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Veterinary research communications2017; 42(1); 19-27; doi: 10.1007/s11259-017-9704-y

Development of a clinical prediction score for detection of suspected cases of equine grass sickness (dysautonomia) in France.

Abstract: Equine grass sickness (EGS) (equine dysautonomia) is a neurodegenerative condition of grazing equines. Pre-mortem diagnosis of EGS is a challenge for practitioners as definitive diagnosis requires ileal/myenteric lymph node biopsies. This study aimed to develop a clinical score that could be used by practitioners to improve the detection of acute or subacute EGS cases in the field. Suspected EGS cases were declared by veterinary practitioners. A case was classified as confirmed positive if ileal or rectal biopsy samples showed neuronal degeneration typical of EGS. A semi-quantitative scoring system, including epidemiological and clinical data, was created to attempt to classify suspected EGS horses into confirmed positive or negative cases. Each variable was weighted based on a boosted regression trees model, while taking into account its clinical relevance. Twenty-eight EGS cases were confirmed by biopsy during the entire study period. The best cut-off value for the score to have a high sensitivity while maximizing specificity was 8, with a sensitivity of 100% and a specificity of 53%. In our dataset, 77% of animals would be correctly classified with this cut-off value of 8. Highest sensitivity was chosen in order to detect the highest number of potential cases. Our score represents an inexpensive and useful tool to aid in the identification of suspected EGS cases in the field and selection for further diagnostics procedures to confirm or rule out the disease. Application of the score to larger populations of animals would be required to further adapt and refine the score.
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Publication Date: 2017-12-04 PubMed ID: 29204821DOI: 10.1007/s11259-017-9704-yGoogle Scholar: Lookup
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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 aims at developing a scoring system to detect cases of Equine Grass Sickness (EGS) minimizing the need for invasive diagnostic procedures like biopsies. The scoring system uses factors like epidemiological and clinical data, and it has proven effective with a 100% sensitivity and a specificity of 53%.

Background on Equine Grass Sickness (EGS)

  • This study focuses on a condition known as Equine Grass Sickness (EGS), also referred to as equine dysautonomia, which is a degenerative disease that affects the nervous system of horses that graze.
  • Diagnosing EGS pre-mortem typically requires invasive procedures like ileal or rectal biopsies, making it challenging for practitioners to confirm the disease.

Methodology

  • The researchers sought to develop a semi-quantitative scoring system to classify suspected EGS horses into confirmed cases (positive or negative).
  • A case was considered confirmed if ileal or rectal biopsies showed signs of the neurodegeneration typical of EGS.
  • The score considered epidemiological factors and clinical data and each variable was weighted according to a boosted regression trees model while considering its clinical relevance.

Results

  • The score was tested on suspected EGS cases; a total of 28 cases were confirmed to be EGS during the study period.
  • Researchers found that a cut-off value of 8 for the score provided the best balance between sensitivity (100%) and specificity (53%).
  • With this cut-off value, 77% of the animals in the dataset were correctly classified.

Implications and Future Research

  • The developed score is seen as a valuable tool to aid in identifying suspected EGS cases in the field, therefore minimizing the need for invasive diagnostic procedures.
  • However, the researchers note that further study would need to be done using larger animal populations to fine-tune the scoring system.
  • The researchers also note that this scoring approach prioritizes sensitivity (identifying as many potential positive cases as possible) over specificity (accurately identifying negative cases).

Cite This Article

APA
Randleff-Rasmussen PK, Leblond A, Cappelle J, Bontemps J, Belluco S, Popoff MR, Marcillaud-Pitel C, Tapprest J, Tritz P, Desjardins I. (2017). Development of a clinical prediction score for detection of suspected cases of equine grass sickness (dysautonomia) in France. Vet Res Commun, 42(1), 19-27. https://doi.org/10.1007/s11259-017-9704-y

Publication

Veterinary research communications
ISSN: 1573-7446
NlmUniqueID: 8100520
Country: Switzerland
Language: English
Volume: 42
Issue: 1
Pages: 19-27

Researcher Affiliations

Randleff-Rasmussen, P K
  • Equine Clinic, University of Lyon, VetAgroSup, Marcy L'Etoile, France. pia.randleff-rasmussen@vetagro-sup.fr.
Leblond, A
  • Equine Clinic, University of Lyon, VetAgroSup, Marcy L'Etoile, France.
  • UMR EPIA, INRA, VetAgro Sup, 69280, Marcy l'étoile, France.
  • Réseau d'Epidémio-Surveillance en Pathologie Equine (RESPE), Saint-Contest, France.
Cappelle, J
  • UMR EPIA, INRA, VetAgro Sup, 69280, Marcy l'étoile, France.
  • UMR ASTRE, CIRAD, INRA, 34398, Montpellier, France.
Bontemps, J
  • Equine Clinic, University of Lyon, VetAgroSup, Marcy L'Etoile, France.
Belluco, S
  • Laboratoire d'Histopathologie, University of Lyon, VetAgroSup, Marcy L'Etoile, France.
Popoff, M R
  • Anaerobic Bacteria and Toxins, Pasteur Institute, Paris, France.
Marcillaud-Pitel, C
  • Réseau d'Epidémio-Surveillance en Pathologie Equine (RESPE), Saint-Contest, France.
Tapprest, J
  • Laboratory for Equine Diseases, French Agency for Food, Environmental and Occupational Health & Safety (Anses), Goustranville, France.
Tritz, P
  • Réseau d'Epidémio-Surveillance en Pathologie Equine (RESPE), Saint-Contest, France.
  • Commission Maladies Infectieuses et Epidémiologie AVEF, Paris, France.
  • Clinique Vétérinaire, Faulquemont, France.
Desjardins, I
  • Equine Clinic, University of Lyon, VetAgroSup, Marcy L'Etoile, France.

MeSH Terms

  • Animals
  • Horse Diseases / diagnosis
  • Horses
  • Primary Dysautonomias / diagnosis
  • Primary Dysautonomias / veterinary
  • Sensitivity and Specificity
  • Veterinary Medicine / methods

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Citations

This article has been cited 2 times.
  1. Cummings CO, Krucik DDR, Price E. Clinical predictive models in equine medicine: A systematic review.. Equine Vet J 2023 Jul;55(4):573-583.
    doi: 10.1111/evj.13880pubmed: 36199162google scholar: lookup
  2. Laus F, Corsalini J, Mandara MT, Bazzano M, Bertoletti A, Gialletti R. Equine grass sickness in italy: a case series study.. BMC Vet Res 2021 Aug 6;17(1):264.
    doi: 10.1186/s12917-021-02966-ypubmed: 34362361google scholar: lookup
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