FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Equine Vet J / Clinical predictive models in equine medicine: A systematic ...
Equine veterinary journal2022; 55(4); 573-583; doi: 10.1111/evj.13880

Clinical predictive models in equine medicine: A systematic review.

Abstract: Clinical predictive models use a patient's baseline demographic and clinical data to make predictions about patient outcomes and have the potential to aid clinical decision making. The extent of equine clinical predictive models is unknown in the literature. Using PubMed and Google Scholar, we systematically reviewed the predictive models currently described for use in equine patients. Models were eligible for inclusion if they were published in a peer-reviewed article as a multivariable model used to predict a clinical/laboratory/imaging outcome in an individual horse or herd. The agreement of at least two authors was required for model inclusion. We summarised the patient populations, model development methods, performance metric reporting, validation efforts, and, using the Predictive model Risk of Bias Assessment Tool (PROBAST), assessed the risk of bias and applicability concerns for these models. In addition, we summarised the index conditions for which models were developed and provided detailed information on included models. A total of 90 predictive models and 9 external validation studies were included in the final systematic review. A plurality of models (41%) was developed to predict outcomes associated with colic, for example, need for surgery or survival to discharge. All included models were at high risk of bias, defined as failing one or more PROBAST signalling questions, primarily for analysis-related reasons. Importantly, a high risk of bias does not necessarily mean that models are unusable, but that they require more careful consideration prior to clinical use. Concerns about applicability were low for the majority of models. Systematic reviews such as this can serve to increase veterinarians' awareness of predictive models, including evaluation of their performance and their use in different patient populations.
© 2022 EVJ Ltd.
Get Full Text
Publication Date: 2022-11-08 PubMed ID: 36199162PubMed Central: PMC10073351DOI: 10.1111/evj.13880Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Systematic Review
  • Journal Article
  • Review

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research study conducted a systematic review of clinical predictive models in equine medicine, assessing their risk of bias, validation efforts, and usefulness in predicting patient outcomes.

Research Methodology

  • The researchers conducted a systematic review of clinical predictive models for horses by searching through scholarly articles on PubMed and Google Scholar.
  • The criteria for inclusion necessitated that the models must have been published in a peer-reviewed article, and they must predict clinical, laboratory, or imaging outcomes for either individual horses or entire herds.
  • The agreement of at least two authors was required for a model to be included in the review.

Analysis Tools and Data Considerations

  • The researchers analyzed the models using the Predictive model Risk of Bias Assessment Tool (PROBAST) to evaluate the risk of bias and concerns about applicability.
  • The metrics that the researchers used to judge the performance of these models were also examined.
  • Models with a “high risk of bias” were those that failed one or more PROBAST signalling questions, primarily for reasons related to analysis.

Findings from the Systematic Review

  • A total of 90 predictive models and 9 external validation studies were included in the review.
  • Of these, most (41%) were developed to predict outcomes associated with colic, such as the necessity for surgery or whether the horse would survive to discharge.
  • All of the models in the systematic review were identified as having a high risk of bias. However, this does not automatically mean the models are unusable, but it does point to the need for more careful evaluation before being relied upon in a clinical setting.
  • The majority of models introduced low concern over applicability, indicating that they could be widely useful.

Significance of Study

  • This systematic review can increase awareness among veterinarians about these predictive models, their performance, and their potential applications in different patient populations.
  • By highlighting potential biases, the study also promotes the need for careful consideration and validation of these models before they are used in clinical decision making.

Cite This Article

APA
Cummings CO, Krucik DDR, Price E. (2022). Clinical predictive models in equine medicine: A systematic review. Equine Vet J, 55(4), 573-583. https://doi.org/10.1111/evj.13880

Publication

Equine veterinary journal
ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English
Volume: 55
Issue: 4
Pages: 573-583

Researcher Affiliations

Cummings, Charles O
  • Tufts Clinical and Translational Science Institute, Tufts Medical Center, Boston, Massachusetts, USA.
Krucik, David D R
  • Department of Comparative Medicine, Stanford University, California, USA.
Price, Emma
  • Tufts Clinical and Translational Science Institute, Tufts Medical Center, Boston, Massachusetts, USA.

