Development of a colic severity score for predicting the outcome of equine colic.
Thirty-two physical examination and laboratory variables were recorded during examination of 165 horses admitted for acute abdominal disease. Univariate analyses were performed to determine which of the variables were significantly different between horses that lived or died. Stepwise logistic regression was performed to identify variables with the best predictive value. Four variables (heart rate, peritoneal fluid total protein concentration, blood lactate concentration, and abnormal mucous membrane) remained significant when entered into the model. Histograms for each significant variable were used to set "cutting-points," establishing categories that were made into a table of assigned values from which a Colic Severity Score (CSS) for each horse was calculated. Seventy-one horses in a second group were used to validate the scoring chart. Case mortality rate was similar in both groups (20.6% in development group versus 21.1% in validation group). All horses with a CSS > 7 died, whereas 75% of those with a score of < or = 7 lived. For the validation group, use of the scoring table yielded a positive predictive value of 100%, negative predictive value of 91.8%, sensitivity of 66.7%, and specificity of 100%. The overall accuracy of the CSS was 93%. The CSS is a rapid and accurate method for predicting survival in cases of equine acute abdominal disease.
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Marion duPont Scott Equine Medical Center, Virginia-Maryland Regional College of Veterinary Medicine, Leesburg, USA.
Lessard
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White
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D000208:Acute Disease; D000704:Analysis of Variance; D000818:Animals; D003085:Colic / Q000401:mortality / Q000503:physiopathology / Q000662:veterinary*; D005189:False Positive Reactions; D006734:Horse Diseases / Q000401:mortality / Q000503:physiopathology*; D006736:Horses; D008954:Models, Biological; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D012984:Software