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.

OBJECTIVE: To evaluate the effect of the cyclooxygenase-2–selective NSAID firocoxib, compared to the nonselective NSAID flunixin meglumine on viscoelastic coagulation parameters in healthy horses. ANIMALS: 12 healthy adult mixed-breed horses. PROCEDURES: Following a crossover protocol, horses were administered flunixin meglumine (1.1 mg/kg, IV, q 12 h for 5 days), allowed a 6-month washout period, and then administered firocoxib (0.3 mg/kg, PO, once, then 0.1 mg/kg, PO, q 24 h for 4 days). Omeprazole (1 mg/kg, PO, q 24 h) was administered concurrently with each NSAID. Viscoelastic coagulation profiles and traditional coagulation parameters (prothrombin time, partial thromboplastin time, and fibrinogen) were measured before and after each treatment. RESULTS: Viscoelastic coagulation parameters were within reference intervals before and after both treatments. There was a statistically significant difference between treatments for amplitude at 10 minutes after clot time (P = .02) and maximum clot formation (P = .02); however, the magnitude of change was not clinically significant. CLINICAL RELEVANCE: Short-term administration of flunixin meglumine and firocoxib did not result in significant alteration of viscoelastic coagulation profiles in healthy horses. However, clinicians should be aware of possible coagulopathy secondary to NSAID administration with long-term use or critical illness, and further study is indicated.

Background: Viscoelastic coagulation testing (VCT) identifies subclinical disruption of coagulation homeostasis and may improve prognostication, particularly for patients with severe systemic inflammation or shock. Machine learning (ML) algorithms may capture complex relationships between clinical variables better than linear regression (GLM). Objective: To evaluate the utility of ML models incorporating VCT and clinical data to predict survival outcomes in horses with acute abdominal pain. Study Design: Retrospective observational cohort study. Methods: VCT (VCM Vet™) was performed on 57 horses with acute abdominal pain at admission, with clinical data collected retrospectively. Coagulopathy was defined as ≥2 abnormal VCT parameters. GLM and random forest (RF) classification models were developed to predict short‐term survival. A training cohort of 40 horses was used for model development, and model performance was determined using the remaining 17 horses. RF models were implemented in a web‐based application to demonstrate clinical application. Results: There were 31 survivors and 26 non‐survivors. The majority of cases were colitis (47.7%), with smaller proportions of impactions, strangulating obstructions and other causes of colic. Coagulopathy diagnosis alone performed poorly for survival prediction (sensitivity 81% [95% CI 64–94], specificity 31% [95% CI 15–50], AUC = 0.515). Final GLM included SIRS score (OR 0.37 [95% CI 0.071–1.68]; p = 0.2), L‐lactate (OR 0.51 [0.25–0.82]; p = 0.02), clot time (CT; OR 1.0 [0.99–1.0], p = 0.39), and clot amplitude at 10 min (A10; OR 0.89 [0.74–1.02], p = 0.2). Final RF model included heart rate, PCV, L‐lactate, white blood cell count, neutrophil count, clot amplitude at 20 min (A20) and CT. RF models improved sensitivity (RF _full 91% [95% CI 60–100]; RF _reduced 83% [95% CI 42–99]) and specificity (both 83% [95% CI 42–99]) compared to GLM (sensitivity 65% [95% CI 47–79], specificity 42% [95% CI 26–61]). Main Limitations: Small number of horses, convenience sampling. Model validation with an independent population is needed to support clinical applicability. Conclusions: L‐lactate remains a key predictor of survival in horses with colic. The integration of VCT with clinical data in machine learning models may enhance prognostication.