Developing a Feline Infectious Disease Triage Model: Insights from Logistic Regression Models in Data from a Veterinary Isolation Unit

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Feline Immunodeficiency Virus (FIV) and Feline Leukemia Virus (FeLV) are two of the most common infections reported in this unit. This study explored the use of logistic regression to predict FIV and FeLV infections in the triage stage. Results: Of 1211 cats treated at the BICU since its opening, 134 cats were FIV-positive and 126 FeLV-positive. Significant triage-related factors for FIV-related hospitalization included being an adult or senior cat, intact males, having access to the outdoors, and presenting concomitant disorders. In contrast, mixed-breed cats with concomitant disorders and a low hematocrit count were significant risk factors for FeLV-related hospitalization. The estimated logistic regression models without cross-validation showed areas under the Receiver Operating Characteristic curve (AUC) of 0.71 for FIV and 0.67 for FeLV, with 95% CI of [0.66-0.76] and [0.62-0.73], respectively. Cross-validation highlighted high sensitivity but low specificity for both infections, indicating a higher propensity for false positives. When cross-validation was performed for FIV infections, the resulting AUC was 0.66, and the specificity was 0.33 using 10- and 5-fold cross validations. The models for FeLV exhibited similar predictive performance with an AUC of 0.63 and specificity of 0.29, which decreased further with 10- and 5-fold cross validation. Conclusions: This study highlights significant triage-related factors for FIV and FeLV infections, in agreement with existing literature. These findings indicate a need for better clinical vigilance and owner education, mainly on neutering and the risk of outdoor access. Future research should expand to other predictive models and include other variables important to predict FIV and FeLV at the triage stage. Triage Epidemiology Infectious Diseases Feline Leukemia Virus Feline Immunodeficiency Virus Predictive Models Cross-Validation Cats Figures Figure 1 Introduction The Biological Isolation and Containment Unit (BICU) of the Teaching Hospital (TH) of the Faculty of Veterinary Medicine, University of Lisbon (FMV-ULisboa) admits patients with the confirmation or suspicion of an infectious disease. The number of hospitalized cats since the foundation of the Unit in October 2013 to December 2022 was 1211. The majority of the cases were diagnosed as infections caused by Feline Immunodeficiency Virus (FIV) and Feline Leukemia Virus (FeLV). FIV and FeLV are the most common infectious agents in domestic cats [ 1 – 4 ]. FIV is a lentivirus [ 5 , 6 ], primarily transmitted through bites during fights and persists throughout life [ 7 ]. FeLV is a gammaretrovirus transmitted via contact with saliva and other secretions from infected cats[ 1 , 5 ]. Both diseases present nonspecific clinical signs and are usually characterized by anemia, immunodeficiency, neoplasia (mainly lymphoma), and secondary infections [ 3 – 5 ]. Although the prevalence of FIV and FeLV infection has been decreasing in recent years, it has stagnated in some countries [ 2 , 5 ]. Understanding the triage-related factors associated with these infections is crucial for rapid clinical management of feline patients. Common factors include older age, intact males, outdoor access, close contact with infected cats, aggressive behavior, and mixed breeds [ 4 , 5 , 8 ]. Given their substantial impact on feline health, particularly in our clinical setting where they constitute the majority of hospitalized cases, developing accurate triage models for FIV and FeLV is imperative. These models have the potential to streamline clinical management by facilitating the rapid identification and prioritization of true infected feline patients among the suspected cases. Currently, there is growing interest in applying statistical and machine learning (ML) methods to healthcare [ 9 ]. In particular, ML is becoming a popular tool in the development of clinical decision support systems for infection disease management (e.g., early detection of clinical signs and risk factors, case monitoring, improved diagnosis of atypical cases, selection of new treatments, or exiting treatments for other diseases) and diagnostic purposes [ 10 ]. While previous studies employed logistic regression to identify risk factors for FIV and FeLV [ 5 , 11 – 14 ], these studies showed limitations while inferring the predictive performance of the estimated models due to the absence of a cross-validation step. Cross-validation is a statistical technique that helps evaluate the generalizability of the predictions provided by statistical and ML models. To evaluate model performance, the training set was split into sections according to the chosen cross-validation method. This process is repeated so that each section is used as a test fold once [ 15 , 16 ]. The primary goal of this study was to aid in the development of a more accurate triage tool for predicting true infected feline patients among suspected cases of FIV and FeLV infections. To accomplish this, our specific aims included a general characterization of FIV- and FeLV-infected cats and their controls, assembling a control group for each disease, determining potential triage-related factors associated with hospitalization due to these retroviruses, and comparing logistic regression models. Upon successful development, these triage models will serve as invaluable tools in the BICU, enabling timely and informed decision making in the triaging of feline patients, thereby improving overall clinical outcomes and resource utilization. Results Basic Characteristics of the FIV and FeLV-infected cats and their controls The initial database consisted of 1211 cats. Of these, 165 and 161 cats were confirmed to have FIV and FeLV infections, respectively. We excluded the patients from the analysis if they had missing values in any of the covariates; therefore, the new data frame contained 134 FIV-infected cats and 126 FeLV-infected cats (complete case scenario). Table 1 displays the demographic variables of the FIV- and FeLV-infected cats and their controls. The majority of FIV-infected cats were adult, mixed-breed, neutered males, living with multiple cats, with outdoor access and presenting concomitant disorders. While FeLV-infected cats were also mixed-breed, adult cats with concomitant disorders, the majority lived exclusively indoors, and presented a lower hematocrit count. Table 1 Basic characteristics of FIV and FeLV-infected cats and their controls Characteristics Categories FIV FeLV Case, n = 134 Cats (%) Control, n = 268 Cats (%) Case, n = 126 Cats (%) Control, n = 252 Cats (%) Breed Purebred 5 (3.7%) 26 (9.7%) 4 (3.2%) 22 (8.7%) Mixed-breed 129 (96.3%) 242 (90.3%) 122 (96.8%) 230 (91.3%) Sex and Neuter Status Neutered Male 71 (53.0%) 107 (39.9%) 39 (31.0%) 114 (45.2%) Intact Male 20 (14.9%) 32 (11.9%) 23 (18.3%) 37 (14.7%) Neutered Female 29 (21.6%) 79 (29.5%) 39 (31.0%) 69 (27.4%) Intact Female 14 (10.4%) 50 (18.7%) 25 (19.8%) 32 (12.7%) Number of cats in the household Single Cat 58 (43.3%) 101 (37.7%) 57 (45.2%) 106 (42.1%) ≥ 2 Cats 76 (56.7%) 167 (62.3%) 69 (54.8%) 146 (57.9%) Lifestyle Indoor 52 (38.8%) 164 (61.2%) 72 (57.1%) 153 (60.7%) Outdoor 82 (61.2%) 104 (38.8%) 54 (42.9%) 99 (39.3%) Concomitant Disorders/ Diseases No 23 (17.2%) 90 (33.6%) 27 (21.4%) 81 (32.1%) Yes 111 (82.8%) 178 (66.4%) 99 (78.6%) 171 (67.9%) Age Groups (Years) < 2 13 (9.7%) 78 (29.1%) 25 (19.8%) 58 (23.0%) ≥ 2 & <10 82 (61.2%) 119 (44.4%) 81 (64.3%) 116 (46.0%) ≥ 10 39 (29.1%) 71 (26.5%) 20 (15.9%) 78 (31.0%) Leukocytes Classification High 28 (20.9%) 54 (20.1%) 25 (19.8%) 58 (23.0%) Normal 79 (59.0%) 156 (58.2%) 69 (54.8%) 148 (58.7%) Low 27 (20.1%) 58 (21.6%) 32 (25.4%) 46 (18.3%) Hematocrit Classification High 16 (11.9%) 39 (14.6%) 12 (9.5%) 35 (13.9%) Normal 77 (57.5%) 149 (55.6%) 45 (35.7%) 134 (53.2%) Low 41 (30.6%) 80 (29.9%) 69 (54.8%) 83 (32.9%) Predictive analysis of hospitalization due to FIV infections Age emerged as a significant triage-related factor for FIV infection (Table 2 ). Both adult and senior cats (≥ 2 & < 10 and ≥ 10 years) were the most frequent and significantly associated with an increased risk of hospitalization in the logistic regression model. Although mixed-breed cats, living with multiple cats, and having a normal hematocrit and leukocyte count were more frequent, these factors were not significantly associated with this infection. Intact male cats with outdoor access and concomitant disorders were found to be at a higher risk of hospitalization due to FIV infections (p < 0.05). The final logistic regression model had an estimated AUC of 0.71 with a 95% confidence interval (CI) [0.66–0.76] before cross-validation (Fig. 1 ). The sensitivity and specificity estimates were 0.63 and 0.69, respectively, which suggested that this model had similar predictive performance on true cases and true controls. After cross-validation based on LOOCV, the AUC decreased to 0.66, with an increased sensitivity of 0.86, but a lower specificity of 0.33. The 10-fold and 5-fold cross-validations yielded similar results with an AUC of 0.69, with a sensitivity of 0.85 and 0.86 and specificity of 0.30 and 0.32, respectively. Table 2 Simple and multiple regression models for predicting the probability of hospitalization due to FIV infection (n = 402). Characteristics Categories Simple Logistic Regression Multiple Logistic Regression Estimate Std error p-value Estimate Std error p-value Breed Purebred — — — — Mixed-breed 1.019 0.49 0.042 0.627 0.528 0.235 Sex and Neuter Status Intact Female — — — — Intact Male 0.803 0.416 0.053 0.991 0.459 0.031 Neutered Female 0.271 0.372 0.467 -0.052 0.397 0.895 Neutered Male 0.863 0.339 0.011 0.457 0.364 0.209 Number of cats in the household 1 — — > 1 -0.232 0.215 0.279 Lifestyle Indoor — — — — Outdoor 0.911 0.217 < 0.001 0.735 0.234 0.002 Concomitant Disorders/ Diseases No — — — — Yes 0.892 0.263 < 0.001 0.69 0.291 0.017 Age Groups (Years) < 2 — — — — ≥ 2 & < 10 1.419 0.332 < 0.001 1.274 0.373 < 0.001 ≥ 10 1.193 0.360 < 0.001 1.093 0.411 0.008 Classification of Leukocyte High 1 — — Normal 2 -0.024 0.271 0.931 Low 3 -0.108 0.329 0.743 Classification of Hematocrit High 4 — — Normal 5 0.231 0.328 0.482 Low 6 0.223 0.364 0.529 1 High>19.5 x10 9 /L; 2 Normal: 5.5–19.5 x10 9 /L; 3 Low 45%; 5 Normal: 30–45%; 6 Low < 30% Predictive analysis of hospitalization due to FeLV infections For FeLV-infected cats, our study identified that mixed-breed, concomitant disorders and a low hematocrit count were significantly associated with FeLV infection (Table 3 , p < 0.05). Remarkably, neutered male cats displayed a significantly lower risk of being infected with FeLV and hospitalized (estimate = -0.836, p = 0.011). Even though adult cats, living indoors, with multiple animals, and presenting normal leukocyte counts were the most prevalent demographic groups, they were not significantly associated with FeLV infection. Before using cross-validation, the final logistic regression model implied an AUC of 0.67 with a 95%CI [0.62–0.73] (Fig. 1 ). The corresponding sensitivity and specificity were estimated at 0.63 and 0.65. To better understand the model performance, we applied cross-validation. LOOCV yielded an AUC of 0.63, with high sensitivity (0.88) but low specificity (0.29). A 10-fold cross-validation presented an AUC of 0.66, with a sensitivity of 0.88, and specificity of 0.25. Similarly, a 5-fold cross-validation revealed an AUC of 0.64, with the previous sensitivity and a further reduced specificity (0.19). Table 3 Simple and Multiple regression models for predicting the probability