US20050224009A1 - Echocardiographic measurements as predictors of racing success - Google Patents

Echocardiographic measurements as predictors of racing success Download PDF

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US20050224009A1
US20050224009A1 US10/521,087 US52108705A US2005224009A1 US 20050224009 A1 US20050224009 A1 US 20050224009A1 US 52108705 A US52108705 A US 52108705A US 2005224009 A1 US2005224009 A1 US 2005224009A1
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measurements
racehorse
candidate
horses
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Jeffrey Seder
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EQUINE BIOMECHANICS AND EXERCISE PHYSIOLOGY Inc
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0858—Detecting organic movements or changes, e.g. tumours, cysts, swellings involving measuring tissue layers, e.g. skin, interfaces
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/46—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient
    • A61B8/461—Displaying means of special interest
    • A61B8/463—Displaying means of special interest characterised by displaying multiple images or images and diagnostic data on one display
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63K—RACING; RIDING SPORTS; EQUIPMENT OR ACCESSORIES THEREFOR
    • A63K3/00—Equipment or accessories for racing or riding sports
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q99/00—Subject matter not provided for in other groups of this subclass

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  • the present invention is directed to methods for screening candidate racehorses, and improving the likelihood of selecting a candidate that will become a successful racehorse.
  • racehorse candidates One has only to look to the results of this year's Kentucky Derby to see how inaccurate the selection of racehorse candidates can be: the race was won by Funny Cide, once a $75,000 purchase, while numerous horses from the same crop that sold for much more, even ten or more times as much, failed to even win a single maiden race.
  • the present invention provides methods of screening a racehorse candidate and selecting a racehorse candidate likely to become a high-earner racehorse.
  • the method for screening racehorse candidates includes the step of obtaining one or more measurements, including echocardiographic measurements, from a racehorse candidate and comparing the measurements to a collection of corresponding measurements from a group of horses.
  • the methods include the step of obtaining a measurement of the width of the ventricular septal wall of a racehorse candidate and comparing it to ventricular septal wall width measurements from a group of horses of similar age, sex, and weight.
  • the methods of the present invention include the step of obtaining a measurement of the splenic cross-sectional area of a racehorse candidate and comparing it to splenic cross-sectional area measurements from the group of horses.
  • the methods of the present invention further comprise the steps of obtaining one or more measurements selected from the cross-sectional area of the left ventricle in diastole, the cross-sectional area of the left ventricle in systole, the body size, or the interventricular septal wall width of the candidate racehorse and comparing these additional measurements to corresponding measurements from the group of horses.
  • the methods of the present invention may further include the step of selecting a racehorse candidate if its ventricular septal wall width measurement is greater than the mean ventricular septal wall width of the group of horses.
  • the methods of the present invention may further include the step of selecting a racehorse candidate if one or more of its measurements, e.g., ventricular septal wall width, left ventricle cross sectional area in diastole or systole, body size, splenic cross sectional area, is greater than the mean corresponding measurement from the group of horses of similar age, weight and sex.
  • the methods of the present invention may further include the step of selecting a racehorse candidate if one or more of its measurements, e.g., ventricular septal wall width, left ventricle cross sectional area in diastole or systole, body size, splenic cross sectional area, is in the 75 th percentile or higher when compared to corresponding measurements from the group of horses.
  • a racehorse candidate if one or more of its measurements, e.g., ventricular septal wall width, left ventricle cross sectional area in diastole or systole, body size, splenic cross sectional area, is in the 75 th percentile or higher when compared to corresponding measurements from the group of horses.
  • the methods of the present invention may also include the step of rejecting a racehorse candidate if one or more of its measurements, e.g., ventricular septal wall width, left ventricle cross sectional area in diastole or systole, body size, splenic cross sectional area, is not in the 75 th percentile or higher when compared to corresponding measurements from the group of horses.
  • a racehorse candidate e.g., ventricular septal wall width, left ventricle cross sectional area in diastole or systole, body size, splenic cross sectional area
  • FIG. 2 A left parasternal short-axis echocardiogram of the left ventricle at end diastole from a 2-year-old Thoroughbred filly with a resting heart rate below 40 bpm obtained from the left cardiac window with a 3.5 MHz probe.
  • the dotted line traces the endocardial border of the left ventricle at the end of diastole.
  • FIG. 5 LVD (mm 2 ) measurements for colts and fillies at weights of 850 to 1150 pounds.
  • FIG. 6 LVS (mm 2 ) measurements for colts and fillies at weights of 850 to 1150 pounds.
  • FIG. 7 SW (mm) measurements for colts and fillies at weights of 850 to 1150 pounds.
  • FIG. 8 PS (pct.) measurements for colts and fillies at weights of 850 to 1150 pounds.
  • FIG. 9 Mean weight percentiles for high earner and low earner horses at ages 12 to 28 months.
  • FIG. 10 Mean LVD percentiles for high earner and low earner horses at ages 12 to 28 months.
  • the present invention provides, inter alia, methods of obtaining certain physical measurements of a candidate racehorse's heart.
  • the present invention also relates, in part, to the discovery that the size of a horse's spleen can also be used as a predictor of the horse's future racing ability. Accordingly, the present invention provides methods of screening a racehorse candidate on the basis of its splenic cross-sectional area.
  • the present invention provides screening methods that further include the step of determining the physical size of a horse.
  • the physical size or body size of a horse or “HTWT” is determined by multiplying the height and the weight of the horse.