MeSH Terms

  • Horses
  • Animals
  • Prognosis
  • Bias

Grant Funding

  • TL1 TR002546 / NCATS NIH HHS
  • T32OD011121 / NIH HHS
  • TL1TR002546 / NCATS NIH HHS
  • TL1TR002546 / NCATS NIH HHS
  • T32OD011121 / NIH HHS

Conflict of Interest Statement

Competing Interests. No competing interests have been declared.

References

This article includes 84 references
  1. Tangri N, Stevens LA, Griffith J, Tighiouart H, Djurdjev O, Naimark D, Levin A, Levey AS. A predictive model for progression of chronic kidney disease to kidney failure.. JAMA 2011 Apr 20;305(15):1553-9.
    pubmed: 21482743doi: 10.1001/jama.2011.451google scholar: lookup
  2. Hadanny A, Shouval R, Wu J, Shlomo N, Unger R, Zahger D, Matetzky S, Goldenberg I, Beigel R, Gale C, Iakobishvili Z. Predicting 30-day mortality after ST elevation myocardial infarction: Machine learning- based random forest and its external validation using two independent nationwide datasets.. J Cardiol 2021 Nov;78(5):439-446.
    pubmed: 34154875doi: 10.1016/j.jjcc.2021.06.002google scholar: lookup
  3. Wyatt JC, Altman DG. Commentary: Prognostic models: clinically useful or quickly forgotten?. BMJ 1995;311:1539–1541.
  4. Kappen TH, van Loon K, Kappen MA, van Wolfswinkel L, Vergouwe Y, van Klei WA, Moons KG, Kalkman CJ. Barriers and facilitators perceived by physicians when using prediction models in practice.. J Clin Epidemiol 2016 Feb;70:136-45.
    pubmed: 26399905doi: 10.1016/j.jclinepi.2015.09.008google scholar: lookup
  5. Reeves MJ, Curtis CR. 'By the seat of your pants' or multivariable predictive modelling.. Equine Vet J 1989 Mar;21(2):83-4.
    pubmed: 2651109doi: 10.1111/j.2042-3306.1989.tb02099.xgoogle scholar: lookup
  6. Parry BW, Anderson GA, Gay CC. Prognosis in equine colic: a comparative study of variables used to assess individual cases.. Equine Vet J 1983 Jul;15(3):211-5.
    pubmed: 6884310doi: 10.1111/j.2042-3306.1983.tb01768.xgoogle scholar: lookup
  7. Reeves MJ, Curtis CR, Salman MD, Hilbert BJ. Prognosis in equine colic patients using multivariable analysis.. Can J Vet Res 1989 Jan;53(1):87-94.
    pmc: PMC1255520pubmed: 2914230
  8. Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW. Calibration: the Achilles heel of predictive analytics.. BMC Med 2019 Dec 16;17(1):230.
    pmc: PMC6912996pubmed: 31842878doi: 10.1186/s12916-019-1466-7google scholar: lookup
  9. Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment.. J Thorac Oncol 2010 Sep;5(9):1315-6.
    pubmed: 20736804doi: 10.1097/jto.0b013e3181ec173dgoogle scholar: lookup
  10. Venema E, Wessler BS, Paulus JK, Salah R, Raman G, Leung LY, Koethe BC, Nelson J, Park JG, van Klaveren D, Steyerberg EW, Kent DM. Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination.. J Clin Epidemiol 2021 Oct;138:32-39.
    pubmed: 34175377doi: 10.1016/j.jclinepi.2021.06.017google scholar: lookup
  11. Publish or Perish. Version 8. Harzing AW; 2021.
  12. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews.. Syst Rev 2016 Dec 5;5(1):210.
    pmc: PMC5139140pubmed: 27919275doi: 10.1186/s13643-016-0384-4google scholar: lookup
  13. Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies.. Ann Intern Med 2019 Jan 1;170(1):51-58.
    pubmed: 30596875doi: 10.7326/m18-1376google scholar: lookup