of hospitalization due to FeLV infection (n = 378). Characteristic Categories Simple Logistic Regression Multiple Logistic Regression Estimate Std error p-value Estimate Std error p-value Breed Purebred — — — — Mixed-breed 1.071 0.555 0.054 1.155 0.568 0.042 Sex and Neuter Status Intact Female — — — — Intact Male -0.229 0.377 0.544 -0.145 0.393 0.712 Neutered Female -0.324 0.334 0.332 -0.381 0.347 0.271 Neutered Male -0.826 0.325 0.011 -0.836 0.336 0.013 Number of cats in the household 1 — — > 1 -0.129 0.220 0.557 Lifestyle Indoor — — — — Outdoor 0.148 0.221 0.505 Concomitant Disorders/ Diseases No — — — — Yes 0.552 0.256 0.031 0.560 0.271 0.039 Age Groups (Years) < 2 — — — — ≥ 2 & < 10 0.482 0.2797 0.084 ≥ 10 -0.519 0.347 0.134 Classification of Leukocyte High 1 — — Normal 2 0.078 0.280 0.779 Low 3 0.479 0.332 0.149 Classification of Hematocrit High 4 — — — — Normal 5 -0.021 0.376 0.956 0.141 0.385 0.714 Low 6 0.886 0.372 0.017 0.964 0.380 0.011 1 High>19.5 x10 9 /L; 2 Normal: 5.5–19.5 x10 9 /L; 3 Low 45%; 5 Normal: 30–45%; 6 Low < 30% Discussion FIV infections Regarding the variable breed, there was no significant association, corroborating other researchers who found no evidence of breed predisposition to hospitalization due to a FIV infection [ 5 , 12 ]. Agreeing with multiple studies [ 5 , 6 , 8 , 12 , 13 ], our study found that intact male cats were at a higher risk of being hospitalized due to FIV infection. FIV is shed in saliva, and one of the most common ways of transmission is through bite wounds [ 6 , 8 ]. Therefore, this association is in line with male cats having a higher likelihood of encountering infected cats, and being prone to aggression and territorial fights. Evidence of an association between intact cats and FIV positivity, mainly in intact male cats, has been found in multiple studies [ 17 , 18 ]. Neutered cats are more likely to be kept indoors and have a lower risk of infection [ 5 , 18 ]. Consistent with other research studies [ 5 , 8 , 12 , 13 , 19 ], our analysis also observed that cohabitation with other cats did not significantly influence admission to the BICU with FIV. This is particularly evident in a study conducted by Litster, in which no transmission of FIV was recorded over several years among 130 uninfected cats living with eight FIV-infected cats [ 19 ]. A correlation was found between outdoor access and being hospitalized with FIV. Outdoor access is a possible triage-related factor, because free-roaming cats are more likely to encounter infected cats [ 5 ]. FIV is an immunosuppressive disease; therefore, FIV-infected cats may be predisposed to secondary and opportunistic infections [ 6 , 8 ]. Similar to FeLV, it is difficult to determine whether the cat’s health is a cause or effect of FIV infection [ 20 ]. FIV in cats with concomitant disorders was significantly associated with an increased risk of hospitalization in the BICU. Another study showed a similar association with sick cats that were more likely to test positive for FIV [ 5 ]. While bone marrow suppression can occur in FIV infections, it is noteworthy that this phenomenon is more commonly associated with FeLV-infected cats [ 2 , 6 , 8 , 21 ]. In FeLV cases, bone marrow disorders, including anemia, are often observed [ 2 ]. However, in FIV infections, the acute phase is characterized by mild neutropenia, and in later stages, lymphopenia may occur [ 22 , 23 ]. Despite these hematological changes, our study did not find a significant association between leukocyte or hematocrit levels and being hospitalized with FIV. This result is consistent with the general understanding that FIV is typically less pathogenic than FeLV [ 2 , 24 ]. FIV-infected cats can remain asymptomatic or mildly symptomatic for an extended period, and the progression of the disease varies among individuals [ 8 ]. Being hospitalized with FIV was statistically relevant in cats between the ages of ≥ 2 & < 10 and ≥ 10 years, respectively, compared to cats with less than 2 years. This result was consistent with previous observations that an increase in age leads to susceptibility of contracting FIV [ 5 , 8 , 12 ]. This increase in age can be explained by the fact that FIV-positive cats can remain asymptomatic for many years and only express clinical signs later in life when diagnosed at that stage[ 12 ]. FeLV infection Typically, FeLV does not have a breed predisposition, however, in this study, an association between mixed-breed cats and being hospitalized with FeLV was found. According to Hartmann and Hofmann-Lehmann [ 2 ] FeLV is commonly found in mixed-breed cats because purebreds tend to be kept indoors, and there is greater awareness from the owners to test their animals. This aligns with the broader understanding that indoor management and proactive healthcare in purebred cats may have a lower risk of infection. However, other studies [ 5 , 12 ] have not found any association. Regarding sex and neuter status, neutered males were associated with being less likely to be admitted to the BICU with FeLV. A possible explanation is that an owner who has a neutered cat is more vigilant and aware of the risks. Older studies, state that FeLV infection was found to be approximately equal between sexes [ 2 , 13 ]. The explanation for this is that FeLV transmission frequently occurs between infected queens and kittens, and among cats living in close contact, infection can occur regardless of sex [ 2 , 13 ]. However, in more recent studies, male cats appeared to be at a greater risk of contracting the disease [ 2 , 5 , 12 , 13 ]. There is no consensus about the neuter status, and some researchers indicated that intact cats, especially males, are more at risk of infection [ 25 , 26 ]. While other authors did not find any association [ 5 , 12 ]. This could be because of the differences between the studied populations; while some studies are about owned cats, others are made at shelters or with stray cats, which leads to different results. Surprisingly, cohabitation with multiple cats was not associated with an increase of hospitalization with FeLV. A possible reason stated by Gleich et al. [ 13 ] was that the increasing awareness of this disease makes owners more susceptible to testing and more careful when adding a new cat to a household of multiple animals. The results of the present study were unexpected because previous studies have stated that FeLV is a “social” disease [ 2 , 13 ]. The non-significant association between outdoor access and FeLV hospitalization challenges the traditional view of outdoor exposure as a risk factor. While previous literature suggests that outdoor access increases the risk of infection [ 2 ], other contemporary studies had the same result as ours [ 5 , 12 , 13 ]. This outcome suggests that indoor cats are not risk-free, potentially due to in-home transmission among cohabiting cats or unknown history of adopted animals. FeLV in cats with concomitant disorders was significantly associated with an increased risk of hospitalization. This association is plausible because, like FIV, FeLV is an immunosuppressive disease; therefore, these cats are predisposed to opportunistic and secondary infections [ 2 , 5 , 21 ]. It is important to remark that the health of the cat at the time of infection and the time after infection can be unknown, so it is difficult to assume whether the cat’s health is a cause or an effect of FeLV infection [ 20 ]. While FeLV is commonly associated with bone marrow suppression, the most prevalent hematological abnormality observed in our study, as well as in the literature is anemia [ 2 , 27 ]. Our findings determined that a lower hematocrit was significantly associated with an increased risk of being hospitalized. It is important to note that while bone marrow suppression can affect other blood cell lines such as leukocytes, our study did not find a significant association between leukocyte classification and hospitalization in FeLV-infected cats. FeLV is considered to be age-dependent, so when cats mature, they become more resistant to the infection [ 2 ]. Thus, younger cats are more likely to be progressively infected with FeLV [ 2 ]. However, in more recent studies adult cats were more likely to be infected with FeLV [ 2 ]. A possible explanation is that, because of an increase in awareness, owners test their cats more frequently; therefore, cats are provided with medical care earlier, leading to a longer life [ 2 ]. In our study, age was not significantly associated with being hospitalized with FeLV. A possible reason for this is that the ages of our FeLV-infected cats and their control groups were similar, and infection can occur at any life stage. Evaluation of model performance To our knowledge, this study is the first to employ cross-validation techniques to evaluate logistic regression models to identify triage-related factors in FIV- and FeLV-infected cats. For FIV-infected cats, our logistic regression model demonstrated a modest predictive ability (AUC = 0.71) without cross-validation. Subsequent cross-validation procedures led to similar results. While sensitivity remained high (≥ 0.85), specificity was modest (≈ 0.30), indicating a tendency to overpredict FIV infection. Despite the stable predictive capability across various data subsets, further model refinement is required to mitigate overfitting. For FeLV-infected cats, our logistic regression model exhibited a moderate ability (AUC = 0.67) to identify infection before cross-validation. However, various cross-validation techniques, particularly LOOCV, revealed a decreased AUC (0.63) and specificity (0.29), despite high sensitivity (≥ 0.88). This pattern of high sensitivity but low specificity underscores the risk of misclassification, and highlights the need for model enhancement to reduce overdiagnosis. It is noteworthy that both ROC curves for FIV- and FeLV-infected cats exhibited similar patterns, showing a comparable predictive ability of the models for both infections. We chose to perform cross-validation because of the limitation of our dataset, which was not sufficiently large to split into independent training and testing sets. While cross-validation provides a robust method for estimating model performance, an independent validation set would be ideal for future studies to further validate the efficacy of our models. In summary, while our models demonstrated promising sensitivities in detecting FIV and FeLV infections, the modest specificities suggest that additional covariates should be included in the model to enhance clinical utility and accuracy. Conclusions Our study revealed significant triage-related factors influencing hospitalization in cats infected with FIV and FeLV, including the association of intact male status and outdoor access with FIV and anemia with FeLV. These parameters highlight the need for owner education regarding the benefits of neutering and risks associated with outdoor access. This research also highlights the necessity of vigilant monitoring for concomitant disorders in affected cats, advocating for proactive management of secondary infections, and mitigating further health complications. While regional variations may exist, our results comply with previous studies assessed around the world and with the literature, supporting the implementation of current guidelines for disease control and prevention. The employment of cross-validation techniques helps to reduce the models over fitness. This suggests a need for further refinement of the predictive models. Furthermore, the investigation should be expanded to consider variables previously excluded because of the large percentage of missing data. Incorporating the origin of the patient could potentially identify risk