  • the heart of a horse is measured in terms of one or more of the following variables: cross sectional area of the left ventricle in diastole (LVD), cross sectional area of the left ventricle in systole (LVS), ventricular septal wall width, and percent change in ventricular area per stroke (PS).
  • LDD cross sectional area of the left ventricle in diastole
  • LMS cross sectional area of the left ventricle in systole
  • PS percent change in ventricular area per stroke
  • ventricular septal wall width refers to the width of the septum dividing the right and left ventricles.
  • a particularly preferred ventricular septal wall measurement involves a particular cardiac structure that runs from the endocardial edge of the right ventricular free wall, at the point where the wall meets the interventricular septum, through the interventricular septum, to the point of attachment of the moderator band in the left ventricle, as shown for example in FIG. 3 .
  • This structure may be readily identified in a left parasternal short-axis view, preferably obtained at end diastole, although other views may also be used to obtain measurements of this structure. Measurements of this particular structure are referred to herein as the “interventricular septal wall structural thickness” or “SW”.
  • LVS and LVD may be determined by freezing, for example, a left parasternal short-axis two dimensional echocardiographic ultrasound image at the peak of systole, and the end of diastole, respectively, and tracing the internal perimeter of the left ventricular chamber using calipers on the ultrasound machine. The area inside the tracing is then calculated based on a pixel count (512 ⁇ 512 for total screen). Many commercially available diagnostic ultrasound machines include software capable of measuring a circumscribed area in this fashion.
  • cardiac measurements cited herein may be measured by any method known to those of skill in the art, as may be described, for example, in one or more of the following: Voros et al., (1990) Equine Vet. J . p. 392-397; Weyman, A. E. (1982) Cross-sectional echocardiography, Lea & Febiger, Philadelphia, p. 497-504; Wyatt, et al. (1979) Circulation 60, p. 1104-1113; O'Grady et al. (1986) Vet. Radiol. 27, p. 34-49; Henry, W. L., et al. (1980) Circulation 62, p. 212-217; Feigenbaum, H.
  • Equine vet. J. 30 (2) p. 117-122; Slater, J. D. and Herrtage, M. E. (1995), Equine vet. J ., Suppl. 19, p. 28-32; Marr, et al., Equine vet. J ., Suppl. 30, p. 131-136; Young et al., (1998), Equine vet. J. 30 (2) p. 117-122; Young, L. E. (1999) Equine vet. J ., Suppl. 30, p. 195-198; Pascoe, J. R., et al., (1990) Equine vet. J, Suppl. 30, p. 148-152.
  • PS LVD - LVS LVD ⁇ 100.
  • PS may be correlated with the volume of blood that is ejected from the heart per stroke, at rest.
  • the splenic cross sectional area or “SPLN” is obtained by producing a cross sectional image of the horse's spleen, and determining the cross sectional area of same, as discussed above with regard to the LVD and LVS measurements.
  • LVS, LVD, and SW are measured from a left parasternal short axis echocardiogram of the left ventricle of the horse at end systole and end diastole.
  • the echocardiogram can be obtained from the left cardiac window using a 3.5 MHz probe.
  • the ultrasound transducer can be held in the right hand with the cursor facing caudally.
  • the left forelimb can be advanced slightly and the transducer can be placed in the 4th or 5th left intercostal space, at a level just dorsal to the point of the olecranon.
  • the transducer beam can be directed perpendicular (horizontal) to the longitudinal cardiac axis.
  • the resulting image provides a nearly circular appearance to the left ventricular lumen.
  • the moderator band(s), papillary muscle, chordae tendinae and septal leaf of the mitral valve can be identified and used as intracardiac reference points to obtain reproducible cardiac images in the same tomographic plane.
  • alternate echocardiogram views may be obtained and the cardiac and splenic size measured from the alternate views, e.g., right parasternal short axis view, left or right parasternal long axis view, apical views. Typically three to five cardiac cycles are measured for each echocardiographic measurement.
  • Short axis images can be projected according to international terminology based on the recommendations of the American Society of Echocardiography (Henry 1980, supra; Feigenbaum 1986, supra). Short axis images recorded from the left side of the chest can be projected as though the tomographic planes are viewed from the base to the apex of the heart.
  • the accuracy of the measurements may be compromised when a horse's heart is beating very quickly. For example, in a very rapidly beating heart, it may be difficult to accurately freeze the image at peak systole, or at end diastole. Accordingly, it is preferred that the measurements be taken when the horse's resting heart rate is less than about 50 beats per minute, with a resting heart rate at the time of examination of less than about 40 beats per minute being even more preferred.
  • the present invention also provides a collection of measurements from a group of horses for comparison with those obtained from the candidate racehorse.
  • selected measurements are obtained from a group of horses, as discussed above.
  • the database preferably includes measurements of each of the variables LVD, LVS, PS, HTWT, SPLN, and ventricular septal wall width, particularly the variable SW, as defined above.
  • the group of horses includes at least about 1000 individuals, with a group of greater than 5000 horses being preferred. Even more preferably, the database will include measurements of at least about 7500 individuals.
  • racehorse candidates are sold as yearlings or two-year olds, it is preferred that the individuals making up the group of horses range in age from about 12 months to about 28 months of age chronologically. It has been found, however, that cardiac measurements vary, depending on age, sex, and weight, making it difficult to compare horses on the basis of cardiac measurements alone, without adjusting for the effects of these parameters. Accordingly, an adequate comparative sample of horses of about the same age, sex and weight as the racehorse candidate is preferred.
