Accuracy of the electronic patient record in a first opinion veterinary practice

Ironing out the wrinkles and folds in the epidemiology of skin fold dermatitis in dog breeds in the UK

Skin fold dermatitis (intertrigo) is an inflammatory process of closely apposing skin surfaces. Extreme conformations towards folded skin in many dog breeds are linked with higher risk. Using anonymised primary-care veterinary data from the VetCompass Programme, this study aimed to report the frequency, demographic risk factors and clinical management for skin fold dermatitis in the UK. Risk factor analysis used random effects multivariable logistic regression modelling. From a study population of 905,553 dogs, the one-year period prevalence in dogs overall was 0.37% (95% CI 0.35–0.39). Diagnosis was supported by laboratory testing in 4.21% cases. Systemic antibiosis was used in 42.30% cases. Compared with crossbreed dogs, the most highly predisposed breeds were English Bulldog (odds ratio [OR] 49.07, 95% CI 37.79–63.70), French Bulldog (OR 25.92, 95% CI 19.62–34.26,) and Pug (OR 16.27, 95% CI 12.20–21.69). The most protected breeds were Yorkshire Terrier (OR 0.14, 95% CI 0.03–0.56), Border Collie (OR 0.31, 95% CI 0.11–0.84), Jack Russell Terrier (OR 0.53, 95% CI 0.30–0.92) and Labrador Retriever (OR 0.57, 95% CI 0.35–0.93). This study adds further evidence to the welfare concerns around high popularity of dog breeds with extreme conformations. The three breeds with by far the highest odds of skin fold dermatitis represent an extreme brachycephalic conformation.

Non-neoplastic anal sac disorders in UK dogs: Epidemiology and management aspects of a research-neglected syndrome

BACKGROUND

Non-neoplastic anal sac disorders (ASD) are frequent presentations for dogs in primary-care practice but evidence-based information on disease occurrence and risk is sparse. This study estimates prevalence, breed associations and other risk factors as well as reporting on clinical management.

METHODS

A cohort study of dogs attending VetCompass practices between 1 January 2013 and 31 December 2013. Risk factor analysis used multivariable logistic regression methods.

RESULTS

Of 104,212 dogs attending 110 UK practices, the 1-year period prevalence of ASD was 4.40% (95% CI: 4.22-4.57). Compared to crossbreds, six breeds showed increased odds of ASD (Cavalier King Charles spaniel, King Charles spaniel, Cockapoo, Shih-tzu, Bichon Frise and Cocker spaniel), and six breeds showed reduced odds (Labrador Retriever, Border collie, Staffordshire Bull Terrier, Lurcher, German Shepherd Dog and Boxer). Brachycephalic types had 2.6 times the odds for ASD compared to dolichocephalic types. Medication prescribed for ASD included antimicrobials (n = 480, 20.24%) and analgesics (n = 284, 11.97%). Anal sacculectomy was performed in under 1% of cases.

CONCLUSIONS

High prevalence, strong breed predispositions and evidence of severity suggested from the antimicrobial and analgesic therapies combined with current substantial knowledge gaps identify ASD as a key research-neglected syndrome in dogs.

Validation of text-mining and content analysis techniques using data collected from veterinary practice management software systems in the UK

Electronic patient records from practice management software systems have been used extensively in medicine for the investigation of clinical problems leading to the creation of decision support frameworks. To date, technologies that have been utilised for this purpose such as text mining and content analysis have not been employed significantly in veterinary medicine. The aim of this research was to pilot the use of content analysis and text-mining software for the synthesis and analysis of information extracted from veterinary electronic patient records. The purpose of the work was to be able to validate this approach for future employment across a number of practices for the purposes of practice based research. The approach utilised content analysis (Prosuite) and text mining (WordStat) software to aggregate the extracted text. Text mining tools such as Keyword in Context (KWIC) and Keyword Retrieval (KR) were employed to identify specific occurrences of data across the records. Two different datasets were interrogated, a bespoke test dataset that had been set up specifically for the purpose of the research, and a functioning veterinary clinic dataset that had been extracted from one veterinary practice. Across both datasets, the KWIC analysis was found to have a high level of accuracy with the search resulting in a sensitivity of between 85.3-100%, a specificity of between 99.1-99.7%, a positive predictive value between 93.5-95.8% and a negative predictive value between 97.7-100%. The KR search, based on machine learning, was utilised for the clinic-based dataset and was found to perform slightly better than the KWIC analysis. This study is the first to demonstrate the application of content analysis and text mining software for validation purposes across a number of different datasets for the purpose of search and recall of specific information across electronic patient records. This has not been demonstrated previously for small animal veterinary epidemiological research for the purposes of large scale analysis for practice-based research. Extension of this work to investigate more complex diseases across larger populations is required to fully explore the use of this approach in veterinary practice.