1. Development of a clinical prediction score for detection of suspected cases of equine grass sickness (dysautonomia) in France
- Author
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Isabelle Desjardins, J Bontemps, P Tritz, J. Tapprest, C Marcillaud-Pitel, S Belluco, Pia Randleff-Rasmussen, Agnès Leblond, M R Popoff, Julien Cappelle, VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Unité Mixte de Recherche d'Épidémiologie des maladies Animales et zoonotiques (UMR EPIA), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Réseau d'Epidémio-Surveillance en Pathologie Équine (RESPE), Animal, Santé, Territoires, Risques et Ecosystèmes (UMR ASTRE), Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Interactions Cellules Environnement - UR (ICE), Centre National de Référence des Bactéries Anaérobies et Botulisme - National Reference Center Anaerobic Bacteria and Botulism (CNR), Institut Pasteur [Paris], Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Association Vétérinaire Equine Française (AVEF), Épidémiologie des Maladies Animales et Zoonotiques - UMR 346 (EPIA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA), Centre National de Référence des Bactéries Anaérobies et du Botulisme - Bactéries Anaérobies et Toxines (CNR), and Institut Pasteur [Paris] (IP)
- Subjects
Veterinary Medicine ,medicine.medical_specialty ,Clinical score ,040301 veterinary sciences ,Primary Dysautonomias ,Disease ,L73 - Maladies des animaux ,Sensitivity and Specificity ,0403 veterinary science ,Internal medicine ,Biopsy ,Epidemiology ,Animals ,Medicine ,MESH: Animals ,Neuronal degeneration ,Grass sickness ,Clinical significance ,Horses ,MESH: Horses ,Prediction score ,[SDV.BA.MVSA]Life Sciences [q-bio]/Animal biology/Veterinary medicine and animal Health ,General Veterinary ,medicine.diagnostic_test ,business.industry ,MESH: Veterinary Medicine ,0402 animal and dairy science ,Dysautonomia ,04 agricultural and veterinary sciences ,General Medicine ,040201 dairy & animal science ,MESH: Sensitivity and Specificity ,3. Good health ,[SDV.TOX]Life Sciences [q-bio]/Toxicology ,Horse Diseases ,Equine grass sickness ,medicine.symptom ,MESH: Horse Diseases ,business ,MESH: Primary Dysautonomias - Abstract
International audience; 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.
- Published
- 2017
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