  • the group of horses used for the comparison are of the same breed as the racehorse candidate, and that breed is preferably Thoroughbred.
  • the term “about the same age, sex and weight” means that the individuals making up the collection of horses used for comparative purposes have a date of birth within about 30 days of the racing candidate, are of the same genetic gender, and have a weight of within about 25 pounds of the racing candidate.
  • the database will include cardiac measurements of at least about 35 horses of the same age, sex and weight as the racehorse candidate. More preferably, the cardiac measurements of the candidate racehorse are compared to a database that includes cardiac measurements of at least about 75, and even more preferably at least about 150, and still more preferably, at least about 300 horses of the same age, sex and weight as the racehorse candidate. As a result of such a large statistical sample, greater accuracy and predictive ability may be achieved by the methods described herein.
  • the cardiac measurements become independent of a horse's age, sex and weight.
  • the racehorse candidate may be assigned a percentile rank for each measurement variable, e.g., LVD, LVS, SW, SPLN, HTWT, and/or PS, as compared to a statistically significant sample of horses of about the same age, sex and weight.
  • This comparison can be used to predict the racing ability of the candidate racehorse, e.g., whether the candidate racehorse will be more likely to become a high earner or lower earner. For example, as described more fully in the examples to follow, by selecting a racehorse candidate having a ventricular septal wall width greater than the mean ventricular septal wall width of a group of horses of about the same age, sex and weight, and/or rejecting a racehorse candidate that has a septal wall width less than the mean, the likelihood of selecting a high earner racehorse is significantly improved.
  • the odds of selecting a high earner racehorse are further improved by selecting a racehorse candidate that has a ventricular septal wall width that is in the 75 th percentile or higher, and/or rejecting a candidate that has a septal wall width that is lower than the 75 th percentile.
  • the odds of selecting a low earner are decreased by selecting a horse that has a ventricular septal wall width greater than the mean ventricular septal wall width of a group of horses of about the same age, sex and weight, with the odds of selecting a low earner even further reduced by selecting a racehorse candidate that has a ventricular septal wall width that is in the 75 th percentile or higher.
  • certain embodiments of the present invention are directed to methods that comprise selecting horses that exhibit one or more of the aforementioned measurements greater than the mean measurement, and preferably fall in the 75 th percentile or higher, than is seen in a group of horses of about the same age, sex and weight.
  • the methods of the present invention can also be used to increase the likelihood of selecting a horse that will be a high earner router, as opposed to a high earner sprinter.
  • a “sprint” is a race of 1 mile (8 furlongs) or less, while a “route” race is one of at least about 8.5 furlongs.
  • the present invention also provides methods for maintaining a horse registry system or database.
  • a system can be managed using bioinformatics.
  • Bioinformatics is the study and application of computer and statistical techniques to the management of biological information.
  • the present invention provides a method for populating a database with the biological information obtained using the methods of the present invention.
  • a database can be populated with LVD, LVS, PS, HTWT, SPLN and ventricular septal wall width measurements from a group of horses whose racing abilities are known.
  • the racing ability of racehorse candidates can be predicted as described above, e.g., by comparing measurements from racehorse candidates to corresponding measurements from a group of horses of about the same age, sex, and weight and ranking the horses according to each measurement. Measurements from the racehorse candidates can be optionally entered into the database as well.
  • the present invention also provides an apparatus for automating the methods of the present invention, the apparatus comprising a computer and a software system capable of comparing and standardizing echocardiographic and other measurements from horses.
  • the data is inputted in computer-readable form and stored in computer-retrievable format.
  • the present invention also provides computer-readable medium encoded with a data set comprising profiles, e.g., LVD, LVS, PS, HTWT, SPLN, and ventricular septal wall width measurements, of horses known to be high earners, low earners, high earner routers, or high earner sprinters.
  • the information in the data set can be used for comparison purposes in order to improve one's odds of selecting a higher earner racehorse. It can also be used by handicappers or others in order to evaluate horses for betting purposes.
  • the methods described herein for obtaining certain measurements from horses provides information which can be used to determine the racing ability of candidate racehorses.
  • data generated from the methods of this invention is suited for manual review and analysis, in a preferred embodiment, prior data processing using high-speed computers is utilized.
  • the invention also provides for the storage and retrieval of a collection of profiles and comparisons in a computer data storage apparatus, which can include magnetic disks, optical disks, magneto-optical disks, DRAM, SRAM, SGRAM, SDRAM, RDRAM, DDR RAM, magnetic bubble memory devices, and other data storage devices, including CPU registers and on-CPU data storage arrays.
  • a computer data storage apparatus can include magnetic disks, optical disks, magneto-optical disks, DRAM, SRAM, SGRAM, SDRAM, RDRAM, DDR RAM, magnetic bubble memory devices, and other data storage devices, including CPU registers and on-CPU data storage arrays.
  • This invention also preferably provides a magnetic disk, such as an IBM-compatible (DOS, Windows, Windows 95/98/2000, Windows NT, OS/2, etc.) or other format, e.g., Linux, SunOS, Solaris, AIX, SCO, Unix, VMS, MV, Mactinosh etc., floppy diskette or hard (fixed, Winchester) disk drive, comprising a bit pattern encoding data collected from the methods of the present invention in a file format suitable for retrievable and processing in a computerized comparison or relative quantification method.