  14. Reeves MJ, Curtis CR, Salman MD, Stashak TS, Reif JS. Validation of logistic regression models used in the assessment of prognosis and the need for surgery in equine colic patients. Prev Vet Med 1992;13:155–172.
  15. Puotunen-Reinert A. Study of variables commonly used in examination of equine colic cases to assess prognostic value.. Equine Vet J 1986 Jul;18(4):275-7.
    pubmed: 3758004doi: 10.1111/j.2042-3306.1986.tb03626.xgoogle scholar: lookup
  16. Orsini JA, Elser AH, Galligan DT, Donawick WJ, Kronfeld DS. Prognostic index for acute abdominal crisis (colic) in horses.. Am J Vet Res 1988 Nov;49(11):1969-71.
    pubmed: 3247920
  17. Reeves MJ, Curtis CR, Salman MD, Reif JS, Stashak TS. A multivariable prognostic model for equine colic patients. Prev Vet Med 1990;9:241–257.
  18. Pascoe PJ, Ducharme NG, Ducharme GR, Lumsden JH. A computer-derived protocol using recursive partitioning to aid in estimating prognosis of horses with abdominal pain in referral hospitals.. Can J Vet Res 1990 Jun;54(3):373-8.
    pmc: PMC1255672pubmed: 2199007
  19. Furr MO, Lessard P, White NA 2nd. Development of a colic severity score for predicting the outcome of equine colic.. Vet Surg 1995 Mar-Apr;24(2):97-101.
    pubmed: 7778263doi: 10.1111/j.1532-950x.1995.tb01302.xgoogle scholar: lookup
  20. Sandholm M, Vidovic A, Puotunen-Reinert A, Sankari S, Nyholm K, Rita H. D-dimer improves the prognostic value of combined clinical and laboratory data in equine gastrointestinal colic.. Acta Vet Scand 1995;36(2):255-72.
    pmc: PMC8095414pubmed: 7484552doi: 10.1186/bf03547694google scholar: lookup
  21. Freden GO, Provost PJ, Rand WM. Reliability of using results of abdominal fluid analysis to determine treatment and predict lesion type and outcome for horses with colic: 218 cases (1991-1994).. J Am Vet Med Assoc 1998 Oct 1;213(7):1012-5.
    pubmed: 9777000
  22. Thoefner MB, Ersbøll AK, Hesselholt M. Prognostic indicators in a Danish hospital-based population of colic horses.. Equine Vet J Suppl 2000 Jun;(32):11-8.
    pubmed: 11202376doi: 10.1111/j.2042-3306.2000.tb05328.xgoogle scholar: lookup
  23. Ihler CF, Venger JL, Skjerve E. Evaluation of clinical and laboratory variables as prognostic indicators in hospitalised gastrointestinal colic horses.. Acta Vet Scand 2004;45(1-2):109-18.
    pmc: PMC1820986pubmed: 15535091doi: 10.1186/1751-0147-45-109google scholar: lookup
  24. Levi O, Affolter VK, Benak J, Kass PH, Le Jeune SS. Use of pelvic flexure biopsy scores to predict short-term survival after large colon volvulus.. Vet Surg 2012 Jul;41(5):582-8.
    pubmed: 22571759doi: 10.1111/j.1532-950x.2012.00994.xgoogle scholar: lookup
  25. Gonzalez LM, Fogle CA, Baker WT, Hughes FE, Law JM, Motsinger-Reif AA, Blikslager AT. Operative factors associated with short-term outcome in horses with large colon volvulus: 47 cases from 2006 to 2013.. Equine Vet J 2015 May;47(3):279-84.
    pmc: PMC4198525pubmed: 24735170doi: 10.1111/evj.12273google scholar: lookup
  26. McConachie E, Giguère S, Barton MH. Scoring System for Multiple Organ Dysfunction in Adult Horses with Acute Surgical Gastrointestinal Disease.. J Vet Intern Med 2016 Jul;30(4):1276-83.
    pmc: PMC5094519pubmed: 27296454doi: 10.1111/jvim.14321google scholar: lookup
