Background Clinical signs and consequences of Cushing's syndrome are likely to impact upon a dog's life. Quantification of this impact on a dog's health‐related quality‐of‐life (HRQoL) could contribute to optimized disease management. Hypothesis/objectives To develop a novel HRQoL tool to aid assessment of dogs with Cushing's syndrome and to evaluate factors that impact upon dogs living with this disease. Animals Two hundred and ten dogs with Cushing's syndrome and 617 dogs without Cushing's syndrome. Methods Cross‐sectional study design. Dog owners answered questions relating to the HRQoL of their dogs which were refined to develop the final tool. The tool was analyzed for reliability, validity, and interpretability, including Cronbach's alpha and principal components analysis. Factors impacting upon the HRQoL of dogs with Cushing's syndrome were assessed using appropriate nonparametric tests. Results The tool was refined from 32 questions to 19 and showed good internal consistency (α = .83). Owners rated questions related to “owner impact” as more important and those related to demeanor as less important. There was a positive correlation between the tool score of dogs with Cushing's syndrome and owner's assessment of their dog's quality‐of‐life (r = .41, P < .001). Dogs currently on treatment with trilostane had a statistically better HRQoL (.33, interquartile range [IQR] .23–.44) than those not receiving trilostane (.36, IQR .33–.54, P = .04). Conclusions and Clinical Importance The developed tool quantifies the HRQoL of dogs with Cushing's syndrome and could assist clinicians in the clinical assessment of dogs with Cushing's syndrome.

Cushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.

Objectives: To estimate the frequency, clinical approaches and risk factors of hypoadrenocorticism in dogs under UK primary veterinary care.Materials and Methods: Dogs diagnosed with hypoadrenocorticism were identified from the UK VetCompass™ programme by searching anonymised electronic patient records. Pre‐existing and newly diagnosed cases of disease during 2016 were included. Cases were further sub‐categorised as having a laboratory‐confirmed or presumed diagnosis of hypoadrenocorticism based on the information recorded in the electronic patient records. Descriptive data were manually extracted. Multivariable logistic regression methods were used to identify demographic risk factors.Results: There were 177 hypoadrenocorticism cases identified from 905,543 dogs in 2016; 72 laboratory‐confirmed and 105 presumed. The 1‐year period prevalence for hypoadrenocorticism in all dogs was 0.06% (95% confidence interval: 0.05‐0.07%). The most common presenting clinical signs in laboratory‐confirmed dogs were lethargy (51/66, 77.3%), anorexia (48/66, 66.7%) and vomiting (48/66, 66.7%). Hyperkalaemia was reported in 47 of 53 (88.7%), hyponatraemia in 46 of 53 (86.8%). Median sodium: potassium ratio was 19.00 (interquartile range: 16.20‐20.60). Breed, age, neuter status and insurance status were associated with a laboratory‐confirmed diagnosis of hypoadrenocorticism. No sex association with hypoadrenocorticism was observed in the multivariable model. The standard poodle had 51.38 times the odds (95% CI: 14.49‐182.18) of hypoadrenocorticism compared with crossbreeds. The labradoodle and West Highland white terrier also had increased odds.Clinical Significance: This is the first epidemiological study to report on hypoadrenocorticism in dogs within the UK primary‐care population. These results provide benchmark data of current veterinary activity relating to hypoadrenocorticism in primary‐care practices.

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.

Objectives: To integrate external data sources with VetCompass postcode data to explore the spatial distribution and examine potential associations with environmental risk factors in dogs diagnosed with lymphoma at primary care veterinary practices.Materials and Methods: Cases of lymphoma were identified from electronic patient records of 455,553 dogs under primary veterinary care during 2013 in the UK. Cases were defined as either laboratory‐confirmed or non‐laboratory‐confirmed. Disease maps at the postcode‐district level were used to define the geographic distribution of lymphoma incidence and spatial clustering was explored. Environmental risk factors from external data sources were transferred to a compatible format and logistic regression modelling was used to examine associations between environmental herbicide, fungicide and radon concentrations with lymphoma.Results: From the denominator population of 455,553 dogs, 279 lymphoma cases (187 with laboratory confirmation and 93 without) were identified. Heterogeneous geographic variation was observed with weak evidence of clustering around London and the south‐west of England. Herbicide and fungicide exposures were weakly associated with a diagnosis of lymphoma in the univariable analysis. After accounting for the age at diagnosis and breed in the multivariable analysis, herbicide exposure was associated with a diagnosis of lymphoma.Clinical Significance: The heterogeneous distribution of lymphoma in UK dogs provides further evidence for geographic variation of lymphoma, perhaps in part associated with underlying environmental risk factors. The results suggest an association between environmental herbicide and canine lymphoma.