System and method for assessing animals considering auscultation and evaluation of physiological responses in various environments

Abstract

The invention includes a system and method for predicting the performance of production animals by analysis of heart and lung sounds to determine likelihoods the animals will develop BRD or other diseases or ailments. Vital signs of animals are recorded during an adrenergic sympathetic “flight or fight” situation. A cardio-pulmonary rate ratio is determined for each animal by dividing a normalized adjusted heart rate value by a normalized adjusted respiratory value. From the ratios calculated for each animal in a group, a ratio range is established. Ratio values at a lower end of the ratio range indicate higher relative respiration rates and poor lung performance due to disease. Ratio values at an upper end of the range may indicate low cardiac output and an inability to tolerate rapid weight gain. Ratio values at either end of the range may indicate compromised cardio-pulmonary function. Animals can be further classified by weight, and the ratio values within weight classes are used to generate probabilities for BRD or other diseases.

Claims

1. A method for assessing animals considering physiological responses to stress, comprising: exposing an animal to a controlled environment known to induce sympathetic adrenergic stress reactions; recording heart and respiration rates of the animal during said reactions; determining a cardiopulmonary rate ratio for the animal expressed as the heart rate divided by the respiration rate; determining a range of ratios for a plurality of animals within an observed population of animals; determining a plurality of values for corresponding ratios indicating respiratory compensating responses (CPR-R), cardiac compensating responses (CPR-C), and normal compensating responses (CPR-N); determining a likelihood the animal will develop a disease taking into account said ratios of values for said compensating responses (CPR-R), cardiac compensating responses (CPR-C), and normal compensating responses (CPR-N); and providing treatment to the animal corresponding to the likelihood the animal will develop the disease.

2. The method, as claimed in claim 1, further including: determining a weight for the animal; and determining a likelihood the animal will develop a disease taking into the weight of the animal.

3. The method, according to claim 1, wherein: said cardiopulmonary rate ratio is determined by dividing a final normalized adjusted heart rate value by a final normalized adjusted respiratory value.

4. The method, according to claim 1, further including: conducting an auscultation analysis for the animal; and providing further treatment to the animal considering results of said auscultation analysis.

5. The method, according to claim 4, wherein: said auscultation analysis further includes designation of a lung score for the results corresponding to the analysis.

6. The method, according to claim 1, wherein: said treatment includes at least one of administration of an antibiotic, administration of a selected nutrition program, or combinations thereof.

7. A method of establishing a cardio-pulmonary ratio (CPR) value for at least one animal within a population of similarly situated animals in a selected environment considering physiological responses to stress therein and using the CPR value for treatment, said method comprising: (a) convert empirical distributions of breath and heart rates of an animal into a standard normal distribution curve; (b) generate CPR norms by: (i) capturing the animal's breath and heart rate; (ii) calculating a normalized breath rate cumulative density value (0 to 1) using determined transformations; (iii) calculating a normalized heart rate cumulative density value (0 to 1) using the transformation determined; (iv) calculating a ratio of the heart rate normalized value to a breath rate normalized value; (v) calculating a normalized CPR value cumulative density value (0 to 1) using the transformation equation determined; and (vi) assigning a CPR category from a value using category determiners as follows: If equal to or less than 0.15, then the animal is categorized as a respiratory compensator (CPR-R); If equal to or greater than 0.85, then the animal is categorized as a cardiac compensator (CPR-C); and If greater than 0.15 and less than 0.85, then the animal is categorized as a non-compensator/normal (CPR-N); (c) reviewing determined CPR categories for the animal selected for treatment; and (d) conducting treatment for the selected animal.

8. The method, according to claim 7, further including: conducting an auscultation analysis for each animal; and providing further treatment to the animal considering results of said auscultation analysis.

9. The method, according to claim 8, wherein: said auscultation analysis further includes designation of a lung score for the results corresponding to the analysis.

10. The method, according to claim 7, wherein: said treatment includes at least one of administration of an antibiotic, administration of a selected nutrition program, or combinations thereof.

11. The method, according to claim 7, wherein: said CPR norms are developed by: (i) calculating a raw CPR value from a value of a corresponding normalized heart rate divided by a value of the breath rate and applied only to animals with values greater than 0 on both normalized breath and normalized heart rates; and (ii) taking the raw CPR values calculated and transform the empirical distribution of the raw CPR values into a standard normal distribution for use to determine an animal's CPR value as a location on a cumulative normal density curve giving a value between 0 and 1.

