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.