  • a magnetic disk such as an IBM-compatible (DOS, Windows, Windows 95/98/2000, Windows NT, OS/2, etc.) or other format, e.g., Linux, SunOS, Solaris, AIX, SCO, Unix, VMS, MV, Mactinosh etc., floppy diskette or hard (fixed, Winchester) disk drive, comprising a bit pattern encoding data collected from the methods of the present invention in a file format suitable
  • the invention also provides a network, comprising a plurality of computing devices linked via a data link, such as an Ethernet cable (coax or 10BaseT), telephone line, ISDN line, wireless network, optical fiber, or other suitable signal transmission medium, whereby at least one network device comprises a pattern of magnetic domains and/or charge domains comprising a bit pattern encoding data acquired from the methods of the invention.
  • a data link such as an Ethernet cable (coax or 10BaseT), telephone line, ISDN line, wireless network, optical fiber, or other suitable signal transmission medium, whereby at least one network device comprises a pattern of magnetic domains and/or charge domains comprising a bit pattern encoding data acquired from the methods of the invention.
  • the invention also provides a method for transmitting data that includes generating an electronic signal on an electronic communications device, such as a modem, ISDN terminal adapter, DSL, cable modem, ATM switch, or the like, wherein the signal includes (in native or encrypted format) a bit pattern encoding data collected using the methods of the present invention.
  • an electronic communications device such as a modem, ISDN terminal adapter, DSL, cable modem, ATM switch, or the like, wherein the signal includes (in native or encrypted format) a bit pattern encoding data collected using the methods of the present invention.
  • the invention provides a computer system for performing the methods of the present invention.
  • a central processor is preferably initialized to load and execute the computer program for alignment and/or comparison of results.
  • Data is entered into the central processor via an I/O device.
  • Execution of the computer program results in the central processor retrieving the data from the data file.
  • the target data or record and the computer program can be transferred to secondary memory, which is typically random access memory.
  • a central processor can be a conventional computer; a program can be a commercial or public domain molecular biology software package; a data file can be an optical or magnetic disk, a data server, or a memory device; an I/O device can be a terminal comprising a video display and a keyboard, a modem, an ISDN terminal adapter, an Ethernet port, a punched card reader, a magnetic strip reader, or other suitable I/O device.
  • Performance records All horses used to predict performance had race records through their three-year-old year. Race records included race date, racetrack, race number, distance raced, level of race, claiming price, finish position and earnings. Horses that raced outside of North America were identified as “foreign,” and their race records were not used, since they were often incomplete or difficult to compare with North American records on the basis of dollar value or race level.
  • Pre-selection biases were reflected in the percentage of stakes winners among horses measured. For example, midway through the 1990 foal crop's ten-year-old year, 2.3 percent had won a stakes race ( Thoroughbred Times , Jul. 22, 2000, p. 51). In contrast, 6.7 percent of horses measured for this study, and which were not known to have raced outside of North America, won a stakes race before they were four years old.
  • the depth of display varied from 15 to 25 centimeters depending on the size of the horse.
  • the ultrasound recorder was equipped with electronic calipers that were used to measure the stored images at the time of the examination.
  • SAS release 6.12 SAS Institute, Cary, N.C.
  • Windows NT Microsoft
  • Universe IBM
  • Windows 2000 Microsoft
  • the server was a Dell 2300 Poweredge (Dell, Atlanta, Ga.) with dual 450 MHz Intel Pentium processors, running Windows 2000.
  • the ultrasound transducer was held in the right hand with the cursor facing caudally.
  • the left forelimb was advanced slightly and the transducer was placed in the 4th or 5th left intercostal space, at a level just dorsal to the point of the olecranon. From this position, a left parasternal short axis view could be obtained by directing the transducer beam perpendicular (horizontal) to the longitudinal cardiac axis.
  • the image provided a nearly circular appearance to the left ventricular lumen.
  • the moderator band(s), papillary muscle, chordae tendinae and septal leaf of the mitral valve were identified and then used as intracardiac reference points to obtain reproducible cardiac images in the same tomographic plane.
  • Short axis images were projected according to international terminology based on the recommendations of the American Society of Echocardiography (Henry 1980, Feigenbaum 1986). Short axis images recorded from the left side of the chest were projected as though the tomographic planes were viewed from the base to the apex of the heart.
  • the ultrasound technician estimated HEIGHT and WEIGHT based solely on visual inspection and prior experience.
  • the variable HTWT which was the product of height times weight, was used in this research as an estimate of overall body size.
  • the ultrasound technician a life-long horseperson, trained horses prior to this research. While a trainer, she had an on-site horse scale in a 40-stall training facility and took daily weight measurements of horses, and compared scale results to weight tape measurements. Alternatively, a five rating category system was used to describe height and weight.
  • the horses were divided on the basis of weight or height into the following five categories: well below average (at least 1.0 standard deviation below the mean), below average (from 0.5 to 1.0 standard deviations below the mean), average (within 0.5 standard deviations of the mean), above average (from 0.5 to 1.0 standard deviations above the mean), and well above average (at least 1.0 standard deviation above the mean).
  • Each weight and height measurement was assigned a whole number from 1 to 5, with 1 equal to “well below average” and 5 equal to “well above average.” HTPLUSWT was created as the sum of these weight and height ratings, providing an overall physical size estimate.
  • Each horse's cardiac measurements i.e., LVD, LVS, SW, and PS
  • Subject comparisons were limited to within ⁇ 1 year of the measurement date in order to minimize the possible effects of gradual small changes in calibration, methodology and external variables acting on the subjects.