12. The method, according to claim 7, wherein: said standard normal distribution curve is determined by: (i) recording breath and heart rates of a large sample of similar animals that are similar in breed, weight and health status; (ii) for breath rates, transform the empirical distribution into a standard normal distribution for use to determine the animals' breath rate location on a cumulative normal density curve giving a value between 0 and 1; and (iii) for heart rates, transform the empirical distribution into a standard normal distribution for use to determine the animals' heart rate location on a cumulative normal density curve giving a value between 0 and 1.

13. A method for assessing animals considering physiological responses to stress, comprising: exposing animals to a controlled environment known to induce sympathetic adrenergic stress reactions; recording heart and respiration rates of the animals during said reactions; determining cardiopulmonary rate ratios for the animals expressed as the heart rate divided by the respiration rate; determining a range of ratios for a plurality of animals within an observed population of animals, said range of ratios including groups of values for respiratory compensating responses (CPR-R), cardiac compensating responses (CPR-C), and normal compensating responses (CPR-N); providing treatment to the animals corresponding to a likelihood the animals will develop a disease by analyzing the cardiopulmonary rate ratios; conducting an auscultation analysis for each animal; and providing treatment to the animals further considering results of said auscultation analyses.

14. The method, according to claim 13, further including: determining a group of first values for ratios indicating the respiratory compensating responses (CPR-R); determining a group of second values for ratios indicating the cardiac compensating responses (CPR-C); determining a group of third values for ratios indicating the normal compensating responses (CPR-N); and determining likelihoods animals will develop a disease taking into account said ratios within said first, second, or third groups of values.

15. A method for assessing animals considering physiological responses to stress, comprising: exposing an animal to a controlled environment known to induce sympathetic adrenergic stress reactions; recording heart and respiration rates of an animal during said reactions; determining a cardiopulmonary rate ratio for the animal expressed as the heart rate divided by the respiration rate; determining a range of ratios for a plurality of animals within an observed population of animals; determining a lung score based on an auscultation analysis conducted on the animal to determine a state of health of the animal; and providing treatment to the animal corresponding to a likelihood the animal will develop a disease by analyzing the cardiopulmonary rate ratio and the determined lung score.

16. The method, as claimed in claim 15, wherein: said determined lung score is between 1 and 5, each numerical lung score corresponding to a different health state of the animal.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 is a visual depiction of example data points on a graph which illustrate how CPR values of the present invention can be used with auscultation data to identify non-BRD pathology;

(2) FIG. 2 is another depiction of example data points on a graph which illustrate how CPR values of the present invention can be used with auscultation data to identify risk stratification of BRD as classified according to lung score groups;

(3) FIG. 3 is a visual depiction of example data points showing lung lesions identified through ultransonography plotting by absolute values of heart and respiratory rates, and more particularly illustrating why lung ailments such as lung lesions cannot be identified simply by recording such data;

(4) FIG. 4 is another visual depiction of example data points of lung lesions identified through ultransonography which illustrates plotting of heart and respiratory rates, but in which the rates for each axis are normalized to a bell-shaped curve, in which normalization as a component of CPR values of the present invention assist to better separate suspect animals in terms of those having lung abnormalities such as lung lesions and those without such abnormalities;

(5) FIG. 5 is yet another visual depiction of example data points of lung lesions identified by ultrasonography which illustrates plotting of heart and respiratory rates according to FIG. 4, with further information added to the graph including separation of the graph into zones corresponding to CPR-R, CPR-N, and CPR-C categories, and the relative abundance (more than twice the rate) of lung lesions in the CPR-R zone compared to the zones for CPR-N and CPR-C;

(6) FIG. 6 is another visual depiction of example data points on a graph that illustrates how knowing the percentage of CPR-C categorized animals upon arrival to a location such as a feed yard can provide a more accurate prediction as to performance of animals group within a pen within the feed yard.

