Performance of predictive models of survival in horses undergoing emergency exploratory laparotomy for colic. Issue 6 (8th June 2022) - Bishop, Rebecca C.; Gutierrez‐Nibeyro, Santiago D.; Stewart, Matthew C.; McCoy, Annette M.
Abstract: Objective: To evaluate previously published predictive survival models in a population of horses undergoing colic surgery in the midwestern United States. Study design: Retrospective cohort study; single referral hospital. Animals: A total of 260 horses met the inclusion criteria. Methods: Medical records of horses undergoing surgical treatment for colic were reviewed. Previously published models were applied to cohort data to predict outcome. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for prediction of short‐term survival were calculated. Results: Single‐variable and multivariable models performed similarly for prediction of survival, with a mean 79% sensitivity (range: 44%–94%), 48% specificity (range: 22%–83%), 63% PPV (range: 56%–72%), 73% NPV (range: 60%–83%), and 64% accuracy (range: 59%–72%). Blood lactate ≤6 mmol/l and the colic severity score (CSS) were highly sensitive for prediction of survival; however, both had poor specificity. Conclusion: Single‐variable and multivariable predictive models did not perform as well for prediction of survival in the study cohort compared to original reports, suggesting that population‐specific factors contribute to patient survival. Clinical significance: Predictive models of survival developed in one population may be less reliable when used to predict outcome in horses undergoing colic surgery from an independent population. Additional model testing and refinement using data from multiple surgical centers could be considered to improve prediction of outcome for horses undergoing laparotomy for treatment of colic.