  27. Farrell A, Kersh K, Liepman R, Dembek KA. Development of a Colic Scoring System to Predict Outcome in Horses.. Front Vet Sci 2021;8:697589.
    pmc: PMC8531487pubmed: 34692803doi: 10.3389/fvets.2021.697589google scholar: lookup
  28. Ducharme NG, Pascoe PJ, Lumsden JH, Ducharme GR. A computer-derived protocol to aid in selecting medical versus surgical treatment of horses with abdominal pain.. Equine Vet J 1989 Nov;21(6):447-50.
    pubmed: 2686971doi: 10.1111/j.2042-3306.1989.tb02194.xgoogle scholar: lookup
  29. Reeves MJ, Curtis CR, Salman MD, Stashak TS, Reif JS. Multivariable prediction model for the need for surgery in horses with colic.. Am J Vet Res 1991 Nov;52(11):1903-7.
    pubmed: 1785737
  30. Thoefner MB, Ersbøll BK, Jansson N, Hesselholt M. Diagnostic decision rule for support in clinical assessment of the need for surgical intervention in horses with acute abdominal pain.. Can J Vet Res 2003 Jan;67(1):20-9.
    pmc: PMC227023pubmed: 12528825
  31. Latson KM, Nieto JE, Beldomenico PM, Snyder JR. Evaluation of peritoneal fluid lactate as a marker of intestinal ischaemia in equine colic.. Equine Vet J 2005 Jul;37(4):342-6.
    pubmed: 16028624doi: 10.2746/0425164054529319google scholar: lookup
  32. Pye J, Espinosa-Mur P, Roca R, Kilcoyne I, Nieto J, Dechant J. Preoperative factors associated with resection and anastomosis in horses presenting with strangulating lesions of the small intestine.. Vet Surg 2019 Jul;48(5):786-794.
    pubmed: 30834566doi: 10.1111/vsu.13184google scholar: lookup
  33. Keppie NJ, Rosenstein DS, Holcombe SJ, Schott HC 2nd. Objective radiographic assessment of abdominal sand accumulation in horses.. Vet Radiol Ultrasound 2008 Mar-Apr;49(2):122-8.
    pubmed: 18418991doi: 10.1111/j.1740-8261.2008.00337.xgoogle scholar: lookup
  34. Louro LF, Robson K, Hughes J, Loomes K, Senior M. Head and tail rope-assisted recovery improves quality of recovery from general anaesthesia in horses undergoing emergency exploratory laparotomy.. Equine Vet J 2022 Sep;54(5):875-884.
    pubmed: 34541712doi: 10.1111/evj.13516google scholar: lookup
  35. Corley KTT, Furr MO. Evaluation of a score designed to predict sepsis in foals: evaluation of sepsis score. J Vet Emergen Crit Care 2003;13:149–155.
  36. Wong DM, Ruby RE, Dembek KA, Barr BS, Reuss SM, Magdesian KG, Olsen E, Burns T, Slovis NM, Wilkins PA. Evaluation of updated sepsis scoring systems and systemic inflammatory response syndrome criteria and their association with sepsis in equine neonates.. J Vet Intern Med 2018 May;32(3):1185-1193.
    pmc: PMC5980351pubmed: 29582480doi: 10.1111/jvim.15087google scholar: lookup
  37. Weber EJ, Sanchez LC, Giguère S. Re-evaluation of the sepsis score in equine neonates.. Equine Vet J 2015 May;47(3):275-8.
    pubmed: 24750245doi: 10.1111/evj.12279google scholar: lookup
  38. Hoffman AM, Staempfli HR, Willan A. Prognostic variables for survival of neonatal foals under intensive care.. J Vet Intern Med 1992 Mar-Apr;6(2):89-95.
    pubmed: 1588547doi: 10.1111/j.1939-1676.1992.tb03157.xgoogle scholar: lookup
  39. Haas SD, Bristol F, Card CE. Risk factors associated with the incidence of foal mortality in an extensively managed mare herd.. Can Vet J 1996 Feb;37(2):91-5.
    pmc: PMC1576626pubmed: 8640655
