Prognosis in equine colic patients using multivariable analysis

Multiple logistic regression was used to investigate prognosis in 308 horses referred to the University of Minnesota veterinary teaching hospital with colic. Bivariate results identified the following significant individual parameters: absent or hypomotile abdominal sounds, medical or surgical classification, peritoneal fluid total protein, anion gap, serum glucose, capillary refill time, blood pH, heart rate, packed cell volume, base excess, serum chloride, plasma bicarbonate, serum urinary nitrogen and age. Two multivariable prognostic models were developed using logistic regression. Model I (based on 257 cases with a mortality rate of 39%) included age, sex, medical or surgical classification, capillary refill time, packed cell volume and heart rate. Model II (based on 138 cases with a mortality rate of 48%) included age, sex, medical or surgical classification, capillary refill time, serum bicarbonate, serum chloride and respiratory rate. Predictive performance of the models was evaluated by treating the calculated probability of death for each horse as a continuous test result. The influence of varying the probability cutoff point for death on test characteristics (sensitivity, specificity and positive and negative predictive values) was determined. These models have not been validated and thus their performance in a different population is uncertain.