattle, with a value of ‘1’ denoting a body weight greater than 1000 pounds and a value of ‘0’ denoting otherwise;Bline_gt3 refers to the count of B-lines, with a value of ‘1 ’ denoting greater than 3 B-lines and a value of ‘0’ denoting otherwise; andMoth_Y es refers to the presence or absence of a moth sign, with a value of ‘ 1 ’ denoting the presence of a moth sign in the corresponding cattle ultrasound, and a value of ‘0’ denoting the absence of a moth sign.

14. The method of claim 1 1 wherein the scoring of more than one variable is combined using a formula to provide a more accurate probability that the mammal will respond to a subsequent treatment after a first treatment.

15. The method of claim 14, wherein the formula is P(FTS = 1) = 1 / (1 + exp[- (0.52110+ 0.30445 • Sex_Steer+ 0.09533 • DOF_43_71- 0.80601 • DOF_gt71 + 0.11039 • LungScore_2+ 0.42144 • LungScore_3- 0.11304 • LungScore_4- 0.83853 • LungScore_5+ 0.27888 • BW_600_800+ 0.95066 • BW_800_1000+ 0.33230 • BW gtlOOO- 0.87714 • Bhne_gt3- 0.95324 • Moth_Yes)]), where “P(FTS = 1)” denotes the probability of a first treatment success for the cattle patient; and wherein”Sex_Steer" refers to the sex of the cattle, with a value of ‘T denoting a steer and a value of ‘O' denoting a heifer;DOF_43-71 refers to the cattle’s days on feed, with a value of ‘ 1 ’ denoting a 43- 71 days on feed and a value of ‘0’ denoting otherwise;DOF_gt7 l refers to the cattle’s days on feed, with a value of ‘T denoting greater than 71 days on feed and a value of ‘O' denoting otherwise;LungScore_2 refers to the lung score, with a value of ‘ 1 ’ denoting a lung score of 2 and a value of ‘0’ denoting otherwise.Lung Score _3 refers to the ultrasound lung score (ULS) as described above, with a value of ‘ 1 ’ denoting a ULS of 3 and a value of ‘0’ denoting otherwise;Lung Score _4 refers to the ultrasound lung score (ULS) as described above, with a value of ‘ 1’ denoting a ULS of 4 and a value of ‘0’ denoting otherwise;Lung Score_5 refers to the ultrasound lung score (ULS) as described above, with a value of ‘ 1’ denoting a ULS of 5 and a value of ‘0’ denoting otherwise;BW_600_800 refers to the body weight of the cattle, with a value of ‘ 1 ’ denoting a body weight between 600 and 799 pounds and a value of ‘0’ denoting otherwise; BW_800_l 000 refers to the body weight of the cattle, with a value of ‘ 1 ’ denoting a body weight between 800 and 999 pounds and a value of ‘0’ denoting otherwise; BW^gtl 000 refers to the body weight of the cattle, with a value of ‘ 1’ denoting a body weight greater than 1000 pounds and a value of ‘0’ denoting otherwise;Bline_gt3 refers to the count of B-lines, with a value of ‘1 ’ denoting greater than 3 B-lines and a value of ‘0’ denoting otherwise; andMoth_Yes refers to the presence or absence of a moth sign, with a value of ‘ 1 ’ denoting the presence of a moth sign in the corresponding cattle ultrasound, and a value of ‘0’ denoting the absence of a moth sign.

16. The method of claim 11 wherein the scoring of more than one variable is combined using a formula to provide a more accurate probability7that the mammal will respond to a subsequent treatment after more than one treatment.

17. The method of claim 16, wherein the formula is: P(DNF = 1) = 1 I (1 + exp[-(- 2.2997- 0.1033 ■ Sex_Steer- 0.7109 ■ DOF_43_71+ 0.1397 ■ DOF_gt72+ 3.4370 ■ Bline_gt3+ 1.4459 ■ Moth_Yes)]), where “P(DNF = 1)” denotes the probability7of a chronic DNF prognosis; and wherein:“Sex_Steer” refers to the sex of the cattle, with a value of ‘ 1 ’ denoting a steer and a value of ‘0’ denoting a heifer;DOF_43-71 refers to the cattle’s days on feed, with a value of ‘ 1 ’ denoting a 43- 71 days on feed and a value of ‘0’ denoting otherwise;DOF_gt72 refers to the cattle’s days on feed, with a value of ‘1’ denoting greater than 72 days on feed and a value of ‘0’ denoting otherwise;Bline_gt3 refers to the count of B-lines, with a value of ‘1 ’ denoting greater than 3 B-lines and a value of ‘0’ denoting otherwise; andMoth_Y es refers to the presence or absence of a moth sign, with a value of ' 1 ’ denoting the presence of a moth sign in the corresponding cattle ultrasound, and a value of ‘0’ denoting the absence of a moth sign.

