Vetnews | November 2025 20 « BACK TO CONTENTS Time series analysis of urethral obstruction in male cats in a veterinary teaching hospital in São Paulo, Brazil Reiner Silveira de Moraes1, Luíz Guilherme Dércore Benevenuto2, Suellen Rodrigues Maia3, Maria Gabriela Picelli de Azevedo1, Fernanda Barthelson Carvalho de Moura2, Diego Ribeiro1, Alessandra Melchert1, Henry David Mogollón García2, Rogério Giuffrida4, Adriano Sakai Okamoto1 & Priscylla Tatiana Chalfun Guimarães Okamoto10 Introduction Time series analysis can be used to understand and forecast patterns in sequential data. This study evaluated three statistical models—ARIMA, Holt-Winters, and linear regression—on the time series of urethral obstruction (UO) cases in male cats treated at the Veterinary Teaching Hospital – São Paulo State University, Botucatu, Brazil. Among the 5,230 cats evaluated between 2010 and 2020, the prevalence of UO in male cats was 7.4% (95% CI: 6.7–8.1%), and the incidence among cats showing lower urinary tract signs was 36.0% (95% CI: 33.19–38.93%). Most affected cats were neutered (60.94%), with a mean body weight of 4.24 ± 1.11 kg and higher body condition scores. ARIMA closely followed historical data but was ineffective for future forecasting, showing a flat projection from 2021 to 2024 (rate: 0.64) despite past fluctuations. The Holt-Winters model projected a rise in UO cases, from 0.70 (95% CI: 0.43–0.97) in 2021 to 1.09 (95% CI: 0.38–1.79) in 2024, but its wide confidence intervals indicated potential overestimation. Meanwhile, linear regression revealed a significant annual increase of 2.6% in UO cases (p = 0.042), explaining 38% of the variance and offering a more accurate long-term forecast, and then, it was considered the most suitable model, capturing trends without overestimating future rates. These findings support improved surveillance, clinical protocols, preventive strategies, and hospital resource planning for managing UO in male cats in a teaching veterinary hospital scenario. In medical emergencies predominantly affecting male cats, urethral obstruction (UO) stands out as one of the most relevant conditions involving the urinary tract1,2. It is frequently associated with feline lower urinary tract disease (FLUTD), a clinical syndrome widely reported in veterinary practice3. Urine retention may rapidly lead to systemic deterioration and potentially death. These cases require medical stabilisation, hospitalisation for relief of obstruction, and intensive monitoring, with variable hospitalisation times3–6. In this context, understanding the behaviour of the disease over a series of timelines, as well as estimating future patterns, may be valuable. Time series analysis is a valuable tool for modelling temporal patterns and forecasting future trends in both infectious and non-infectious diseases7. Despite its broad application in human epidemiology, its use in veterinary medicine – particularly for small animal diseases – remains limited. Common statistical models employed in time series analysis include autoregressive integrated moving average (ARIMA), Holt-Winters, and linear regression, each offering specific advantages depending on data structure8–10. In veterinary epidemiology, linear regression has been used to evaluate the risk of diseases11while ARIMA and Holt-Winters models have been applied to predict disease outbreaks12 and assess public health impacts during epidemics13. The UO in male cats can have various causes, including feline idiopathic cystitis (FIC), urethral plugs (p), urolithiasis (u), urinary tract infection (UTI), neoplasms (neo), and anatomical defects (ad)3,14–16. The prevalence of different etiologies of UO in male cats has been reported over time and varies according to the population studied. For instance, Kruger et al.17 demonstrated that UO caused by urethral plugs occurs in approximately 21% of cats diagnosed with FLUTD. Gerber et al.14 identified idiopathic FLUTD (53.3%), urolithiasis (28.8%), and urethral plugs (17.7%) as the main causes of UO in a population of 45 cats. Similarly, Dorsch et al.15 observed that UO was significantly more frequent in cats with FIC (57.1%) than in those with UTI (28.5%). More recently, Lew-Kojrys et al.16 also reported that UO was more common in cats with FIC (55.1%), urethral plugs (100%), and urolithiasis (56.0%), compared to those with UTI (13.3%) and neoplasms (25.0%), in a population of 229 cats with obstructive FLUTD. Numerous male cats with UO are annually admitted to the Veterinary Teaching Hospital of the São Paulo State University (UNESP) in Botucatu, São Paulo. In recent years, an apparent increase in UO cases has been noted, raising concerns about potential seasonal patterns or long-term trends. However, there is currently a lack of data-driven methods to quantify such trends, forecast future cases, or inform hospital resource planning. Therefore, the aim of this study was twofold: (1) to determine the prevalence and incidence of UO over 11 years; and (2) to evaluate which among three time series models—ARIMA, Holt-Winters, and linear regression—best captures the observed temporal patterns of UO in male cats. This approach seeks to address a relevant gap in veterinary epidemiological literature and to provide data-driven insights for clinical planning and disease prevention strategies. Methods Ethics statement This study followed the recommendations of the Brazilian National Council for the Control of Animal Experimentation (CONCEA) and was approved by the Ethics Committee on the Use of Animals (CEUA) in Research at the School of Veterinary Medicine and Animal Science (FMVZ) at the São Paulo State University — UNESP (0235/2021). All procedures followed the guidelines and regulations of the UNESP ethics council. Before any medical intervention and for inclusion in the study, written informed consent was obtained from all cat owners. The research followed ARRIVE criteria. Eligibility assessment and data collection A retrospective review was conducted using 5,230 medical records of cats treated at the Veterinary Teaching Hospital of FMVZ–UNESP in Botucatu, São Paulo, Brazil, a region known for having a high
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