Abstract
BACKGROUND: Novel methods to aid identification of dogs with spontaneous Cushing's syndrome are warranted to optimize case selection for diagnostics, avoid unnecessary testing, and ultimately aid decision-making for veterinarians. HYPOTHESIS/OBJECTIVES: To develop and internally validate a prediction tool for dogs receiving a diagnosis of Cushing's syndrome using primary-care electronic health records. ANIMALS: Three hundred and ninety-eight dogs diagnosed with Cushing's syndrome and 541 noncase dogs, tested for but not diagnosed with Cushing's syndrome, from a cohort of 905 544 dogs attending VetCompass participating practices. METHODS: A cross-sectional study design was performed. A prediction model was developed using multivariable binary logistic regression taking the demography, presenting clinical signs and some routine laboratory results into consideration. Predictive performance of each model was assessed and internally validated through bootstrap resampling. A novel clinical prediction tool was developed from the final model. RESULTS: The final model included predictor variables sex, age, breed, polydipsia, vomiting, potbelly/hepatomegaly, alopecia, pruritus, alkaline phosphatase, and urine specific gravity. The model demonstrated good discrimination (area under the receiver operating curve [AUROC] = 0.78 [95% CI = 0.75-0.81]; optimism-adjusted AUROC = 0.76) and calibration (C-slope = 0.86). A tool was developed from the model which calculates the predicted likelihood of a dog having Cushing's syndrome from 0% (score = -13) to 96% (score = 10). CONCLUSIONS AND CLINICAL IMPORTANCE: A tool to predict a diagnosis of Cushing's syndrome at the point of first suspicion in dogs was developed, with good predictive performance. This tool can be used in practice to support decision-making and increase confidence in diagnosis.