18. A method of rapidly diagnosing a cattle patient for respiratory disease using targeted thoracic point-of-care ultrasound (TT-POCUS). the method comprising: scanning a thoracic region of a lung of the cattle patient with an ultrasound probe and thereby generating an ultrasound image; identifying a count of A-lines and a count of B-lines in the ultrasound image; identifying a presence or absence of severe lung consolidation; and determining a rapid diagnosis for the cattle patient, wherein:the rapid diagnosis is no respiratory disease when the B-line count is fewer than three and the absence of severe lung consolidation has been identified; the rapid diagnosis is interstitial pneumonia when the B-line count is greater than or equal to three; and the rapid diagno sis is respirator}' disease that is not interstitial pneumonia when the presence of severe lung consolidation has been identified.

19. The method of Claim 18 further comprising determining a presence or absence of one or more abnormal pleural findings.

20. The method of Claim 19 wherein the one or more abnormal pleural findings include a moth sign.

21. The method of Claim 18 wherein if the count of A-lines is less than three, the rapid diagnosis is one of either interstitial pneumonia or respiratory disease that is not interstitial pneumonia.

22. The method of Claim 18 wherein the thoracic region is within intercostal spaces between an 8thand an 11thrib on a right side of the cattle patient.

23. A method of determining treatment of a respiratory disease in cattle subject comprising distinguishing between IP and non-IP and treating the cattle appropriately based on the probability determined in claim 18.

24. The method of claim 23, wherein if the cattle subject is diagnosed with interstitial pneumonia according to Claim 18. the method of treatment comprises administering anti-inflammatory and supportive care, and wherein if the cattle patient is diagnosed with respiratory disease that is not interstitial pneumonia according to Claim 18, the method of treatment comprises antimicrobial therapy.

25. A method of rapidly assessing a cattle patient for respiratory' disease using targeted thoracic point-of-care ultrasound (TT-POCUS). the method comprising: scanning a thoracic region of a lung of the cattle patient with an ultrasound probe and thereby generating an ultrasound image; identifying a count of A-lines and a count of B-lines in the ultrasound image; identify ing a presence or absence of severe lung consolidation; assigning a ultrasound lung score (ULS) to the ultrasound image on the basis of the count of A-lines, the count of B-lines, and the presence or absence and the severity of lung consolidation, wherein the ULS is a numerical score ranging from 1 to 5, further wherein: the ULS score is 1 when the count of B-lines is fewer than three; the ULS score is 2 when the count of B-lines is three or greater and the B-lines have a thickness of less than 7 mm; the ULS score is 3 when the B-lines have a thickness greater than 7 mm; the ULS score is 4 when the B-lines have a thickness greater than 7 mm and the presence of one or more abnormal pleural findings has been determined; and the ULS score is 5 when severe lung consolidation has been determined.

26. The method of Claim 25. further comprising determining a presence or absence of one or more abnormal pleural findings.

27. The method of Claim 26 wherein the one or more abnormal pleural findings include a moth sign.

28. The method of Claim 25 wherein the thoracic region is within intercostal spaces between an 8thand an 11thrib on a right side of the cattle patient.

29. The method of Claim 25 further comprising a step of rapidly generating a disease diagnosis, wherein: if the ULS score is 1 and the A-line count is three or more, the disease diagnosis is no respiratory disease; if the ULS score is 2, 3 or 4, the disease diagnosis is interstitial pneumonia; and if the ULS score is 5, the disease diagnosis is respiratory' disease that is not interstitial pneumonia.

30. A method of treatment of the cattle subject, wherein if the cattle patient is diagnosed with interstitial pneumonia according to Claim 29, the method of treatment comprises administering anti-inflammatory and supportive care, and wherein if the cattle patient is diagnosed with respiratory disease that is not interstitial pneumonia according to Claim 29, the method of treatment comprises antimicrobial therapy.

31. The method of Claim 25 further comprising a step of a first treatment failure (FTF) prognosis after a first treatment, wherein the cattle patient is displaying one or more clinical signs of respiratory disease, further wherein: the FTF prognosis is a probability that the first treatment will not successfully treat the cattle patient; if the ULS score is 1. 2, or 3. the FTF prognosis is less than 50%; if the ULS score is 4. the FTF prognosis is greater than 50%; and if the ULS score is 5. the FTF prognosis is greater than 65%.

32. The method of Claim 25 further comprising a step of a did not finish (DNF) prognosis after a first treatment, wherein the cattle patient is displaying one or more clinical signs of respiratory disease, further wherein: the DNF prognosis is a probability' that the cattle patient will die or be culled within60 days after the first treatment; if the ULS score is 1, 2, or 3, the DNF prognosis is less than 35%; if the ULS score is 4, the DNF prognosis is less than 45%; and if the ULS score is 5, the DNF prognosis is greater than 65%.

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