  40. Furr M, Tinker MK, Edens L. Prognosis for neonatal foals in an intensive care unit.. J Vet Intern Med 1997 May-Jun;11(3):183-8.
    pubmed: 9183771doi: 10.1111/j.1939-1676.1997.tb00088.xgoogle scholar: lookup
  41. Rohrbach BW, Buchanan BR, Drake JM, Andrews FM, Bain FT, Byars DT, Bernard WV, Furr MO, Paradis MR, Lawler J, Giguère S, Dunkel B. Use of a multivariable model to estimate the probability of discharge in hospitalized foals that are 7 days of age or less.. J Am Vet Med Assoc 2006 Jun 1;228(11):1748-56.
    pubmed: 16740076doi: 10.2460/javma.228.11.1748google scholar: lookup
  42. Staempfli HR, Townsend HG, Prescott JF. Prognostic features and clinical presentation of acute idiopathic enterocolitis in horses.. Can Vet J 1991 Apr;32(4):232-7.
    pmc: PMC1481315pubmed: 17423769
  43. Boemer F, Detilleux J, Cello C, Amory H, Marcillaud-Pitel C, Richard E, van Galen G, van Loon G, Lefère L, Votion DM. Acylcarnitines profile best predicts survival in horses with atypical myopathy.. PLoS One 2017;12(8):e0182761.
    pmc: PMC5573150pubmed: 28846683doi: 10.1371/journal.pone.0182761google scholar: lookup
  44. Tirosh-Levy S, Solomovich R, Comte J, Sutton GA, Steinman A. Daboia (Vipera) palaestinae envenomation in horses: Clinical and hematological signs, risk factors for mortality and construction of a novel severity scoring system.. Toxicon 2017 Oct;137:58-64.
    pubmed: 28698056doi: 10.1016/j.toxicon.2017.07.007google scholar: lookup
  45. Roy MF, Kwong GP, Lambert J, Massie S, Lockhart S. Prognostic Value and Development of a Scoring System in Horses With Systemic Inflammatory Response Syndrome.. J Vet Intern Med 2017 Mar;31(2):582-592.
    pmc: PMC5354005pubmed: 28207163doi: 10.1111/jvim.14670google scholar: lookup
  46. de Barros AMC, Silva AFR, Zibordi M, Spagnolo JD, Corrêa RR, Belli CB, de Camargo MM. Equine simplified acute physiology score: Personalised medicine for the equine emergency patient.. Vet Rec 2021 Sep;189(5):e136.
    pubmed: 33729604doi: 10.1002/vetr.136google scholar: lookup
  47. Ishihara A, Reed SM, Rajala-Schultz PJ, Robertson JT, Bertone AL. Use of kinetic gait analysis for detection, quantification, and differentiation of hind limb lameness and spinal ataxia in horses.. J Am Vet Med Assoc 2009 Mar 1;234(5):644-51.
    pubmed: 19250044doi: 10.2460/javma.234.5.644google scholar: lookup
  48. Kutasi O, Balogh N, Lajos Z, Nagy K, Szenci O. Diagnostic approaches for the assessment of equine chronic pulmonary disorders. J Equine Vet Sci 2011;31:400–410.
  49. Porter RS, Leblond A, Lecollinet S, Tritz P, Cantile C, Kutasi O, Zientara S, Pradier S, van Galen G, Speybroek N, Saegerman C. Clinical diagnosis of West Nile Fever in Equids by classification and regression tree (CART) analysis and comparative study of clinical appearance in three European countries.. Transbound Emerg Dis 2011 Jun;58(3):197-205.
    pubmed: 21208395doi: 10.1111/j.1865-1682.2010.01196.xgoogle scholar: lookup
  50. Tilley P, Sales Luis JP, Branco Ferreira M. Correlation and discriminant analysis between clinical, endoscopic, thoracic X-ray and bronchoalveolar lavage fluid cytology scores, for staging horses with recurrent airway obstruction (RAO).. Res Vet Sci 2012 Oct;93(2):1006-14.
    pubmed: 22136797doi: 10.1016/j.rvsc.2011.10.024google scholar: lookup