groups, such as stray cats, offering insights into the epidemiology of FIV and FeLV. Moreover, FeLV vaccination history should be explored to determine if the absence of it is a contributory risk factor. Future research should implement a multicentric approach to enhance the generalizability and reliability of our findings, contributing to the development of improved diagnostic tools and preventive strategies in veterinary medicine. Material and Methods Study design and setting This retrospective case-control study was conducted at the Biological Isolation and Containment Unit (BICU) between October 2013 and December 2022. BICU is a multispecies facility for the hospitalization of animals either with suspicion or confirmed infectious diseases. This establishment is separate from the TH; it has two hospitalization wards for dogs and two for cats, each one of them with a capacity for four animals. BICU is equipped with high-efficiency particulate air (HEPA) filters, a video surveillance system, personal protective equipment, and standard operating procedures and operates under negative pressure. Participants and Data Collection Data from all cats admitted to the BICU over the nine years, were retrieved from medical records stored in the TH management software Guruvet® and Qvet®. Subsequently, the records were compiled and validated using Microsoft Office Excel 365 spreadsheets. All animals that participated in this study were client-owned and joined the study after owner’s written consent and Ethical Committee approval. The study population consisted of 1211 hospitalized cats in the BICU. The cases for this population were cats infected with FIV (n = 165) and FeLV (n = 161). The inclusion criterion for each infection was a positive laboratory test explained in the sample collection and testing section. The inclusion criteria for the controls were assigned to ensure that the animals consigned to these cases did not have the disease. Therefore, for both FIV and FeLV, controls needed to have a recent retrovirus-negative test [ 20 , 28 ]. Sample testing A variety of diagnostic methods could be used for each cat presenting with TH with clinical signs of infectious diseases. For both FIV and FeLV detection, rapid immunomigration-type assays WITNESS® with a sensitivity of 93.8%, 92.9%, and specificity of 93.4%, 96.5%, respectively, were performed. In the virology and immunology laboratory of the University of Lisbon in the Faculty of Veterinary Medicine (VIL-FMV-ULisbon), Enzyme-Linked Immunosorbent Assay (ELISA) Viracheck® was performed, with the detection of antibodies in case of FIV and antigens for FeLV, with a sensitivity of 92.6%, 94.9% and specificity of 99.8%, 98.4%. After November 2021, a new ELISA kit, Vetline®, was used for FIV detection, with a sensitivity of 95.5% and specificity of 96.3%. Variables A series of questions was asked when the patient was admitted To evaluate the triage-related factors associated with FIV and FeLV. Continuous variables were converted into categorical variables. recorded data included breed (breed or mixed-breed), sex and neuter status (intact or neutered male and intact or neutered female), number of cats in the household (single cat household or multi-cat household [ 29 ]), origin (street, shelter, and breeder), lifestyle (outdoor access or strictly indoor), concomitant disorders (this variable assembles non-infectious diseases or other infectious diseases that did not need to be hospitalized in the BICU, such as chronic kidney disease, tick-borne diseases), age groups (young [< 2 years], adult [≥ 2 < 10 years], and geriatric ["≥" 10 years], according to the Feline Life Stage Guidelines [ 30 ]. Vaccination status (updated or not updated), corresponding to the 2020 AAHA/AAFP Feline Vaccination Guidelines [ 31 ]. Using the complete blood count (CBC) of the admission day (+/- 48 hours) the total leukocyte count was categorized as low if > 5.5x109/L, normal 5.5–19.5 x109/L or high > 19.5 x109/L [ 32 ]. Likewise, the hematocrit was categorized as low if 45% [ 32 ]. Bias The data used depends on the accuracy of the information shared by the owner and the precision of the veterinarian who registered the history (e.g., lack of information about the cat’s lifestyle or vaccination status). Therefore, interviewer bias may have occurred. Second, all the patients chosen to be controlled were patients in the BICU, so a Berkson’s bias could occur. This type of bias occurs when the controls are selected from hospital patients. Third, multiple animals could not be used because a definitive diagnosis was needed, which was dependent on the clinical status of the animal and the economic situation of the owner. Statistical analysis Descriptive analysis was conducted for FIV, FeLV-infected cats, and their controls hospitalized at the BICU using R version 4.2.2 (2022-10-31) for Mac. For simplicity, continuous quantitative variables as described above were converted to categorical variables such as age, leukocyte count, and hematocrit count. Categorical variables were summarized as frequencies and percentages. For the development of triage models, cases with missing data were excluded to maintain the data integrity and analytical accuracy. Additionally, covariates that exhibited more than 10% missing data were not included in the model development to prevent potential bias. Initially, simple logistic regression models were used to estimate the data. In these models, it was considered the infection status for each virus as the outcome variable and each risk factor as the only covariate infection (FIV and FeLV). All covariates with p-values < 0.05 in the simple logistic regression models were selected for the final models for each infection [ 33 ]. The covariates were considered significant in the final models when p-value < 0.05. To evaluate the predictive performance of the final models, we employed the pROC package (version 1.18.0) [ 34 ] for calculating the area under the Receiver Operating Characteristic curve (AUC). Furthermore, the pROC package was utilized to determine the optimal sensitivity and specificity using the Youden Index. To understand the predictive performance of the final models, we performed cross-validation via the package caret (version 6.0.04). Specifically, we conducted LOOCV and 5- and 10-fold cross-validation. The dataset was randomly split into “k” equal-sized subsamples for k-fold cross-validation. The AUC, sensitivity, and specificity of each cross-validated model were calculated and compared to assess the predictive performance and generalizability of our findings. Abbreviations AUC Area Under the Curve BICU Biological Isolation and Containment Unit CI Confidence Interval ELISA Enzyme-Linked Immunosorbent Assay FeLV Feline Leukemia Virus FIV Feline Immunodeficiency Virus FMV ULisboa-Faculty of Veterinary Medicine, University of Lisbon HEPA High-Efficiency Particulate Air LOOCV Leave One Out Cross-Validation ML Machine Learning PCR Polymerase Chain Reaction TH Teaching Hospital VGG Vaccination Guidelines Group VIL Virology and Immunology Laboratory Declarations Acknowledgments Not applicable. Authors’ contributions MM and IM compiled, validated, analyzed the data, and performed the statistical analysis. NS and TN helped with the statistical analysis, drafting, and revising the manuscript. LT and VA contributed to the analysis and interpretation of data. SG conceived the study and participated in its coordination, helped to draft the manuscript, and supervised throughout. All authors read and approved the final manuscript. Authors’ information Maximino, M. – Miguel Maximino, DVM, [email protected] Machado, I. – Inês Machado, DVM, [email protected] Nunes, T. – Telmo Nunes, DVM, [email protected] Tavares, L. – Luís Tavares, Professor, DVM, Full Professor, PhD, [email protected] Almeida, V. – Virgílio Almeida, Professor, DVM, Associate Professor, PhD, [email protected] Sepúlveda, N. – Nuno Sepúlveda, Assistant Professor, PhD, [email protected] *Gil, S. – Corresponding author, Solange Gil, DVM, Associate Professor, PhD, [email protected] Funding This work was supported by CIISA - Centro de Investigação Interdisciplinar em Sanidade Animal, Faculdade de Medicina Veterinária, Universidade de Lisboa, Lisboa, Portugal, Project UIDB/00276/2020 (funded by FCT). LA/P/0059/2020 - AL4AnimalS. NS was partially financed by national funds through FCT – Fundação para a Ciência e a Tecnologia under the project UIDB/00006/2020. DOI: 10.54499/UIDB/00006/2020 (https://doi.org/10.54499/UIDB/00006/2020). Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate All cats that participated in this study were client-owned animals and joined the study after the owner’s written consent and Ethical Committee approval by the Committee for Ethics and Animal Welfare (CEBEA) of the Faculty of Veterinary Medicine, University of Lisbon (ref. 014/2023). Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. References Hartmann K. Feline Leukemia Virus Infection. In: Greene CE, Stringer S, editors. Infectious Diseases of the Dog and Cat. 4th ed. St.Louis, Missouri: Elsevier Saunders; 2012. p. 224–38. Hartmann K, Hofmann-Lehmann R. What’s New in Feline Leukemia Virus Infection. Veterinary Clinics of North America - Small Animal Practice. 2020;50(5):1013–36. Leal RO, Gil S, Duarte A, McGahie D, Sepúlveda N, Niza MMRE, et al. Evaluation of viremia, proviral load and cytokine profile in naturally feline immunodeficiency virus infected cats treated with two different protocols of recombinant feline interferon omega. Res Vet Sci. 2015 Apr 1;99:87–95. Gil S, Leal RO, Duarte A, McGahie D, Sepúlveda N, Siborro I, et al. Relevance of feline interferon omega for clinical improvement and reduction of concurrent viral excretion in retrovirus infected cats from a rescue shelter. Res Vet Sci. 2013 Jun;94(3):753–63. Bande F, Arshad SS, Hassan L, Zakaria Z, Sapian NA, Rahman NA, et al. Prevalence and risk factors of feline leukaemia virus and feline immunodeficiency virus in peninsular Malaysia. BMC Vet Res. 2012 Mar 22;8(33):1–6. Sykes JE. Feline Immunodeficiency Virus Infection. In: Sykes JE, editor. Canine and Feline Infectious Diseases. 1st ed. St.Louis, Missouri: Elsevier Saunders; 2013. p. 209–23. Bęczkowski PM, Beatty JA. Feline Immunodeficiency Virus. Advances in Small Animal Care. 2022 Nov;3(1):145–59. Little S, Levy J, Hartmann K, Hofmann-Lehmann R, Hosie M, Olah G, et al. 2020 AAFP Feline Retrovirus Testing and Management Guidelines. J Feline Med Surg. 2020;22(1):5–30. Keeling MJ, Rohani P. Introduction. In: Modeling Infectious Diseases in Humans and Animals. 1st ed. New Jersey: Princton University Press; 2008. p. 1–13. Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Pantelis G, Lescure FX, et al. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clinical Microbiology and Infection. 2020;26(5):584–95. Stavisky J, Dean RS, Molloy MH. Prevalence of and risk factors for FIV and FeLV infection in two shelters in the United Kingdom (2011-2012). Veterinary Record. 2017;181(17):1–5. Biezus G, Machado G, Ferian PE, da Costa UM, Pereira LHH da S, Withoeft JA, et al. Prevalence of and factors associated with feline leukemia virus (FeLV) and feline immunodeficiency virus (FIV) in cats of the state of Santa Catarina, Brazil. Comp Immunol Microbiol Infect Dis. 2019;63:17–21. Gleich SE, Krieger S, Hartmann K. Prevalence of feline immunodeficiency virus and feline leukaemia virus among client-owned cats and risk factors for infection in Germany. J Feline Med Surg. 2009;11(12):985–92. Chhetri BK, Berke O, Pearl DL, Bienzle D. Comparison of risk factors for seropositivity to feline immunodeficiency virus and feline leukemia virus among cats: A case-case study. BMC Vet Res. 2015;11(1):1–7. Berrar D. Cross-validation. In: Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. Elsevier; 2018. p. 542–5. Ramezan CA, Warner TA, Maxwell AE. Evaluation of sampling and cross-validation tuning strategies for regional-scale machine learning classification. Remote Sens (Basel). 