Vetnuus | November 2025 25 Article Although the primary focus of our study was the identification of the presence of UO, incomplete medical records prevented our ability to properly determine the UO aetiology in a substantial number of animals due to incomplete laboratory and imaging test results. Thus, comprehensive documentation, diagnostic workups, and standardised protocols are essential to improve the accuracy of future retrospective analyses. Further investigations could benefit from integrating machine learning approaches with time series modelling to enhance predictive accuracy and improve identification of high-risk patients. In conclusion, our study identified a high prevalence of UO cases in male cats, with a clear increasing incidence over recent years. Among the models tested, the Holt-Winters exponential smoothing model was demonstrated to be complementary to the linear regression model. However, given the overestimation provided by the Holt-Winters model for more distant projections, linear regression proved to be the most suitable for predicting future case rates in our veterinary hospital setting for UO in male cats. These findings offer a valuable insight into future caseload expectations and underscore the importance of long-term monitoring of UO incidence and the integration of predictive analytics in veterinary epidemiology. The persistence of elevated case numbers beyond the pandemic period suggests that lifestyle and environmental factors may have a lasting influence on UO risk, highlighting the need for targeted preventive strategies in clinical practice. Nevertheless, it enables the development and implementation of standardised clinical protocols and targeted staff training to appropriately manage the anticipated increase in admissions of male cats with UO in the coming years. Figure 5: Case rate prediction according to ARIMA (0,1,0) Figure 6: Case rate prediction according to the Holt-Winters model >>>26
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