Pre-test probability of urinary tract infection in dogs with clinical signs of lower urinary tract disease. (May 2019) - Sørensen, T.M.; Holmslykke, M.; Nordlund, M.; Siersma, V.; Jessen, L.R.
Highlights: Sex, age, hematuria and pollakiuria predicted the presence of canine urinary tract infection (UTI). Older, intact females had the highest likelihood of UTI. A combination of specific clinical signs only had modest predictive value for canine UTI. Abstract: Clinical signs of lower urinary tract disease in dogs are characteristic but non-specific for infection. It has been hypothesized that age, sex and neuter status influences the prevalence of urinary tract infection (UTI), but the predictive value of the combined clinical presentation has not been explored in dogs. The aim of the study was to assess clinical predictors (sex/neuter status, age, dysuria/stranguria, pollakiuria, macroscopic hematuria, malodorous urine and history of recurrent UTI) for bacterial cystitis, and to develop a clinical decision rule. Data was retrieved from medical records (retrospective cases) or from standardized recording sheets (prospective cases). Bacterial cystitis was defined as significant bacteriuria on quantitative bacterial culture in dogs with compatible clinical signs of urinary tract disease. Dogs of any breed, sex and age were included. A total of 1727 microbiology records were screened and 424 samples were included in the analysis. Bacterial cystitis was confirmed in 46% of the cases. Four variables predicted bacterial cystitis: sex/neuter status, age, pollakiuria and hematuria. A score was designated to each variable and a clinical rule was constructed. This rule attained an AUC of 0.75 and had sensitivity of 83% and specificity of 55% at its optimal cut-off (score ≥2.0). A score cut-off of ≥3.0 had a positive predictive value of 70%. Several factors predicted bacterial cystitis, but the clinical rule had only modest predictive value. Other variables or point-of-care test results should be included in future research to optimize overall precision.