  51. Saegerman C, Alba-Casals A, García-Bocanegra I, Dal Pozzo F, van Galen G. Clinical Sentinel Surveillance of Equine West Nile Fever, Spain.. Transbound Emerg Dis 2016 Apr;63(2):184-93.
    pubmed: 24899369doi: 10.1111/tbed.12243google scholar: lookup
  52. Marti E, Wang X, Jambari NN, Rhyner C, Olzhausen J, Pérez-Barea JJ, Figueredo GP, Alcocer MJ. Novel in vitro diagnosis of equine allergies using a protein array and mathematical modelling approach: a proof of concept using insect bite hypersensitivity.. Vet Immunol Immunopathol 2015 Oct 15;167(3-4):171-7.
    pubmed: 26163936doi: 10.1016/j.vetimm.2015.06.013google scholar: lookup
  53. Haltmayer E, Reiser S, Schramel JP, van den Hoven R. Breathing pattern and thoracoabdominal asynchrony in horses with chronic obstructive and inflammatory lung disease.. Res Vet Sci 2013 Oct;95(2):654-9.
    pubmed: 23837917doi: 10.1016/j.rvsc.2013.06.008google scholar: lookup
  54. Bullone M, Hélie P, Joubert P, Lavoie JP. Development of a Semiquantitative Histological Score for the Diagnosis of Heaves Using Endobronchial Biopsy Specimens in Horses.. J Vet Intern Med 2016 Sep;30(5):1739-1746.
    pmc: PMC5032871pubmed: 27527123doi: 10.1111/jvim.14556google scholar: lookup
  55. Miller JE, Mann S, Fettelschoss-Gabriel A, Wagner B. Comparison of three clinical scoring systems for Culicoides hypersensitivity in a herd of Icelandic horses.. Vet Dermatol 2019 Dec;30(6):536-e163.
    pubmed: 31441172doi: 10.1111/vde.12784google scholar: lookup
  56. Randleff-Rasmussen PK, Leblond A, Cappelle J, Bontemps J, Belluco S, Popoff MR, Marcillaud-Pitel C, Tapprest J, Tritz P, Desjardins I. Development of a clinical prediction score for detection of suspected cases of equine grass sickness (dysautonomia) in France.. Vet Res Commun 2018 Mar;42(1):19-27.
    pubmed: 29204821doi: 10.1007/s11259-017-9704-ygoogle scholar: lookup
  57. White SJ, Moore-Colyer M, Marti E, Hannant D, Gerber V, Coüetil L, Richard EA, Alcocer M. Antigen array for serological diagnosis and novel allergen identification in severe equine asthma.. Sci Rep 2019 Oct 23;9(1):15170.
    pmc: PMC6811683pubmed: 31645629doi: 10.1038/s41598-019-51820-7google scholar: lookup
  58. Delarocque J, Frers F, Feige K, Huber K, Jung K, Warnken T. Metabolic changes induced by oral glucose tests in horses and their diagnostic use.. J Vet Intern Med 2021 Jan;35(1):597-605.
    pmc: PMC7848347pubmed: 33277752doi: 10.1111/jvim.15992google scholar: lookup
  59. Haspeslagh M, Gerber V, Knottenbelt DC, Schüpbach G, Martens A, Koch C. The clinical diagnosis of equine sarcoids-Part 2: Assessment of case features typical of equine sarcoids and validation of a diagnostic protocol to guide equine clinicians in the diagnosis of equine sarcoids.. Vet J 2018 Oct;240:14-18.
    pubmed: 30268326doi: 10.1016/j.tvjl.2018.08.010google scholar: lookup
  60. Pfau T, Robilliard JJ, Weller R, Jespers K, Eliashar E, Wilson AM. Assessment of mild hindlimb lameness during over ground locomotion using linear discriminant analysis of inertial sensor data.. Equine Vet J 2007 Sep;39(5):407-13.
    pubmed: 17910264doi: 10.2746/042516407x185719google scholar: lookup
  61. Church EE, Walker AM, Wilson AM, Pfau T. Evaluation of discriminant analysis based on dorsoventral symmetry indices to quantify hindlimb lameness during over ground locomotion in the horse.. Equine Vet J 2009 Mar;41(3):304-8.