2019 Jan 1;11(2). Goldkamp CE, Levy JK, Edinboro CH, Lachtara JL. Seroprevalences of feline leukemia virus and Feline Immunodeficiency Virus in Cats Guidelines for Retrovirus Testing. Journal of the American Veterinary Medical Association (JAVMA). 2008;232(8):1152–8. Little S, Sears W, Lachtara J, Bienzle D. Seroprevalence of feline leukemia virus and feline immunodeficiency virus infection among cats in Canada. Canadian Veterinary Journal. 2009;50(6):644–8. Litster AL. Transmission of feline immunodeficiency virus (FIV) among cohabiting cats in two cat rescue shelters. The Veterinary Journal. 2014;201(2):184–8. Murray JK, Roberts MA, Skillings E, Morrow LD, Gruffydd-Jones TJ. Risk factors for feline immunodeficiency virus antibody test status in Cats Protection adoption centres (2004). J Feline Med Surg. 2009;11(6):467–73. Sykes JE, Hartmann K. Feline Leukemia Virus Infection. In: Sykes JE, editor. Canine and Feline Infectious Diseases. 1st ed. St.Louis, Missouri: Elsevier Saunders; 2013. p. 224–38. Hosie MJ, Addie D, Belák S, Boucraut-Baralon C, Egberink H, Frymus T, et al. Feline immunodeficiency ABCD guidelines on prevention and management. Vol. 11, Journal of Feline Medicine and Surgery. W.B. Saunders Ltd; 2009. p. 575–84. Westman ME, Coggins SJ, van Dorsselaer M, Norris JM, Squires RA, Thompson M, et al. Feline immunodeficiency virus (FIV) infection in domestic pet cats in Australia and New Zealand: Guidelines for diagnosis, prevention and management. Aust Vet J. 2022 Aug 1;100(8):345–59. Sellon RK, Hartmann K. Feline Immunodeficiency Virus Infection. In: Greene CE, Stringer S, editors. Infectious Diseass of the Dog and Cat. 4th ed. St.Louis, Missouri: Elsevier Saunders; 2012. p. 136–49. Levy JK, Scott HM, Lachtara JL, Crawford CP. Seroprevalence of feline leukemia virus and feline immunodeficiency virus infection among cats in North America and risk factors for seropositivity. Journal of the American Veterinary Medical Association (JAVMA). 2006;228(3):371–6. Muchaamba F, Mutiringindi TH, Tivapasi MT, Dhliwayo S, Matope G. A survey of feline leukaemia virus infection of domestic cats from selected areas in Harare, Zimbabwe. J S Afr Vet Assoc. 2014;85(1):1–6. Hans ML, Diane A, Sándor B, Corine B-B, Herman E, Tadeusz F, et al. Feline Leukaemia ABCD guidelines on prevention and management. J Feline Med Surg. 2009;(11):565–74. Macieira DB, de Menezes R de CAA, Damico CB, Almosny NRP, McLane HL, Daggy JK, et al. Prevalence and risk factors for hemoplasmas in domestic cats naturally infected with feline immunodeficiency virus and/or feline leukemia virus in Rio de Janeiro - Brazil. J Feline Med Surg. 2008;10(2):120–9. Finka LR, Foreman-Worsley R. Are multi-cat homes more stressful? A critical review of the evidence associated with cat group size and wellbeing. Vol. 24, Journal of Feline Medicine and Surgery. SAGE Publications Ltd; 2022. p. 65–76. Quimby J, Gowland S, Carney HC, DePorter T, Plummer P, Westropp JL. 2021 AAHA / AAFP Feline Life Stage Guidelines. J Feline Med Surg. 2021;23:211–33. Stone AES, Brummet GO, Carozza EM, Kass PH, Petersen EP, Sykes J, et al. 2020 AAHA/AAFP Feline Vaccination Guidelines. J Feline Med Surg. 2020 Sep 1;22(9):813–30. MSD. Hematology (Complete Blood Count) Reference Ranges [Internet]. 2023 [cited 2023 Sep 10]. Available from: https://www.msdvetmanual.com/multimedia/table/hematology-complete-blood-count-reference-ranges Hosmer DW, Lemeshow S. Model-Building Strategies and Methods for Logistic Regression. In: Balding DJ, Cressie NAC, Fitzmaurice GM, Goldstein H, Johnstone IM, Molenberghs G, et al., editors. Applied Logistic Regression. 3rd ed. Hoboken, New Jersey: John Wiley & Sons Ltd; 2000. p. 89–151. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011 Mar 17;12. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4248708","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291242615,"identity":"fa300ede-90e0-4767-a255-350e0e29e584","order_by":0,"name":"Miguel M Maximino","email":"","orcid":"","institution":"CIISA- Centre of Interdisciplinary Research in Animal Health, Faculty of Veterinary Medicine, University of Lisbon, Av. Universidade Técnica, 1300-477, Lisbon, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"M","lastName":"Maximino","suffix":""},{"id":291242616,"identity":"0b67fcc9-f566-4140-b592-7c4dbe7f734d","order_by":1,"name":"Inês C Machado","email":"","orcid":"","institution":"Faculty of Veterinary Medicine, University of Lisbon, Av. Universidade Técnica, 1300-477 Lisbon, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Inês","middleName":"C","lastName":"Machado","suffix":""},{"id":291242617,"identity":"7fa4f047-b08e-4984-811d-a8d4b3bc7842","order_by":2,"name":"Telmo P Nunes","email":"","orcid":"","institution":"CIISA- Centre of Interdisciplinary Research in Animal Health, Faculty of Veterinary Medicine, University of Lisbon, Av. Universidade Técnica, 1300-477, Lisbon, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Telmo","middleName":"P","lastName":"Nunes","suffix":""},{"id":291242618,"identity":"650fc99d-9ad6-4bc5-91b2-531566a51e66","order_by":3,"name":"Luís M Tavares","email":"","orcid":"","institution":"CIISA- Centre of Interdisciplinary Research in Animal Health, Faculty of Veterinary Medicine, University of Lisbon, Av. Universidade Técnica, 1300-477, Lisbon, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Luís","middleName":"M","lastName":"Tavares","suffix":""},{"id":291242619,"identity":"c81bd58c-d97d-4c7b-aff7-a23f7e889186","order_by":4,"name":"Virgílio S Almeida","email":"","orcid":"","institution":"CIISA- Centre of Interdisciplinary Research in Animal Health, Faculty of Veterinary Medicine, University of Lisbon, Av. Universidade Técnica, 1300-477, Lisbon, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Virgílio","middleName":"S","lastName":"Almeida","suffix":""},{"id":291242620,"identity":"2bf60c36-ef99-4a86-a049-fad66a12bed7","order_by":5,"name":"Nuno Sepúlveda","email":"","orcid":"","institution":"Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, Warsaw, Poland","correspondingAuthor":false,"prefix":"","firstName":"Nuno","middleName":"","lastName":"Sepúlveda","suffix":""},{"id":291242621,"identity":"408fa849-10b6-448c-839b-2c5d43a6dc99","order_by":6,"name":"Solange A Gil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYFAC5gYQKccOpgokGAwIa2EEqzXmOQCiDGBaEghrSeyBaGEgrEW+vbHxc8Evm/QesTMGzDwGFvbm7M0PmAt/4NZicOZgs/TMvrTcHukckBaJxJ09xwyYZ+CxBaimQZq353Dufum0BJCWBIMbCUC9+Bw2I7H5N2/P/3QeqBZ7gxvpH/BqYbiR2CbN8+NAAo908gGQFsYNN3Lw2wL0S5s1b0OyYQ9Qy8E5YL+cKTg8Iw2Pw9qbD9/m+WMnzyOd2PjgTUUdMMTaNz4usMHjMBBgbIPQB2AChwloAII/aHxmwlpGwSgYBaNgBAEAQ6FNL/KYYOoAAAAASUVORK5CYII=","orcid":"","institution":"CIISA- Centre of Interdisciplinary Research in Animal Health, Faculty of Veterinary Medicine, University of Lisbon, Av. Universidade Técnica, 1300-477, Lisbon, Portugal","correspondingAuthor":true,"prefix":"","firstName":"Solange","middleName":"A","lastName":"Gil","suffix":""}],"badges":[],"createdAt":"2024-04-10 17:29:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4248708/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4248708/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54811245,"identity":"b11ac35f-12a0-45ee-8e98-862986c3a79d","added_by":"auto","created_at":"2024-04-17 06:19:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65151,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curves for the final regression models of FIV and FeLV infections.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4248708/v1/e02b9a2043e62bef5d8f63c3.png"},{"id":74773322,"identity":"edf9714f-a578-4687-8471-0bfd05d0a9d2","added_by":"auto","created_at":"2025-01-26 12:01:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1255611,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4248708/v1/7367b197-fda8-43d1-93a0-71290c190406.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Developing a Feline Infectious Disease Triage Model: Insights from Logistic Regression Models in Data from a Veterinary Isolation Unit","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Biological Isolation and Containment Unit (BICU) of the Teaching Hospital (TH) of the Faculty of Veterinary Medicine, University of Lisbon (FMV-ULisboa) admits patients with the confirmation or suspicion of an infectious disease. The number of hospitalized cats since the foundation of the Unit in October 2013 to December 2022 was 1211. The majority of the cases were diagnosed as infections caused by Feline Immunodeficiency Virus (FIV) and Feline Leukemia Virus (FeLV).\u003c/p\u003e \u003cp\u003eFIV and FeLV are the most common infectious agents in domestic cats [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. FIV is a lentivirus [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], primarily transmitted through bites during fights and persists throughout life [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. FeLV is a gammaretrovirus transmitted via contact with saliva and other secretions from infected cats[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Both diseases present nonspecific clinical signs and are usually characterized by anemia, immunodeficiency, neoplasia (mainly lymphoma), and secondary infections [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the prevalence of FIV and FeLV infection has been decreasing in recent years, it has stagnated in some countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Understanding the triage-related factors associated with these infections is crucial for rapid clinical management of feline patients. Common factors include older age, intact males, outdoor access, close contact with infected cats, aggressive behavior, and mixed breeds [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Given their substantial impact on feline health, particularly in our clinical setting where they constitute the majority of hospitalized cases, developing accurate triage models for FIV and FeLV is imperative. These models have the potential to streamline clinical management by facilitating the rapid identification and prioritization of true infected feline patients among the suspected cases.\u003c/p\u003e \u003cp\u003eCurrently, there is growing interest in applying statistical and machine learning (ML) methods to healthcare [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In particular, ML is becoming a popular tool in the development of clinical decision support systems for infection disease management (e.g., early detection of clinical signs and risk factors, case monitoring, improved diagnosis of atypical cases, selection of new treatments, or exiting treatments for other diseases) and diagnostic purposes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile previous studies employed logistic regression to identify risk factors for FIV and FeLV [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], these studies showed limitations while inferring the predictive performance of the estimated models due to the absence of a cross-validation step. Cross-validation is a statistical technique that helps evaluate the generalizability of the predictions provided by statistical and ML models. To evaluate model performance, the training set was split into sections according to the chosen cross-validation method. This process is repeated so that each section is used as a test fold once [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe primary goal of this study was to aid in the development of a more accurate triage tool for predicting true infected feline patients among suspected cases of FIV and FeLV infections. To accomplish this, our specific aims included a general characterization of FIV- and FeLV-infected cats and their controls, assembling a control group for each disease, determining potential triage-related factors associated with hospitalization due to these retroviruses, and comparing logistic regression models. Upon successful development, these triage models will serve as invaluable tools in the BICU, enabling timely and informed decision making in the triaging of feline patients, thereby improving overall clinical outcomes and resource utilization.