DETAILED DESCRIPTION

(7) The creation of CPR values for the present invention is optimized if large samples of data are used to establish norms that indicate true high and low values of vital signs within a species for a given environment. CPR values can be determined for any species of animal in which preferably large samples of data are used to establish norms, and in which a preferred protocol for obtaining respiration and cardiac rates are to be taken from the same type of stress-induced environment for each animal. More specifically, the CPR values are more reliable when each animal of the population is exposed to the same or similar stress induced environment.

(8) Set forth below is an example method/protocol of the invention for establishing a CPR formula or mathematical expression for a species, such as a bovine species:

(9) Convert empirical distribution of breath and heart rates of a given species and breed into standard normal distribution curves ˜N(μ=0,σ=1).

(10) Determine or capture the breath and heart rates of a large sample of similar animals; similar in breed, weight and health status. Preferably obtain captured data for many animals.

(11) For breath rates; using the data in step 1a, transform the empirical distribution into a standard normal distribution ˜N(μ=0,σ=1) which can be used to determine an animal's breath rate location on a cumulative normal density curve giving a value between 0 and 1.

(12) For heart rates; using the data in step 1a, transform the empirical distribution into a standard normal distribution ˜N(μ=0,σ=1) which can be used to determine an animal's heart rate location on a cumulative normal density curve giving a value between 0 and 1.

(13) Develop CPR norms.

(14) For each animal in the sample; a raw CPR value is calculated from the value of their normalized heart rate (step 1c) divided by the value of the breath rate (step 1b). This is applied only to those animals with values greater than zero on both the normalized breath and normalized heart rates.

(15) Taking the ratio values created in step 2a, transform the empirical distribution of the raw ratio values into a standard normal distribution ˜N(μ=0,σ=1) which can be used to determine an animal's CPR score or value as a location on a cumulative normal density curve giving a value between 0 and 1.

(16) Utilization of CPR Norms

(17) Capture an animal's breath and heart rate.

(18) Calculate the normalized breath rate cumulative density value (0 to 1) using the transformation equation determined in step 1b.

(19) Calculate the normalized heart rate cumulative density value (0 to 1) using the transformation equation determined in step 1c.

(20) Calculate the ratio of the heart rate normalized value (step 3c) to the breath rate normalized value (step 3b).

(21) Calculate the normalized CPR value cumulative density value (0 to 1) using the transformation equation determined in step 2b.

(22) Assign the CPR category from value in step 3e using the following determination cut-off points.

(23) If equal to or less than 0.15, then animal is a categorized as a respiratory compensator (CPR-R).

(24) If equal to or greater than 0.85, then animal is categorized as a cardiac compensator (CPR-C).

(25) If greater than 0.15 and less than 0.85, then animal is categorized as a non-compensator/normal (CPR-N).

(26) Based upon the foregoing explanation, one example formula to describe a CPR score or value may be expressed as follows:
CPR=e{circumflex over ( )}(−10−(ASINH(((e{circumflex over ( )}(−(0.6+(1.*LN(((LN(HeartRate)−4))/((6−LN(HeartRate)))))){circumflex over ( )}2/2)/e{circumflex over ( )}(−(0.3−LN(((BreathRate−3))/((100−BreathRate)))){circumflex over ( )}2/2)+0.03))/0.00001))){circumflex over ( )}2/2)√2π

(27) Referring now to the Figures, FIG. 1 is a visual depiction of data points on a graph which illustrate how CPR values of the present invention can be used with auscultation data to identify non-BRD pathology. More specifically, FIG. 1 shows example data concerning classification of CPR scores or values for a group of observed animals. The background information on the animals is that they arrived to a location, such as a feedlot, and each animal in the group was previously treated with antibiotics. The number of animals in the population/observed group is 1,069 animals. Each of the animals were evaluated in terms of obtaining auscultation data, such as a corresponding lung score as disclosed in the above mentioned U.S. application Ser. No. 13/442,569. Each of the animals were also evaluated by generating corresponding CPR scores, and specific data points shown in the graph correspond to groups of animals within the population that had the corresponding CPR scores. As further shown in the graph, the two general categories of CPR evaluations recorded include CPR-N and CPR-C. On the right side of the graph along a lung score of 4, as expected, there was a fairly high case fatality rate for those animals which had a high lung score and which were determined as having respiratory compensating or cardiac compensating CPR scores. However, the graph also shows a high case fatality rate for one group of animals on the left side of the graph along a lung score of 1. Although these animals had presumably healthy respiratory systems because of the low lung score, there was still a high fatality rate that cannot be explained by just an evaluation of auscultation data. This elevated case fatality rate is only observable as a function of the determination of CPR scores for this group of animals, and it can therefore be deduced that that relatively high fatality rate was due to non-BRD pathology. These animals may also have been observed as not responding to antibiotic use; however, a determination of potential other diseases is simply not possible with auscultation analysis. Therefore, one proposed or prudent treatment that could take place for this group of animals is to withdraw the animals from any antibiotics, and to memorize the animal from other conditions which may contribute to something other than BRD, such as a metabolic disorder. In summary, FIG. 1 is therefore intended to illustrate that although an animal may have a favorable lung score, increased fatality rates for these types of animals can be difficult to predict unless there's some type of other measurement parameter which may provide a caregiver, a more thorough and comprehensive diagnostic analysis of the state of health of the animal.

