Prognosis in equine colic patients using multivariable analysis.

This research sought to determine the prognosis of equine colic in horses referred to the University of Minnesota veterinary teaching hospital and developed two prognostic models based on a multiple of factors, though the overall effectiveness of these models is yet to be fully validated.

Study Methodology

• The research used multiple logistic regression to analyze the prognosis of 308 horses with colic.
• Several parameters were identified as significant in determining prognosis, including 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. The models relied on different parameters – age, sex, medical or surgical classification, capillary refill time, packed cell volume and heart rate for Model I; and age, sex, medical or surgical classification, capillary refill time, serum bicarbonate, serum chloride and respiratory rate for Model II.

Model Evaluation and Performance

• The models’ performance was assessed by treating the calculated probability of death for each horse as a continuous test result. This allowed for continuous adjustment and refinement of the model.
• The investigators also examined the influence of varying the probability cut off point for death on the test characteristics (sensitivity, specificity and positive and negative predictive values).
• Model I was based on 257 cases with a mortality rate of 39%, while Model II was based on 138 cases with a higher mortality rate of 48%.

Limits of the Study and Future Considerations

• The researchers note that these models have not been validated in other populations, and their performance remains uncertain when applied to different groups of horses.
• It’s also important to mention that although these models included a broad range of parameters, not all possible factors influencing prognosis might have been considered. Future research is suggested to further refine these models and validate their efficacy across diverse settings.