    pubmed: 19469241doi: 10.2746/042516409x397352google scholar: lookup
  62. Luethy D, Feldman R, Stefanovski D, Aitken MR. Risk factors for laminitis and nonsurvival in acute colitis: Retrospective study of 85 hospitalized horses (2011-2019).. J Vet Intern Med 2021 Jul;35(4):2019-2025.
    pmc: PMC8295695pubmed: 33938584doi: 10.1111/jvim.16147google scholar: lookup
  63. Cresswell EN, McDonough SP, Palmer SE, Hernandez CJ, Reesink HL. Can quantitative computed tomography detect bone morphological changes associated with catastrophic proximal sesamoid bone fracture in Thoroughbred racehorses?. Equine Vet J 2019 Jan;51(1):123-130.
    pubmed: 29758110doi: 10.1111/evj.12965google scholar: lookup
  64. Louro LF, Milner PI, Bardell D. Epidural administration of opioid analgesics improves quality of recovery in horses anaesthetised for treatment of hindlimb synovial sepsis.. Equine Vet J 2021 Jul;53(4):682-689.
    pubmed: 32852063doi: 10.1111/evj.13338google scholar: lookup
  65. Lyle CH, Blissitt KJ, Kennedy RN, Mc Gorum BC, Newton JR, Parkin TD, Stirk A, Boden LA. Risk factors for race-associated sudden death in Thoroughbred racehorses in the UK (2000-2007).. Equine Vet J 2012 Jul;44(4):459-65.
    pubmed: 22128788doi: 10.1111/j.2042-3306.2011.00496.xgoogle scholar: lookup
  66. Barrier Battut I, Kempfer A, Becker J, Lebailly L, Camugli S, Chevrier L. Development of a new fertility prediction model for stallion semen, including flow cytometry.. Theriogenology 2016 Sep 1;86(4):1111-1131.
    pubmed: 27207472doi: 10.1016/j.theriogenology.2016.04.001google scholar: lookup
  67. Biral NV, Azevedo Santos H, Senne NA, Paulino PG, Camilo TA, Tassinari WS, Silva VL, Santos FN, Angelo IDC. A cross-sectional study of Leishmania spp. in draft horses from the Distrito Federal, Brazil: Seroprevalence, spatial distribution, and associated factors.. Prev Vet Med 2021 Oct;195:105467.
    pubmed: 34416652doi: 10.1016/j.prevetmed.2021.105467google scholar: lookup
  68. Boyle AG, Smith MA, Boston RC, Stefanovski D. A case-control study developing a model for predicting risk factors for high SeM-specific antibody titers after natural outbreaks of Streptococcus equi subsp equi infection in horses.. J Am Vet Med Assoc 2017 Jun 15;250(12):1432-1439.
    pubmed: 28569630doi: 10.2460/javma.250.12.1432google scholar: lookup
  69. Agnew ME, Slack J, Stefanovski D, Linton JK, Sertich PL. Sonographic appearance of the late gestation equine fetal intestine.. Theriogenology 2019 Oct 15;138:121-126.
    pubmed: 31326658doi: 10.1016/j.theriogenology.2019.07.006google scholar: lookup
  70. Brewer BD, Koterba AM, Carter RL, Rowe ED. Comparison of empirically developed sepsis score with a computer generated and weighted scoring system for the identification of sepsis in the equine neonate.. Equine Vet J 1988 Jan;20(1):23-4.
    pubmed: 3284743doi: 10.1111/j.2042-3306.1988.tb01446.xgoogle scholar: lookup
  71. Kattan MW, Hess KR, Amin MB, Lu Y, Moons KG, Gershenwald JE, Gimotty PA, Guinney JH, Halabi S, Lazar AJ, Mahar AL, Patel T, Sargent DJ, Weiser MR, Compton C. American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine.. CA Cancer J Clin 2016 Sep;66(5):370-4.