(28) Referring to FIG. 2, another graph shows how CPR values of the present invention can be used with auscultation data to identify risk stratification of BRD as classified according to lung score groups. As reflected in this figure, the data points correspond to a study of groups of animals characterized as either CPR-N or CPR-R, and the population or sample was 15,937 head of cattle. There are few conclusions that can be drawn from a review of these recorded data points. First, the graph shows that there was an increased fatality rate for animals across all ranges of the lung scores when comparing animals classified as CPR-R versus CPR-N. In other words, the fatality rate increased for animals having a respiratory compensating response as opposed to those animals that did not have a respiratory compensating response, and this increase occurred even with animals having low lung scores, that is, those animals in which presumptive diagnoses could be made regarding BRD by review of only auscultation data. As shown in the graph, for observed animals having a lung score of 1, there was a 64.8% increase in mortality rates when comparing CPR-R versus CPR-N; for observed animals having a lung score of 2, there was a 37.6% increase in mortality rates when comparing CPR-R versus CPR-N; for observed animals having a lung score of 3, there was a 11.5% increase in mortality rates when comparing CPR-R versus CPR-N; for observed animals having a lung score of 4, there was a 55.5% increase in mortality rates when comparing CPR-R versus CPR-N; and for observed animals having a lung score of 5, there was a 75.4% increase in mortality rates when comparing CPR-R versus CPR-N. Another general conclusion that can be drawn from the data shown in this graph is that some animals classified in one lung score with CPR-R should be considered for a different treatment protocol because the increased mortality rate places or qualifies him for consideration for treatment in a different lung score/category. More specifically, the animals categorized as CPR-R with a lung score of 2 had a slightly higher mortality rate than those animals classified as CPR-N and a lung score of 3. Therefore, a caregiver may wish to alter the treatment protocol for these animals to correspond to the treatment being given for animals having a lung score of 3.

(29) Referring to FIG. 3, this graph provides a visual depiction of data points which illustrates plotting of heart and respiratory rates, and more particularly illustrates why lung ailments such as lung lesions cannot be identified simply by evaluating heart and respiratory rates. More specifically, FIG. 3 illustrates heart and respiratory rate data for a group of animals that were studied to detect the presence of lung lesions. The presence of lung lesions negatively impacts cardiovascular performance and typically corresponds to long-term decreases in production performance. The study included 210 cow calves under 150 pounds, and the animals were analyzed to obtain both lung scores and CPR scores. The presence of lung lesions in the animals were verified by conducting ultrasounds giving CPR a diagnostic sensitivity for lung lesions of 0.82 at a peripheral lung depth of 2 cm or more. The animals with lung lesions as compared to those without lung lesions were indistinguishable in terms of identifiable differences in heart or respiration rates. In other words, by review of only heart and respiratory rates, no conclusions could be made as to differences between the animals. Therefore, it is apparent that a traditional auscultation analysis could not assist in easily distinguishing animals for purposes of detecting lung lesions.

