RESUMEN
BACKGROUND: Scoring models are useful tools that guide the attending clinician in gauging the severity of disease evolution and in evaluating the efficacy of treatment. There are few tools available with this purpose for the non-human patient, including horses. We aimed (i) to adapt the simplified acute physiology score 3 (SAPS-3) model for the equine species, reaching a margin of accuracy greater than 75% in the calculation of the probability of survival/death and (ii) to build a decision tree that helps the attending veterinarian in assessment of the clinical evolution of the equine patient. METHODS: From an initial pool of 5568 medical records from University-based Veterinary Hospitals, a final cohort of 1000 was further mined manually for data extraction. A set of 19 variables were evaluated and tested by five machine learning data mining algorithms. RESULTS: The final scoring model, named EqSAPS for equine simplified acute physiology score, reached 91.83% of correct estimates (post hoc) for probability of death within 24 hours upon hospitalization. The area under receiver operating characteristic curve for outcome 'death' was 0.742, while for 'survival' was 0.652. The final decision tree was able to refine prognosis of patients whose EqSAPS score suggested 'death'. CONCLUSION: EqSAPS is a useful tool to gauge the severity of the clinical presentation of the equine patient.