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBasic Characteristics of the FIV and FeLV-infected cats and their controls\u003c/h2\u003e \u003cp\u003eThe initial database consisted of 1211 cats. Of these, 165 and 161 cats were confirmed to have FIV and FeLV infections, respectively. We excluded the patients from the analysis if they had missing values in any of the covariates; therefore, the new data frame contained 134 FIV-infected cats and 126 FeLV-infected cats (complete case scenario).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the demographic variables of the FIV- and FeLV-infected cats and their controls.\u003c/p\u003e \u003cp\u003eThe majority of FIV-infected cats were adult, mixed-breed, neutered males, living with multiple cats, with outdoor access and presenting concomitant disorders. While FeLV-infected cats were also mixed-breed, adult cats with concomitant disorders, the majority lived exclusively indoors, and presented a lower hematocrit count.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic characteristics of FIV and FeLV-infected cats and their controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eFeLV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase, n\u0026thinsp;=\u0026thinsp;134 Cats (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl, n\u0026thinsp;=\u0026thinsp;268 Cats (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCase, n\u0026thinsp;=\u0026thinsp;126 Cats (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControl, n\u0026thinsp;=\u0026thinsp;252 Cats (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBreed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePurebred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed-breed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 (96.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e242 (90.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122 (96.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e230 (91.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSex and Neuter Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutered Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (53.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (39.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (31.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e114 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntact Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutered Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (31.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntact Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of cats in the household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle Cat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 Cats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (56.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167 (62.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e146 (57.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLifestyle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164 (61.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e153 (60.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutdoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (61.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99 (39.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConcomitant Disorders/ Diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (82.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178 (66.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99 (78.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e171 (67.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge Groups\u003c/p\u003e \u003cp\u003e(Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 \u0026amp; \u0026lt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (61.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (64.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116 (46.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78 (31.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLeukocytes Classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (59.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156 (58.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e148 (58.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (25.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHematocrit Classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (57.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149 (55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134 (53.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredictive analysis of hospitalization due to FIV infections\u003c/h3\u003e\n\u003cp\u003eAge emerged as a significant triage-related factor for FIV infection (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both adult and senior cats (\u0026ge;\u0026thinsp;2 \u0026amp; \u0026lt; 10 and \u0026ge;\u0026thinsp;10 years) were the most frequent and significantly associated with an increased risk of hospitalization in the logistic regression model. Although mixed-breed cats, living with multiple cats, and having a normal hematocrit and leukocyte count were more frequent, these factors were not significantly associated with this infection.\u003c/p\u003e \u003cp\u003eIntact male cats with outdoor access and concomitant disorders were found to be at a higher risk of hospitalization due to FIV infections (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe final logistic regression model had an estimated AUC of 0.71 with a 95% confidence interval (CI) [0.66\u0026ndash;0.76] before cross-validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The sensitivity and specificity estimates were 0.63 and 0.69, respectively, which suggested that this model had similar predictive performance on true cases and true controls. After cross-validation based on LOOCV, the AUC decreased to 0.66, with an increased sensitivity of 0.86, but a lower specificity of 0.33. The 10-fold and 5-fold cross-validations yielded similar results with an AUC of 0.69, with a sensitivity of 0.85 and 0.86 and specificity of 0.30 and 0.32, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSimple and multiple regression models for predicting the probability of hospitalization due to FIV infection (n\u0026thinsp;=\u0026thinsp;402).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eSimple Logistic Regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMultiple Logistic Regression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eStd error\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eStd error\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBreed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePurebred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed-breed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSex and Neuter Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntact Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntact Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutered Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutered Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of cats in the household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLifestyle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutdoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConcomitant Disorders/ Diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge Groups (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 \u0026amp; \u0026lt; 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eClassification of Leukocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eClassification of Hematocrit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eHigh\u0026gt;19.5 x10\u003csup\u003e9\u003c/sup\u003e/L; \u003csup\u003e2\u003c/sup\u003eNormal: 5.5\u0026ndash;19.5 x10\u003csup\u003e9\u003c/sup\u003e/L; \u003csup\u003e3\u003c/sup\u003eLow \u0026lt; 5.5x10\u003csup\u003e9\u003c/sup\u003e/L;\u003c/p\u003e \u003cp\u003e\u003csup\u003e4\u003c/sup\u003eHigh \u0026gt;\u0026thinsp;45%; \u003csup\u003e5\u003c/sup\u003eNormal: 30\u0026ndash;45%; \u003csup\u003e6\u003c/sup\u003eLow \u0026lt;\u0026thinsp;30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePredictive analysis of hospitalization due to FeLV infections\u003c/h2\u003e \u003cp\u003eFor FeLV-infected cats, our study identified that mixed-breed, concomitant disorders and a low hematocrit count were significantly associated with FeLV infection (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Remarkably, neutered male cats displayed a significantly lower risk of being infected with FeLV and hospitalized (estimate = -0.836, p\u0026thinsp;=\u0026thinsp;0.011).\u003c/p\u003e \u003cp\u003eEven though adult cats, living indoors, with multiple animals, and presenting normal leukocyte counts were the most prevalent demographic groups, they were not significantly associated with FeLV infection.\u003c/p\u003e \u003cp\u003eBefore using cross-validation, the final logistic regression model implied an AUC of 0.67 with a 95%CI [0.62\u0026ndash;0.73] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The corresponding sensitivity and specificity were estimated at 0.63 and 0.65.\u003c/p\u003e \u003cp\u003eTo better understand the model performance, we applied cross-validation. LOOCV yielded an AUC of 0.63, with high sensitivity (0.88) but low specificity (0.29). A 10-fold cross-validation presented an AUC of 0.66, with a sensitivity of 0.88, and specificity of 0.25. Similarly, a 5-fold cross-validation revealed an AUC of 0.64, with the previous sensitivity and a further reduced specificity (0.19).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSimple and Multiple regression models for predicting the probability of hospitalization due to FeLV infection (n\u0026thinsp;=\u0026thinsp;378).