  • Examples of external variables that may have changed over time and affected measurements include sales preparation techniques of horses at auctions, steroid use, growth hormones, wear and tear on equipment, etc.
  • Standardized scores could be difficult to interpret because, while they generally ranged from ⁇ 3 to +3, they tended to congregate around zero. It seems easier to understand that a horse is in the 70 th percentile compared to his peers than to know that his standardized score is 0.55.
  • Variation (or differences) between cardiac measurements is caused by a combination of within- and between-subject variation.
  • Within-subject variation sometimes called measurement error, indicates how accurately or reproducibly the technician and equipment measures a given variable (hearts and horses are moving targets).
  • Between-subject variation is the range of expected differences among a particular variable in the general population that isn't due to error. Between-subject variation accounted for 84-92% of variation in cardiac measurements in this study, while within-subject variation accounted for 8-16% of variation.
  • Measurement variability was calculated for LVD, LVS, and SW among 1,464 horses measured in 1999. These cardiac measurements were repeated at least three times within a period of a few minutes. [1,571 horses were measured in 1999. Those excluded from this variability study lacked at least three measurements for LVD, LVS, or SW because of auction conditions, during which the technician may have lacked time to repeat measurements, could not sustain a resting heart rate (or behavioral cooperation), or reported only the average.]
  • Table 5 summarizes between-subject variation (s B ) and within-subject variation (s W ) and shows some basic statistical equations used.
  • Column 1 lists the variables studied.
  • Column 2 lists the mean value of each variable among all 1,464 horses in this part of the study.
  • Column 3 lists between-subject variation, which is the standard deviation associated with the mean reported in Column 2.
  • Column 4 lists within-subject variation.
  • Column 5 lists total variation.
  • Column 6 lists the percentage of total variation due to within-subject variation (or measurement error).
  • S B 2 and S W 2 are mean squared error terms from the between- and within-subject groups studied.
  • FIG. 4 compares LVD for colts vs. fillies, and is typical of sex-related differences. Most growth curves were described well (R 2 ⁇ 0.90) by second-degree polynominal equations, as shown on the graphs. The growth curves should be limited to application over the period from 12 through 27 months of age for which they were calculated (i.e., not used to estimate average LVD at 32 months of age).
  • Anomalies appeared in the data patterns of cardiac measurements versus age at 20 and 21 months of age. These horses were primarily measured during October through December, between the timing of select yearling and select two-year-old auctions. Horses often enter training during those interim months. Training regimens, and thus each heart's response to training, likely varied greatly during this time (Young, 1999). Puberty may play a role among fillies at this age. Most horses were measured during this period at private farms, without any pre-selection based on conformation or pedigree. The ratio of colts to fillies (60% colts to 40% fillies) in this study closely matches those at auctions. This ratio may favor colts because breeding farms keep some of the best-bred, best-conformed fillies for their breeding programs. Therefore, relative to auctions, the fillies seen at private farms may be of higher quality, overall, since they may include the best-bred, best-conformed fillies that never make it to auctions.
  • T-tests compared high vs. low earners of combined sexes and ages, using data normalized for sex, age and size. Significant differences (P-values ⁇ 0.0001) existed between high and low earners for all of the cardiac parameters listed in Table 9, except for PS. Stepwise analysis, as discussed in this paper, identified SW or SPLN, LVS and HTWT as the most significant discriminant variables when differentiating between high and low earners. High earners were defined as horses that raced at least three times, with earnings per start of at least $10,000. TABLE 9 T-tests - Percentiles (Data Adjusted for Age, Sex and Weight) High Earners (Earnings Per Start ⁇ $10,000) vs.
  • T-tests also compared high earner routers vs. high earner sprinters of combined sexes and ages, using data standardized for horses of the same age, sex and size. Significant differences (P-values ⁇ 0.05) existed between high earner routers and sprinters for the cardiac variables of LVD, LVS, WEIGHT, HEIGHT and HTWT, as shown in Table 10. Stepwise analysis, as discussed in this paper, identified LVD, LVS, HTWT and PS as the most significant discriminant variables when differentiating between high earner routers and sprinters. High earner routers raced at least three times at distances of at least 8.5 furlongs, with earnings per start at those route distances of at least $10,000.
  • Stepwise analysis was conducted for colts, fillies and combined sexes, using percentiles for the variables: LVD, LVS, SW, PS, SPLN and HTWT (HTWT is the product of height times weight).
  • Stepwise analysis was used to identify statistically significant variables that could differentiate between groups of horses categorized as high and low earners, defined as:
  • stepwise analysis identified the following significant variables (listed in order of statistical significance):
  • stepwise analysis identified the following significant variables (listed in order of statistical significance):
  • Discriminant analysis was used to classify high earners vs. low earners, and high earner routers vs. high earner sprinters, as defined in the stepwise analysis section.
  • a classification threshold is the minimum acceptable probability (as defined by the model user) required to classify a horse into a particular group. Thus, no horse was classified into a group unless the models assigned it at least a 50% probability of belonging to that group. Generally, the higher the threshold, the better the models performed (i.e., a horse with a 70% high earner probability was more likely to be a high earner than a horse with a lower probability. As the threshold increases for a particular group, the models generally misclassify more members of that group. At public auctions, a high “high earner” threshold would minimize the chances of buying poor performers (Type II errors), while increasing the chances of rejecting good performers (Type I errors).