(30) Referring to FIG. 4, another visual depiction of data points is shown on a graph illustrating plotting of heart and respiratory rates; however, respiratory rates are normalized to a bell-shaped curve, and normalization of the respiratory rates provides an improved indication as to how to distinguish between animals that may have lung lesions. In summary, FIG. 4 illustrates that normalizing the data for the breath rates produces a bell-shaped curve which can be used as more useful information regarding the impact of lung lesions because it can be seen that the lung lesions are much more prevalent in the top 50% of the bell curve as compared to the bottom 50% area. By normalizing the absolute rates, meaningful relationships can be defined between breath rate values beyond their absolute differences. That is; a breath rate difference between 65/min and 55/min is 10/min but the same difference between, for example, 95/min and 85/min is a much rarer occurrence as 95/min is at the tail end of the upper distribution and can be considered almost a statistical outlier. In summary, respiratory rates above a value of 0.50 show much greater density in terms of the number of animals who were detected having lung lesions according to the results of the verifying ultrasound procedures. Normalization is a component of determining CPR values of the present invention and therefore, normalization in this figure indicates that by placing relative frequencies of breath rate occurrences, interpretation of the information is fundamentally changed by adding a new dimension to the data.

(31) Referring to FIG. 5, this is yet another visual depiction of data points on a graph which illustrates plotting of heart and respiratory rates according to FIG. 4, with further information added to the graph including separation of the graph into zones corresponding to CPR-R, CPR-N, and CPR-C categories. More specifically, FIG. 5 shows a distribution of minimum lesion depths (2.0 cm) as measured by the confirmatory ultrasounds in which a separation of the graph into the zones provides valuable information regarding those animals that should be targeted for treatment. The dotted line extending from the origin in an upwards manner to approximately 0.20 on the horizontal axis separates data points for those animals classified as CPR-R and CPR-N. The data points for the group of animals to the left of this line are classified as CPR-R, while the data points for the group of animals to the right of this line are classified as CPR-N. The dotted line extending from the origin in a more flat manner and terminating near 1.0 on the horizontal axis separates data points for the animals classified as CPR-N and CPR-C. The data points for the group of animals above and to the left of this line are the animals classified as CPR-N, while the data points for the group of animals below and to the right of this line are the animals classified as CPR-C. One general conclusion that can be made from the use of CPR data in this graph is a prediction of lung lesions known to be associated with lower performance and higher morbidity, and this group of animals correspond to those classified in CPR-R. As shown, the animals classified in this group have more than twice the rate of lung lesions than the other two classified groups of animals. Accordingly, these animals could be selectively treated on arrival to minimize infection spread and minimize re-infection rates. Use of this treatment approach supports best treatment practices to include good antibiotic stewardship and judicious use only for those animals with a diagnosis. Other treatment approaches may be adopted considering other diseases that can be diagnosed early by classification of animals from corresponding CPR categories.

(32) FIG. 6 is another graph that illustrates how knowing the percentage of CPR-C categorized animals upon arrival to a location such as a feed yard can provide a more accurate prediction as to performance of animals group within a pen within the feed yard. More specifically, FIG. 6 illustrates that CPR is capable of predicting closeout average daily gain (ADG) by evaluating, for example, a pen lot considering animals characterized as CPR-C. This figure shows to groups of animals, namely, steers and mixed. The vertical axis shows average daily gain at closeout, and the effect of more pronounced cardiac compensating response animals which have a lower average daily gain as compared to those animals which have less pronounced cardiac compensating responses. This figure also shows the difference between steers and mixed, and the overall increased ability for steers to gain weight as compared to mixed animals across a large range of CPR-C values. From an economic standpoint, the linear relationships that can be seen in the graph for both steers and mixed in terms of average daily gain, one may more accurately predict when groups of animals may actually attain desired weight gain goals. Therefore, a more accurate prediction in terms of closeout dates can provide numerous advantages.

(33) It should be understood that the method of the invention can be executed within a data processing system in which the mathematical calculations conducted for the CPA scores and other mathematical calculations relating to auscultation data are manipulated, stored, and made available to a user in various user interface displays. For storage and calculation of data, this can be achieved on a data processing network or within respective standalone data computer systems, depending upon how a user may wish to use and secure the data. It is further contemplated that functionality associated with displaying the results of CPA scores and corresponding auscultation data can be presented to a user on conventional user interface displays, such as screen displays on personal computers, screen displays on mobile devices, and others. FIGS. 1-6 represent exemplary graphs that may be used as displays for data to a user to evaluate and compare groups of animals according to various observed characteristics, to include not only CPR and auscultation data, but any other measured parameters such as animal weight, days on feed, days on antibiotic, and others.