    pmc: PMC4955656pubmed: 26784705doi: 10.3322/caac.21339google scholar: lookup
  72. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.. BMC Med 2015 Jan 6;13:1.
    pmc: PMC4284921pubmed: 25563062doi: 10.1186/s12916-014-0241-zgoogle scholar: lookup
  73. Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal.. BMJ 2020 Apr 7;369:m1328.
    pmc: PMC7222643pubmed: 32265220doi: 10.1136/bmj.m1328google scholar: lookup
  74. Andaur Navarro CL, Damen JAA, Takada T, Nijman SWJ, Dhiman P, Ma J, Collins GS, Bajpai R, Riley RD, Moons KGM, Hooft L. Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review.. BMJ 2021 Oct 20;375:n2281.
    pmc: PMC8527348pubmed: 34670780doi: 10.1136/bmj.n2281google scholar: lookup
  75. Daines L, McLean S, Buelo A, Lewis S, Sheikh A, Pinnock H. Systematic review of clinical prediction models to support the diagnosis of asthma in primary care.. NPJ Prim Care Respir Med 2019 May 9;29(1):19.
    pmc: PMC6509212pubmed: 31073125doi: 10.1038/s41533-019-0132-zgoogle scholar: lookup
  76. Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S. PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration.. Ann Intern Med 2019 Jan 1;170(1):W1-W33.
    pubmed: 30596876doi: 10.7326/m18-1377google scholar: lookup
  77. Watson J, Hutyra CA, Clancy SM, Chandiramani A, Bedoya A, Ilangovan K, Nderitu N, Poon EG. Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers?. JAMIA Open 2020 Jul;3(2):167-172.
    pmc: PMC7382631pubmed: 32734155doi: 10.1093/jamiaopen/ooz046google scholar: lookup
  78. Brown LA, Benhamou K, May AM, Mu W, Berk R. Machine Learning Algorithms in Suicide Prevention: Clinician Interpretations as Barriers to Implementation.. J Clin Psychiatry 2020 Apr 21;81(3).
    pubmed: 32316078doi: 10.4088/jcp.19m12970google scholar: lookup
  79. Cummings CO. Letter to the editor: A tool for calculating VetCOT score.. J Vet Emerg Crit Care (San Antonio) 2022 May;32(3):426.
    pubmed: 35363939doi: 10.1111/vec.13194google scholar: lookup
  80. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews.. BMJ 2021 Mar 29;372:n71.
    pmc: PMC8005924pubmed: 33782057doi: 10.1136/bmj.n71google scholar: lookup
  81. Fischer AT Jr. Corrections for prognostic index equation.. Am J Vet Res 1989 Aug;50(8):1429.
    pubmed: 2782726
  82. Brewer BD, Koterba AM. Development of a scoring system for the early diagnosis of equine neonatal sepsis.. Equine Vet J 1988 Jan;20(1):18-22.
    pubmed: 3366100doi: 10.1111/j.2042-3306.1988.tb01445.xgoogle scholar: lookup
  83. Stewart AJ, Hinchcliff KW, Saville WJ, Jose-Cunilleras E, Hardy J, Kohn CW, Reed SM, Kowalski JJ. Actinobacillus sp. bacteremia in foals: clinical signs and prognosis.. J Vet Intern Med 2002 Jul-Aug;16(4):464-71.
    pubmed: 12141310doi: 10.1892/0891-6640(2002)016<0464:sbifcs>2.3.co;2google scholar: lookup
  84. Dembek KA, Hurcombe SD, Frazer ML, Morresey PR, Toribio RE. Development of a likelihood of survival scoring system for hospitalized equine neonates using generalized boosted regression modeling.. PLoS One 2014;9(10):e109212.
    pmc: PMC4189956pubmed: 25295600doi: 10.1371/journal.pone.0109212google scholar: lookup

Citations

This article has been cited 3 times.
Subscribe to our newsletter
Join 99,344 horse owners like you who receive our email updates on horse health and nutrition.