  • Z-statistics were computed to determine the reliability of discriminant results using the formula below (shown for high earners):
  • Z H P H post - P H pre P H pre ⁇ ( 1 - P H pre ) N CH post
  • the model parameters were:
  • Non-Blind Test A non-blind test is one in which the horses classified by a model were used to create the model. Thus, the models “saw” those horses before.
  • a non-blind test is the best-case scenario of how well a model performs.
  • a blind-test is one in which the horses classified by a model were not used to create the model. Thus, the models did not “see” those horses before.
  • the first table presents non-blind test results based on all horses available for the study.
  • the second table presents non-blind test results based on horses with names beginning with the letters A-M.
  • the third table presents blind-test results, for which the A-M model was used to classify horses with names beginning with the letters N-Z, which the models did't seen previously.
  • Each table presents summary statistics as described below:
  • the pre-model probability is the ratio of all Group A or Group B horses to the total number of horses in the model. This is the probability, using a random selection technique without statistically created models, of correctly classifying a Group A or Group B horse. This probability is shown as a Ratio and a Percent. For example, if there are 7 Group A horses and 93 Group B horses, there is a 7% probability of randomly selecting a Group A horse. For Group A horses, this would be shown as a ratio of 7/100 and as a percent of 7.00.
  • Post-Model Probability Discriminating between two groups (A and B) the post-model probability is the ratio of Group A or Group B horses correctly classified by the models to the total number of horses classified by the statistically created models as Group A or Group B horses. This is the probability with discriminant models of correctly classifying Group A or Group B horses.
  • a discriminant model classifying the same 100 horses might classify 25 horses into Group A, of which 5 horses actually belonged to Group A. In this case, the ratio for Group A horses would be 5/25, or 20 percent.
  • the discriminant models improved the odds of correctly identifying Group A horses from 7% without models to 20% with models.
  • they improved the odds of correctly classifying Group B horses from 93% without models to 73/75, or 97.3% with models.
  • the P-value was listed corresponding to the Z-statistic computed for each model.
  • Table 14-Table 16 summarize discriminant results for non-blind and blind tests of high earners and low earners, comprised of colts and fillies combined, that had raced at least three times (i.e., had three “starts”). High earners earned at least $10,000 per start and low earners earned $2,000 or less per start. The improvement associated with discriminant modeling was statistically significant for both high and low earners for all groups studied (P-values ⁇ 0.0027).
  • Non-Blind A-Z Table 14 shows that among 1,479 total horses, non-blind discriminant models improved the odds of correctly classifying high earners from 28.26% without models to 37.32% with models. They improved the odds of correctly classifying low earners from 71.74% without models to 79.57% with models. The improvement associated with discriminant modeling was statistically significant for both high and low earners (P-values ⁇ 0.0001). TABLE 14 Discriminant Model Results - High Earners vs. Low Earners Non-Blind Tests -- Combined Sexes - Names Starting with Letters A-Z Pre-Model Post-Model Probability Probability Category Ratio Pct. Ratio Pct. P-Value High Earners 418/1479 28.26 256/686 37.32 0.0000 Low Earners 1061/1479 71.74 631/793 79.57 0.0000
  • Non-Blind A-M Table 15 shows that among horses with names beginning with the letters A-M, non-blind discriminant models improved the odds of correctly classifying high earners from 27.75% without models to 37.65% with models. They improved the odds of correctly classifying low earners from 72.25% without models to 80.80% with models. The improvement associated with discriminant modeling was statistically significant for both high and low earners (P-values ⁇ 0.0001). TABLE 15 Discriminant Model Results - High Earners vs. Low Earners Non-Blind Tests -- Combined Sexes - Names Starting with Letters A-M Pre-Model Post-Model Probability Probability Category Ratio Pct. Ratio Pct. P-Value High Earners 245/883 27.75 154/409 37.65 0.0000 Low Earners 638/883 72.25 383/474 80.80 0.0000
  • Table 17-Table 19 summarize discriminant results for high vs low earners among colts.
  • Table 17 shows that among 880 colts, non-blind discriminant models improved the odds of correctly classifying high earners from 26.70% without models to 34.96% with models. They improved the odds of correctly classifying low earners from 73.30% without models to 80.47% with models. The improvement associated with discriminant modeling was statistically significant for both high and low earners (P-values ⁇ 0.0004).
  • TABLE 17 Discriminant Model Results - High Earners vs. Low Earners Non-Blind Tests - Colts - Names Starting with Letters A-Z Pre-Model Post-Model Probability Probability Category Ratio Pct. Ratio Pct. P-Value High Earners 235/880 26.70 143/409 34.96 0.0002 Low Earners 645/880 73.30 379/471 80.47 0.0004
  • Non-Blind A-M Table 18 shows that among colts with names beginning with the letters A-M, non-blind discriminant models improved the odds of correctly classifying high earners from 26.47% without models to 33.33% with models. They improved the odds of correctly classifying low earners from 73.53% without models to 79.51% with models. The improvement associated with discriminant modeling was statistically significant for both high and low earners (P-values ⁇ 0.0226). TABLE 18 Discriminant Model Results - High Earners vs. Low Earners Non-Blind Tests - Colts - Names Starting with Letters A-M Pre-Model Post-Model Probability Probability Category Ratio Pct. Ratio Pct. P-Value High Earners 140/529 26.47 82/246 33.33 0.0147 Low Earners 389/529 73.53 225/283 79.51 0.0226
  • Table 20-Table 22 summarize discriminant results for high vs. low earners among fillies.