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eSimple Logistic Regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMultiple Logistic Regression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eStd error\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eStd error\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBreed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePurebred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed-breed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSex and Neuter Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntact Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntact Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutered Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutered Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of cats in the household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLifestyle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutdoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConcomitant Disorders/ Diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge Groups (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 \u0026amp; \u0026lt; 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eClassification of Leukocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eClassification of Hematocrit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eHigh\u0026gt;19.5 x10\u003csup\u003e9\u003c/sup\u003e/L; \u003csup\u003e2\u003c/sup\u003eNormal: 5.5\u0026ndash;19.5 x10\u003csup\u003e9\u003c/sup\u003e/L; \u003csup\u003e3\u003c/sup\u003eLow \u0026lt; 5.5x10\u003csup\u003e9\u003c/sup\u003e/L;\u003c/p\u003e \u003cp\u003e\u003csup\u003e4\u003c/sup\u003eHigh \u0026gt;\u0026thinsp;45%; \u003csup\u003e5\u003c/sup\u003eNormal: 30\u0026ndash;45%; \u003csup\u003e6\u003c/sup\u003eLow \u0026lt;\u0026thinsp;30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFIV infections\u003c/h2\u003e \u003cp\u003eRegarding the variable breed, there was no significant association, corroborating other researchers who found no evidence of breed predisposition to hospitalization due to a FIV infection [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAgreeing with multiple studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], our study found that intact male cats were at a higher risk of being hospitalized due to FIV infection. FIV is shed in saliva, and one of the most common ways of transmission is through bite wounds [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, this association is in line with male cats having a higher likelihood of encountering infected cats, and being prone to aggression and territorial fights. Evidence of an association between intact cats and FIV positivity, mainly in intact male cats, has been found in multiple studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Neutered cats are more likely to be kept indoors and have a lower risk of infection [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsistent with other research studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], our analysis also observed that cohabitation with other cats did not significantly influence admission to the BICU with FIV. This is particularly evident in a study conducted by Litster, in which no transmission of FIV was recorded over several years among 130 uninfected cats living with eight FIV-infected cats [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA correlation was found between outdoor access and being hospitalized with FIV. Outdoor access is a possible triage-related factor, because free-roaming cats are more likely to encounter infected cats [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFIV is an immunosuppressive disease; therefore, FIV-infected cats may be predisposed to secondary and opportunistic infections [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similar to FeLV, it is difficult to determine whether the cat\u0026rsquo;s health is a cause or effect of FIV infection [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. FIV in cats with concomitant disorders was significantly associated with an increased risk of hospitalization in the BICU. Another study showed a similar association with sick cats that were more likely to test positive for FIV [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile bone marrow suppression can occur in FIV infections, it is noteworthy that this phenomenon is more commonly associated with FeLV-infected cats [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In FeLV cases, bone marrow disorders, including anemia, are often observed [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, in FIV infections, the acute phase is characterized by mild neutropenia, and in later stages, lymphopenia may occur [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Despite these hematological changes, our study did not find a significant association between leukocyte or hematocrit levels and being hospitalized with FIV. This result is consistent with the general understanding that FIV is typically less pathogenic than FeLV [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. FIV-infected cats can remain asymptomatic or mildly symptomatic for an extended period, and the progression of the disease varies among individuals [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeing hospitalized with FIV was statistically relevant in cats between the ages of \u0026ge;\u0026thinsp;2 \u0026amp; \u0026lt; 10 and \u0026ge;\u0026thinsp;10 years, respectively, compared to cats with less than 2 years. This result was consistent with previous observations that an increase in age leads to susceptibility of contracting FIV [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This increase in age can be explained by the fact that FIV-positive cats can remain asymptomatic for many years and only express clinical signs later in life when diagnosed at that stage[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFeLV infection\u003c/h2\u003e \u003cp\u003eTypically, FeLV does not have a breed predisposition, however, in this study, an association between mixed-breed cats and being hospitalized with FeLV was found. According to Hartmann and Hofmann-Lehmann [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] FeLV is commonly found in mixed-breed cats because purebreds tend to be kept indoors, and there is greater awareness from the owners to test their animals. This aligns with the broader understanding that indoor management and proactive healthcare in purebred cats may have a lower risk of infection. However, other studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] have not found any association.\u003c/p\u003e \u003cp\u003eRegarding sex and neuter status, neutered males were associated with being less likely to be admitted to the BICU with FeLV. A possible explanation is that an owner who has a neutered cat is more vigilant and aware of the risks. Older studies, state that FeLV infection was found to be approximately equal between sexes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The explanation for this is that FeLV transmission frequently occurs between infected queens and kittens, and among cats living in close contact, infection can occur regardless of sex [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, in more recent studies, male cats appeared to be at a greater risk of contracting the disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. There is no consensus about the neuter status, and some researchers indicated that intact cats, especially males, are more at risk of infection [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. While other authors did not find any association [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This could be because of the differences between the studied populations; while some studies are about owned cats, others are made at shelters or with stray cats, which leads to different results. Surprisingly, cohabitation with multiple cats was not associated with an increase of hospitalization with FeLV. A possible reason stated by Gleich et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] was that the increasing awareness of this disease makes owners more susceptible to testing and more careful when adding a new cat to a household of multiple animals. The results of the present study were unexpected because previous studies have stated that FeLV is a \u0026ldquo;social\u0026rdquo; disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe non-significant association between outdoor access and FeLV hospitalization challenges the traditional view of outdoor exposure as a risk factor. While previous literature suggests that outdoor access increases the risk of infection [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], other contemporary studies had the same result as ours [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This outcome suggests that indoor cats are not risk-free, potentially due to in-home transmission among cohabiting cats or unknown history of adopted animals.\u003c/p\u003e \u003cp\u003eFeLV in cats with concomitant disorders was significantly associated with an increased risk of hospitalization. This association is plausible because, like FIV, FeLV is an immunosuppressive disease; therefore, these cats are predisposed to opportunistic and secondary infections [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It is important to remark that the health of the cat at the time of infection and the time after infection can be unknown, so it is difficult to assume whether the cat\u0026rsquo;s health is a cause or an effect of FeLV infection [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile FeLV is commonly associated with bone marrow suppression, the most prevalent hematological abnormality observed in our study, as well as in the literature is anemia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our findings determined that a lower hematocrit was significantly associated with an increased risk of being hospitalized. It is important to note that while bone marrow suppression can affect other blood cell lines such as leukocytes, our study did not find a significant association between leukocyte classification and hospitalization in FeLV-infected cats.\u003c/p\u003e \u003cp\u003eFeLV is considered to be age-dependent, so when cats mature, they become more resistant to the infection [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Thus, younger cats are more likely to be progressively infected with FeLV [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, in more recent studies adult cats were more likely to be infected with FeLV [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A possible explanation is that, because of an increase in awareness, owners test their cats more frequently; therefore, cats are provided with medical care earlier, leading to a longer life [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In our study, age was not significantly associated with being hospitalized with FeLV. A possible reason for this is that the ages of our FeLV-infected cats and their control groups were similar, and infection can occur at any life stage.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEvaluation of model performance\u003c/h3\u003e\n\u003cp\u003eTo our knowledge, this study is the first to employ cross-validation techniques to evaluate logistic regression models to identify triage-related factors in FIV- and FeLV-infected cats.\u003c/p\u003e \u003cp\u003eFor FIV-infected cats, our logistic regression model demonstrated a modest predictive ability (AUC\u0026thinsp;=\u0026thinsp;0.71) without cross-validation. Subsequent cross-validation procedures led to similar results. While sensitivity remained high (\u0026ge;\u0026thinsp;0.85), specificity was modest (\u0026asymp;\u0026thinsp;0.30), indicating a tendency to overpredict FIV infection. Despite the stable predictive capability across various data subsets, further model refinement is required to mitigate overfitting.\u003c/p\u003e \u003cp\u003eFor FeLV-infected cats, our logistic regression model exhibited a moderate ability (AUC\u0026thinsp;=\u0026thinsp;0.67) to identify infection before cross-validation. However, various cross-validation techniques, particularly LOOCV, revealed a decreased AUC (0.63) and specificity (0.29), despite high sensitivity (\u0026ge;\u0026thinsp;0.88). This pattern of high sensitivity but low specificity underscores the risk of misclassification, and highlights the need for model enhancement to reduce overdiagnosis. It is noteworthy that both ROC curves for FIV- and FeLV-infected cats exhibited similar patterns, showing a comparable predictive ability of the models for both infections.\u003c/p\u003e \u003cp\u003eWe chose to perform cross-validation because of the limitation of our dataset, which was not sufficiently large to split into independent training and testing sets. While cross-validation provides a robust method for estimating model performance, an independent validation set would be ideal for future studies to further validate the efficacy of our models.