  • Non-Blind A-Z Table 20 shows that among 599 fillies, non-blind discriminant models improved the odds of correctly classifying high earners from 30.55% without models to 42.22% with models. They improved the odds of correctly classifying low earners from 69.45% without models to 79.03% with models. The improvement associated with discriminant modeling was statistically significant for both high and low earners (P-values ⁇ 0.0002).
  • TABLE 20 Discriminant Model Results - High Earners vs. Low Earners Non-Blind Tests - Fillies - Names Starting with Letters A-Z Pre-Model Post-Model Probability Probability Category Ratio Pct. Ratio Pct. P-Value High Earners 183/599 30.55 114/270 42.22 0.0000 Low Earners 416/599 69.45 260/329 79.03 0.0002
  • Non-Blind A-M Table 21 shows that among fillies with names beginning with the letters A-M, non-blind discriminant models improved the odds of correctly classifying high earners from 29.66% without models to 44.16% with models. They improved the odds of correctly classifying low earners from 70.34% without models to 81.50% with models. The improvement associated with discriminant modeling was statistically significant for both high and low earners (P-values ⁇ 0.0005).
  • Table 23-Table 25 summarize discriminant results for high earner routers vs. sprinters. Table 23 shows that among 314 high earner horses, non-blind discriminant models improved the odds of correctly classifying routers from 42.68% without models to 55.03% with models. They improved the odds of correctly classifying sprinters from 57.32% without models to 68.48% with models. The improvement associated with discriminant modeling was statistically significant for both routers and sprinters (P-values ⁇ 0.0037). TABLE 23 Discriminant Model Results - High Earner Routers vs. High Earner Sprinters Non-Blind Tests - Combined Sexes - Names Starting with Letters A-Z Pre-Model Post-Model Probability Probability Category Ratio Pct. Ratio Pct. P-Value Routers 134/314 42.68 82/149 55.03 0.0023 Sprinters 180/314 57.32 113/165 68.48 0.0037
  • Table 26-Table 28 summarize discriminant results for non-blind and blind tests of high earners and low earners, comprised of colts and fillies combined, that had raced at least three times (i.e., had three “starts”). High earners earned at least $10,000 per start and low earners earned $2,000 or less per start. The improvement associated with discriminant modeling was statistically significant for both high and low earners for all groups studied (P-values ⁇ 0.0002).
  • Non-Blind A-Z Table 26 shows that among 1,430 total horses, non-blind discriminant models improved the odds of correctly classifying high earners from 28.32% without models to 37.78% with models. They improved the odds of correctly classifying low earners from 71.68% without models to 79.95% with models. The improvement associated with discriminant modeling was statistically significant for both high and low earners (P-values ⁇ 0.0001). TABLE 26 Discriminant Model Results - High Earners vs. Low Earners Non-Blind Tests - Combined Sexes - Names Starting with Letters A-Z Pre-Model Post-Model Probability Probability Category Ratio Pct. Ratio Pct. P-Value High Earners 405/1430 28.32 252/667 37.78 0.0000 Low Earners 1025/1430 71.68 610/763 79.95 0.0000
  • Non-Blind A-M Table 27 shows that among horses with names beginning with the letters A-M, non-blind discriminant models improved the odds of correctly classifying high earners from 27.87% without models to 37.47% with models. They improved the odds of correctly classifying low earners from 72.13% without models to 80.39% with models. The improvement associated with discriminant modeling was statistically significant for both high and low earners (P-values ⁇ 0.0001). TABLE 27 Discriminant Model Results - High Earners vs. Low Earners Non-Blind Tests - Combined Sexes - Names Starting with Letters A-M Pre-Model Post-Model Probability Probability Category Ratio Pct. Ratio Pct. P-Value High Earners 238/854 27.87 148/395 37.47 0.0000 Low Earners 616/854 72.13 369/459 80.39 0.0001
  • Table 29-Table 31 summarize discriminant results for high vs. low earners among colts.
  • Non-Blind A-Z Table 29 shows that among 859 colts, non-blind discriminant models improved the odds of correctly classifying high earners from 26.66% without models to 34.89% with models. They improved the odds of correctly classifying low earners from 73.34% without models to 80.75% with models. The improvement associated with discriminant modeling was statistically significant for both high and low earners (P-values ⁇ 0.0004).
  • TABLE 29 Discriminant Model Results - High Earners vs. Low Earners Non-Blind Test - Colts - Names Starting with Letters A-Z Pre-Model Post-Model Probability Probability Category Ratio Pct. Ratio Pct. P-Value High Earners 229/859 26.70 142/407 34.89 0.0002 Low Earners 630/859 73.34 365/452 80.75 0.0004
  • Non-Blind A-M Table 30 shows that among colts with names beginning with the letters A-M, non-blind discriminant models improved the odds of correctly classifying high earners from 26.45% without models to 34.58% with models. They improved the odds of correctly classifying low earners from 73.55% without models to 80.58% with models. The improvement associated with discriminant modeling was statistically significant for both high and low earners (P-values ⁇ 0.0078). TABLE 30 Discriminant Model Results - High Earners vs. Low Earners Non-Blind Test - Colts - Names Starting with Letters A-M Pre-Model Post-Model Probability Probability Category Ratio Pct. Ratio Pct. P-Value High Earners 137/518 26.45 83/240 34.58 0.0042 Low Earners 381/518 73.55 224/278 80.58 0.0078
  • Table 32-Table 34 summarize discriminant results for high vs. low earners among fillies.
  • Non-Blind A-Z Table 32 shows that among 571 fillies, non-blind discriminant models improved the odds of correctly classifying high earners from 30.82% without models to 42.01% with models. They improved the odds of correctly classifying low earners from 69.18% without models to 79.14% with models. The improvement associated with discriminant modeling was statistically significant for both high and low earners (P-values ⁇ 0.0002).
  • TABLE 32 Discriminant Model Results - High Earners vs. Low Earners Non-Blind Test - Fillies - Names Starting with Letters A-Z Pre-Model Post-Model Probability Probability Category Ratio Pct. Ratio Pct. P-Value High Earners 176/571 30.82 113/269 42.01 0.0001 Low Earners 395/571 69.18 239/302 79.14 0.0002
  • Table 35 shows the percentage of horses that earned at least $10,000 per racing start among horses grouped by physical size and heart size. Overall, 13.3 percent of the horses in this study's sample earned at least $10,000 per start. TABLE 35 Percentage of Horses that Earned at least $10,000 Per Start Based on Percentiles for Individual Variables Percentiles 0-25% 25-50% 50-75% 75-100% HTWT 7.6 12.8 14.5 17.8 LVD 11.6 11.1 13.4 17.5 LVS 11.4 11.8 13.9 16.3 SW 10.8 13.1 13.1 16.3 PS 14.3 11.4 14.0 13.3 Average* 10.4 12.2 13.7 17.0 *Average was calculated excluding PS, which wasn't usually predictive.
  • Table 35 shows that as physical size and heart size measurements increased, except for PS, so did the percentage of high earners. This table shows that 17.8% of horses with HTWT in the 75-100% percentile range earned at least $10,000 per start. The percentage of horses that earned at least $10,000 per start was below average (13.3% was average for all horses studied) for groups with cardiac variables below the 50 th percentile. Horses with cardiac variables in the 75 th and higher percentiles were more likely to earn at least $10,000 per start.
  • Chi-square analysis was used to examine how Thoroughbreds' normalized heart size (as measured by LVD, LVS, PS, and SW) and normalized physical size (as measured by HTWT, which is the product of height times weight) relate to subsequent earnings and racing distances. Chi-square methods were used to show the predictive nature of each variable individually. Chi-square methods were then used to show the predictive nature of each cardiac variable, when used in conjunction with HTWT.
  • High earners and high earner routers were more likely to be above average in normalized physical size and normalized heart size (as measured by LVD, LVS, and SW). Low earners were more likely to be below average in normalized physical size and normalized heart size. High earner sprinters tended to be above average in normalized physical size with thick heart walls (as measured by normalized SW).
  • Table 41-Table 44 show the percentage of high earner routers with various combinations of above and below average normalized HTWT and normalized cardiac measurements (LVD, LVS, SW and PS).
  • Tables 45-48 show the percentage of high earner sprinters with various combinations of above and below average normalized HTWT and normalized cardiac measurements (LVD, LVS, SW and PS).
  • Table 49-52 show the percentage of low earners with various combinations of above and below average normalized HTWT and normalized cardiac measurements (LVD, LVS, SW and PS).
  • blind test discriminant models improved random odds of identifying high earners (or routers) by 35 percent (i.e., going from a 30% probability of correctly identifying high earners without models to a 40% probability with models).
  • Stepwise and discriminant analyses beyond those presented here sometimes produced exceptional results for one group in the comparison, but unexceptional results for the other group.
  • a high vs. low earners model may accurately predict high earners, while just meeting random expectations among low earners.
  • Multiple models differentiated by level of earnings may be needed in such instances.
  • Model limitations have to be assessed relative to potential applications. Z-tests were helpful in determining the statistical strength of discriminant results for each individual group represented in the models.
  • ecogenicity e.g., clarity, sharpness of contrast, type and symmetry of shapes, smoothness of functioning of structures
  • Stepwise analysis identified statistically significant variables that could differentiate between groups of horses categorized as high and low earners.
  • stepwise analysis identified the following significant variables
  • Non-Blind A-Z Table 53 shows that among 394 horses, non-blind discriminant models improved the odds of correctly classifying high earners from 33.25% without models to 43.93% with models. They improved the odds of correctly classifying low earners from 66.75% without models to 75.11% with models. All results were statistically significant (P-values ⁇ 0.0083). TABLE 53 Discriminant Model Results Using Subjective 1-5 Variables - High vs. Low Earners Non-Blind Tests - Combined Sexes - Names Starting with Letters A-Z Pre-Model Post-Model Probability Probability Category Ratio Pct. Ratio Pct. P-Value High Earners 131/394 33.25 76/173 43.93 0.0029 Low Earners 263/394 66.75 166/221 75.11 0.0083
  • Non-Blind A-M Table 54 shows that among horses with names beginning with the letters A-M, non-blind discriminant models improved the odds of correctly classifying high earners from 34.18% without models to 41.28% with models. They improved the odds of correctly classifying low earners from 65.82% without models to 71.88% with models. Results were not statistically significant (P ⁇ 0.1499). TABLE 54 Discriminant Model Results Using Subjective 1-5 Variables - High vs. Low Earners Non-Blind Tests - Combined Sexes - Names Starting with Letters A-M Pre-Model Post-Model Probability Probability Category Ratio Pct. Ratio Pct. P-Value High Earners 81/237 34.18 45/109 41.28 0.1188 Low Earners 156/237 65.82 92/128 71.88 0.1499

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