\u003c/p\u003e \u003cp\u003eIn summary, while our models demonstrated promising sensitivities in detecting FIV and FeLV infections, the modest specificities suggest that additional covariates should be included in the model to enhance clinical utility and accuracy.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study revealed significant triage-related factors influencing hospitalization in cats infected with FIV and FeLV, including the association of intact male status and outdoor access with FIV and anemia with FeLV. These parameters highlight the need for owner education regarding the benefits of neutering and risks associated with outdoor access. This research also highlights the necessity of vigilant monitoring for concomitant disorders in affected cats, advocating for proactive management of secondary infections, and mitigating further health complications.\u003c/p\u003e \u003cp\u003eWhile regional variations may exist, our results comply with previous studies assessed around the world and with the literature, supporting the implementation of current guidelines for disease control and prevention.\u003c/p\u003e \u003cp\u003eThe employment of cross-validation techniques helps to reduce the models over fitness. This suggests a need for further refinement of the predictive models. Furthermore, the investigation should be expanded to consider variables previously excluded because of the large percentage of missing data. Incorporating the origin of the patient could potentially identify risk groups, such as stray cats, offering insights into the epidemiology of FIV and FeLV. Moreover, FeLV vaccination history should be explored to determine if the absence of it is a contributory risk factor.\u003c/p\u003e \u003cp\u003eFuture research should implement a multicentric approach to enhance the generalizability and reliability of our findings, contributing to the development of improved diagnostic tools and preventive strategies in veterinary medicine.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis retrospective case-control study was conducted at the Biological Isolation and Containment Unit (BICU) between October 2013 and December 2022. BICU is a multispecies facility for the hospitalization of animals either with suspicion or confirmed infectious diseases. This establishment is separate from the TH; it has two hospitalization wards for dogs and two for cats, each one of them with a capacity for four animals. BICU is equipped with high-efficiency particulate air (HEPA) filters, a video surveillance system, personal protective equipment, and standard operating procedures and operates under negative pressure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Data Collection\u003c/h2\u003e \u003cp\u003eData from all cats admitted to the BICU over the nine years, were retrieved from medical records stored in the TH management software Guruvet\u0026reg; and Qvet\u0026reg;. Subsequently, the records were compiled and validated using Microsoft Office Excel 365 spreadsheets. All animals that participated in this study were client-owned and joined the study after owner\u0026rsquo;s written consent and Ethical Committee approval.\u003c/p\u003e \u003cp\u003eThe study population consisted of 1211 hospitalized cats in the BICU. The cases for this population were cats infected with FIV (n\u0026thinsp;=\u0026thinsp;165) and FeLV (n\u0026thinsp;=\u0026thinsp;161). The inclusion criterion for each infection was a positive laboratory test explained in the sample collection and testing section.\u003c/p\u003e \u003cp\u003eThe inclusion criteria for the controls were assigned to ensure that the animals consigned to these cases did not have the disease. Therefore, for both FIV and FeLV, controls needed to have a recent retrovirus-negative test [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSample testing\u003c/h2\u003e \u003cp\u003eA variety of diagnostic methods could be used for each cat presenting with TH with clinical signs of infectious diseases. For both FIV and FeLV detection, rapid immunomigration-type assays WITNESS\u0026reg; with a sensitivity of 93.8%, 92.9%, and specificity of 93.4%, 96.5%, respectively, were performed. In the virology and immunology laboratory of the University of Lisbon in the Faculty of Veterinary Medicine (VIL-FMV-ULisbon), Enzyme-Linked Immunosorbent Assay (ELISA) Viracheck\u0026reg; was performed, with the detection of antibodies in case of FIV and antigens for FeLV, with a sensitivity of 92.6%, 94.9% and specificity of 99.8%, 98.4%. After November 2021, a new ELISA kit, Vetline\u0026reg;, was used for FIV detection, with a sensitivity of 95.5% and specificity of 96.3%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eVariables\u003c/h2\u003e \u003cp\u003eA series of questions was asked when the patient was admitted To evaluate the triage-related factors associated with FIV and FeLV. Continuous variables were converted into categorical variables. recorded data included breed (breed or mixed-breed), sex and neuter status (intact or neutered male and intact or neutered female), number of cats in the household (single cat household or multi-cat household [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]), origin (street, shelter, and breeder), lifestyle (outdoor access or strictly indoor), concomitant disorders (this variable assembles non-infectious diseases or other infectious diseases that did not need to be hospitalized in the BICU, such as chronic kidney disease, tick-borne diseases), age groups (young [\u0026lt;\u0026thinsp;2 years], adult [\u0026ge;\u0026thinsp;2\u0026thinsp;\u0026lt;\u0026thinsp;10 years], and geriatric [\"\u0026ge;\" 10 years], according to the Feline Life Stage Guidelines [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Vaccination status (updated or not updated), corresponding to the 2020 AAHA/AAFP Feline Vaccination Guidelines [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Using the complete blood count (CBC) of the admission day (+/- 48 hours) the total leukocyte count was categorized as low if\u0026thinsp;\u0026gt;\u0026thinsp;5.5x109/L, normal 5.5\u0026ndash;19.5 x109/L or high\u0026thinsp;\u0026gt;\u0026thinsp;19.5 x109/L [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Likewise, the hematocrit was categorized as low if\u0026thinsp;\u0026lt;\u0026thinsp;30%, normal 30\u0026ndash;45%, and high if\u0026thinsp;\u0026gt;\u0026thinsp;45% [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBias\u003c/h2\u003e \u003cp\u003eThe data used depends on the accuracy of the information shared by the owner and the precision of the veterinarian who registered the history (e.g., lack of information about the cat\u0026rsquo;s lifestyle or vaccination status). Therefore, interviewer bias may have occurred. Second, all the patients chosen to be controlled were patients in the BICU, so a Berkson\u0026rsquo;s bias could occur. This type of bias occurs when the controls are selected from hospital patients. Third, multiple animals could not be used because a definitive diagnosis was needed, which was dependent on the clinical status of the animal and the economic situation of the owner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive analysis was conducted for FIV, FeLV-infected cats, and their controls hospitalized at the BICU using R version 4.2.2 (2022-10-31) for Mac. For simplicity, continuous quantitative variables as described above were converted to categorical variables such as age, leukocyte count, and hematocrit count. Categorical variables were summarized as frequencies and percentages. For the development of triage models, cases with missing data were excluded to maintain the data integrity and analytical accuracy. Additionally, covariates that exhibited more than 10% missing data were not included in the model development to prevent potential bias.\u003c/p\u003e \u003cp\u003eInitially, simple logistic regression models were used to estimate the data. In these models, it was considered the infection status for each virus as the outcome variable and each risk factor as the only covariate infection (FIV and FeLV). All covariates with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the simple logistic regression models were selected for the final models for each infection [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The covariates were considered significant in the final models when p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eTo evaluate the predictive performance of the final models, we employed the pROC package (version 1.18.0) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] for calculating the area under the Receiver Operating Characteristic curve (AUC). Furthermore, the pROC package was utilized to determine the optimal sensitivity and specificity using the Youden Index.\u003c/p\u003e \u003cp\u003eTo understand the predictive performance of the final models, we performed cross-validation via the package caret (version 6.0.04). Specifically, we conducted LOOCV and 5- and 10-fold cross-validation. The dataset was randomly split into \u0026ldquo;k\u0026rdquo; equal-sized subsamples for k-fold cross-validation. The AUC, sensitivity, and specificity of each cross-validated model were calculated and compared to assess the predictive performance and generalizability of our findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBiological Isolation and Containment Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eELISA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnzyme-Linked Immunosorbent Assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFeLV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFeline Leukemia Virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFeline Immunodeficiency Virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFMV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eULisboa-Faculty of Veterinary Medicine, University of Lisbon\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHEPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Efficiency Particulate Air\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOOCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeave One Out Cross-Validation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eML\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMachine Learning\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTeaching Hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVaccination Guidelines Group\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVirology and Immunology Laboratory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e MM and IM compiled, validated, analyzed the data, and performed the statistical analysis. NS and TN helped with the statistical analysis, drafting, and revising the manuscript. LT and VA contributed to the analysis and interpretation of data. SG conceived the study and participated in its coordination, helped to draft the manuscript, and supervised throughout. All authors read and approved the final manuscript. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaximino, M. – Miguel Maximino, DVM, [email protected]\u003c/p\u003e\n\u003cp\u003eMachado, I. – Inês Machado, DVM, [email protected]\u003c/p\u003e\n\u003cp\u003eNunes, T. – Telmo Nunes, DVM, [email protected] \u003c/p\u003e\n\u003cp\u003eTavares, L. – Luís Tavares, Professor, DVM, Full Professor, PhD, [email protected]\u003c/p\u003e\n\u003cp\u003eAlmeida, V. – Virgílio Almeida, Professor, DVM, Associate Professor, PhD, [email protected] \u003c/p\u003e\n\u003cp\u003eSepúlveda, N. – Nuno Sepúlveda, Assistant Professor, PhD, [email protected] \u003c/p\u003e\n\u003cp\u003e*Gil, S. – Corresponding author, Solange Gil, DVM, Associate Professor, PhD, [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by CIISA - Centro de Investigação Interdisciplinar em Sanidade Animal, Faculdade de Medicina Veterinária, Universidade de Lisboa, Lisboa, Portugal, Project UIDB/00276/2020 (funded by FCT). LA/P/0059/2020 - AL4AnimalS. NS was partially financed by national funds through FCT – Fundação para a Ciência e a Tecnologia under the project UIDB/00006/2020. DOI: 10.54499/UIDB/00006/2020 (https://doi.org/10.54499/UIDB/00006/2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll cats that participated in this study were client-owned animals and joined the study after the owner’s written consent and Ethical Committee approval by the Committee for Ethics and Animal Welfare (CEBEA) of the Faculty of Veterinary Medicine, University of Lisbon (ref. 014/2023). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHartmann K. Feline Leukemia Virus Infection. In: Greene CE, Stringer S, editors. Infectious Diseases of the Dog and Cat. 4th ed. St.Louis, Missouri: Elsevier Saunders; 2012. p. 224\u0026ndash;38. \u003c/li\u003e\n\u003cli\u003eHartmann K, Hofmann-Lehmann R. What\u0026rsquo;s New in Feline Leukemia Virus Infection. Veterinary Clinics of North America - Small Animal Practice. 2020;50(5):1013\u0026ndash;36. \u003c/li\u003e\n\u003cli\u003eLeal RO, Gil S, Duarte A, McGahie D, Sep\u0026uacute;lveda N, Niza MMRE, et al. Evaluation of viremia, proviral load and cytokine profile in naturally feline immunodeficiency virus infected cats treated with two different protocols of recombinant feline interferon omega. Res Vet Sci. 2015 Apr 1;99:87\u0026ndash;95. \u003c/li\u003e\n\u003cli\u003eGil S, Leal RO, Duarte A, McGahie D, Sep\u0026uacute;lveda N, Siborro I, et al. 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Advances in Small Animal Care. 2022 Nov;3(1):145\u0026ndash;59. \u003c/li\u003e\n\u003cli\u003eLittle S, Levy J, Hartmann K, Hofmann-Lehmann R, Hosie M, Olah G, et al. 2020 AAFP Feline Retrovirus Testing and Management Guidelines. J Feline Med Surg. 2020;22(1):5\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eKeeling MJ, Rohani P. Introduction. In: Modeling Infectious Diseases in Humans and Animals. 1st ed. New Jersey: Princton University Press; 2008. p. 1\u0026ndash;13. \u003c/li\u003e\n\u003cli\u003ePeiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Pantelis G, Lescure FX, et al. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clinical Microbiology and Infection. 2020;26(5):584\u0026ndash;95. \u003c/li\u003e\n\u003cli\u003eStavisky J, Dean RS, Molloy MH. Prevalence of and risk factors for FIV and FeLV infection in two shelters in the United Kingdom (2011-2012). Veterinary Record. 2017;181(17):1\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eBiezus G, Machado G, Ferian PE, da Costa UM, Pereira LHH da S, Withoeft JA, et al. Prevalence of and factors associated with feline leukemia virus (FeLV) and feline immunodeficiency virus (FIV) in cats of the state of Santa Catarina, Brazil. Comp Immunol Microbiol Infect Dis. 2019;63:17\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eGleich SE, Krieger S, Hartmann K. Prevalence of feline immunodeficiency virus and feline leukaemia virus among client-owned cats and risk factors for infection in Germany. J Feline Med Surg. 2009;11(12):985\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eChhetri BK, Berke O, Pearl DL, Bienzle D. Comparison of risk factors for seropositivity to feline immunodeficiency virus and feline leukemia virus among cats: A case-case study. BMC Vet Res. 2015;11(1):1\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eBerrar D. Cross-validation. In: Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. Elsevier; 2018. p. 542\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eRamezan CA, Warner TA, Maxwell AE. Evaluation of sampling and cross-validation tuning strategies for regional-scale machine learning classification. Remote Sens (Basel). 2019 Jan 1;11(2). \u003c/li\u003e\n\u003cli\u003eGoldkamp CE, Levy JK, Edinboro CH, Lachtara JL. Seroprevalences of feline leukemia virus and Feline Immunodeficiency Virus in Cats Guidelines for Retrovirus Testing. Journal of the American Veterinary Medical Association (JAVMA). 2008;232(8):1152\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eLittle S, Sears W, Lachtara J, Bienzle D. Seroprevalence of feline leukemia virus and feline immunodeficiency virus infection among cats in Canada. Canadian Veterinary Journal. 2009;50(6):644\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eLitster AL. Transmission of feline immunodeficiency virus (FIV) among cohabiting cats in two cat rescue shelters. The Veterinary Journal. 2014;201(2):184\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eMurray JK, Roberts MA, Skillings E, Morrow LD, Gruffydd-Jones TJ. Risk factors for feline immunodeficiency virus antibody test status in Cats Protection adoption centres (2004). J Feline Med Surg. 2009;11(6):467\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eSykes JE, Hartmann K. Feline Leukemia Virus Infection. In: Sykes JE, editor. Canine and Feline Infectious Diseases. 1st ed. St.Louis, Missouri: Elsevier Saunders; 2013. p. 224\u0026ndash;38. \u003c/li\u003e\n\u003cli\u003eHosie MJ, Addie D, Bel\u0026aacute;k S, Boucraut-Baralon C, Egberink H, Frymus T, et al. Feline immunodeficiency ABCD guidelines on prevention and management. Vol. 11, Journal of Feline Medicine and Surgery. W.B. Saunders Ltd; 2009. p. 575\u0026ndash;84. \u003c/li\u003e\n\u003cli\u003eWestman ME, Coggins SJ, van Dorsselaer M, Norris JM, Squires RA, Thompson M, et al. Feline immunodeficiency virus (FIV) infection in domestic pet cats in Australia and New Zealand: Guidelines for diagnosis, prevention and management. Aust Vet J. 2022 Aug 1;100(8):345\u0026ndash;59. \u003c/li\u003e\n\u003cli\u003eSellon RK, Hartmann K. Feline Immunodeficiency Virus Infection. In: Greene CE, Stringer S, editors. Infectious Diseass of the Dog and Cat. 4th ed. St.Louis, Missouri: Elsevier Saunders; 2012. p. 136\u0026ndash;49. \u003c/li\u003e\n\u003cli\u003eLevy JK, Scott HM, Lachtara JL, Crawford CP. Seroprevalence of feline leukemia virus and feline immunodeficiency virus infection among cats in North America and risk factors for seropositivity. Journal of the American Veterinary Medical Association (JAVMA). 2006;228(3):371\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eMuchaamba F, Mutiringindi TH, Tivapasi MT, Dhliwayo S, Matope G. A survey of feline leukaemia virus infection of domestic cats from selected areas in Harare, Zimbabwe. J S Afr Vet Assoc. 2014;85(1):1\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eHans ML, Diane A, S\u0026aacute;ndor B, Corine B-B, Herman E, Tadeusz F, et al. Feline Leukaemia ABCD guidelines on prevention and management. J Feline Med Surg. 2009;(11):565\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eMacieira DB, de Menezes R de CAA, Damico CB, Almosny NRP, McLane HL, Daggy JK, et al. Prevalence and risk factors for hemoplasmas in domestic cats naturally infected with feline immunodeficiency virus and/or feline leukemia virus in Rio de Janeiro - Brazil. J Feline Med Surg. 2008;10(2):120\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eFinka LR, Foreman-Worsley R. Are multi-cat homes more stressful? A critical review of the evidence associated with cat group size and wellbeing. Vol. 24, Journal of Feline Medicine and Surgery. SAGE Publications Ltd; 2022. p. 65\u0026ndash;76. \u003c/li\u003e\n\u003cli\u003eQuimby J, Gowland S, Carney HC, DePorter T, Plummer P, Westropp JL. 2021 AAHA / AAFP Feline Life Stage Guidelines. J Feline Med Surg. 2021;23:211\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eStone AES, Brummet GO, Carozza EM, Kass PH, Petersen EP, Sykes J, et al. 2020 AAHA/AAFP Feline Vaccination Guidelines. J Feline Med Surg. 2020 Sep 1;22(9):813\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eMSD. Hematology (Complete Blood Count) Reference Ranges [Internet]. 2023 [cited 2023 Sep 10]. Available from: https://www.msdvetmanual.com/multimedia/table/hematology-complete-blood-count-reference-ranges\u003c/li\u003e\n\u003cli\u003eHosmer DW, Lemeshow S. Model-Building Strategies and Methods for Logistic Regression. In: Balding DJ, Cressie NAC, Fitzmaurice GM, Goldstein H, Johnstone IM, Molenberghs G, et al., editors. Applied Logistic Regression. 3rd ed. Hoboken, New Jersey: John Wiley \u0026amp; Sons Ltd; 2000. p. 89\u0026ndash;151. \u003c/li\u003e\n\u003cli\u003eRobin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011 Mar 17;12. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Triage, Epidemiology, Infectious Diseases, Feline Leukemia Virus, Feline Immunodeficiency Virus, Predictive Models, Cross-Validation, Cats ","lastPublishedDoi":"10.21203/rs.3.rs-4248708/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4248708/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe Biological Isolation and Containment Unit (BICU) of the Faculty of Veterinary Medicine, University of Lisbon, is dedicated to treating animals with suspected or confirmed infectious diseases. Feline Immunodeficiency Virus (FIV) and Feline Leukemia Virus (FeLV) are two of the most common infections reported in this unit. This study explored the use of logistic regression to predict FIV and FeLV infections in the triage stage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOf 1211 cats treated at the BICU since its opening, 134 cats were FIV-positive and 126 FeLV-positive. Significant triage-related factors for FIV-related hospitalization included being an adult or senior cat, intact males, having access to the outdoors, and presenting concomitant disorders. In contrast, mixed-breed cats with concomitant disorders and a low hematocrit count were significant risk factors for FeLV-related hospitalization. The estimated logistic regression models without cross-validation showed areas under the Receiver Operating Characteristic curve (AUC) of 0.71 for FIV and 0.67 for FeLV, with 95% CI of [0.66-0.76] and [0.62-0.73], respectively. Cross-validation highlighted high sensitivity but low specificity for both infections, indicating a higher propensity for false positives. When cross-validation was performed for FIV infections, the resulting AUC was 0.66, and the specificity was 0.33 using 10- and 5-fold cross validations. The models for FeLV exhibited similar predictive performance with an AUC of 0.63 and specificity of 0.29, which decreased further with 10- and 5-fold cross validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study highlights significant triage-related factors for FIV and FeLV infections, in agreement with existing literature. These findings indicate a need for better clinical vigilance and owner education, mainly on neutering and the risk of outdoor access. Future research should expand to other predictive models and include other variables important to predict FIV and FeLV at the triage stage.\u003c/p\u003e","manuscriptTitle":"Developing a Feline Infectious Disease Triage Model: Insights from Logistic Regression Models in Data from a Veterinary Isolation Unit","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-17 06:19:48","doi":"10.21203/rs.3.rs-4248708/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a483279-02f2-4f3b-b956-22b3cc41d1b2","owner":[],"postedDate":"April 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-26T11:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-17 06:19:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4248708","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4248708","identity":"rs-4248708","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}