OBJECTIVES: Leptospirosis is a re-emergent zoonotic bacterial disease associated with renal and hepatic injury. In free-roaming cats in some regions, a high prevalence of Leptospira antibodies has been identified, and pathogenic leptospires have been detected in renal tissue, indicating that they may play a role in Leptospira epidemiology. The objective of this study was to determine the prevalence of Leptospira seroreactivity and urinary shedding of Leptospira DNA in free-roaming cats from northern California and southern Texas. A secondary objective was to compare the results of a point-of-care (POC) assay, designed to detect Leptospira antibodies, with the results of the microscopic agglutination test (MAT) when applied to serum samples from feral cats. METHODS: Specimens were obtained from free-roaming cats from northern California (n = 52; 2020) and southern Texas (n = 75; 2017). Leptospira quantitative PCR was performed on blood and urine specimens from Californian cats. Serum samples from Californian and Texan cats were subjected to MAT to categorize them as Leptospira antibody-positive or antibody-negative. The performance of the POC assay was assessed using the MAT as the gold standard. RESULTS: Leptospira DNA was not detected in the blood or urine of any cats tested. The results of the MAT were positive in 17.3% (n = 9) of Californian cats and 10.7% (n = 8) of Texan cats (P = 0.3). The median MAT titer was 1:100 (range 1:100-1:200) in Californian cats and 1:200 (range 1:100-1:800) in Texan cats. The POC assay was negative in all specimens. CONCLUSIONS AND RELEVANCE: Free-roaming cats in California and Texas are exposed to Leptospira species and may have the potential to act as sentinel hosts. No cats had evidence of current infection, as determined using PCR on blood and urine specimens. The POC test did not reliably detect anti-Leptospira antibodies in these cats. The role of cats in the maintenance or shedding of pathogenic leptospires requires further investigation.

Background

Clinical findings of glucocorticoid-deficient hypoadrenocorticism (GDH) can overlap with other diseases, presenting a diagnostic challenge.

Objectives

Describe clinicopathologic and ultrasonographic features of dogs with GDH and those suspected of having GDH that had the disease ruled out.

Animals

Six hundred twenty-three dogs.

Methods

Records from dogs with suspected GDH between 2003 and 2018 were reviewed. Dogs with hyperkalemia or hyponatremia were excluded. Dogs were categorized as controls when the resting serum cortisol or post-ACTH cortisol concentration were > 2 μg/dL. Clinicopathologic and ultrasonographic features were compared between groups. The optimal cut-point, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated for individual features used to detect GDH.

Results

Dogs were categorized as GDH (n = 29) or controls (n = 594). Lymphocyte count (>1750 cells/L; sensitivity, 96.6%; 95% confidence interval [CI], 82.8%-99.8%; specificity, 60.3%; 95% CI, 56.3%-64.1%; AUC, 0.828; 95% CI, 0.762-0.894) and albumin/globulin ratio (<1.081; sensitivity, 86.2%; 95% CI, 69.4%-94.5%; specificity, 78.8%; 95% CI, 75.3%-81.9%; AUC, 0.886; 95% CI, 0.827-0.944) had the highest discriminatory power between groups. Left adrenal gland width <0.39 cm was 80% (95% CI, 58.4%-91.9%) sensitive and 82.4% (95% CI, 74.2-88.4) specific for GDH. Serum cobalamin concentrations and ultrasonographic abnormalities of the GI tract were not different between groups.

Conclusion and clinical importance

No single variable could be used to confidently rule out GDH and obviate the need for cortisol testing in dogs with a clinical presentation consistent with GDH.

To assess sex-specific differences in coccidioidomycosis, a retrospective analysis of human patients, nonhuman primates, and veterinary patients (including the neutered status of the animal) was performed. We found higher rates of infection and severity in males. This observed increased infection risk suggests deeper biological underpinnings than solely occupational/exposure risks.

Leptospirosis is a life-threatening, zoonotic disease with various clinical presentations, including renal injury, hepatic injury, pancreatitis, and pulmonary hemorrhage. With prompt recognition of the disease and treatment, 90% of infected dogs have a positive outcome. Therefore, rapid, early diagnosis of leptospirosis is crucial. Testing for Leptospira-specific serum antibodies using the microscopic agglutination test (MAT) lacks sensitivity early in the disease process, and diagnosis can take >2 wk because of the need to demonstrate a rise in titer. We applied machine-learning algorithms to clinical variables from the first day of hospitalization to create machine-learning prediction models (MLMs). The models incorporated patient signalment, clinicopathologic data (CBC, serum chemistry profile, and urinalysis = blood work [BW] model), with or without a MAT titer obtained at patient intake (=BW + MAT model). The models were trained with data from 91 dogs with confirmed leptospirosis and 322 dogs without leptospirosis. Once trained, the models were tested with a cohort of dogs not included in the model training (9 leptospirosis-positive and 44 leptospirosis-negative dogs), and performance was assessed. Both models predicted leptospirosis in the test set with 100% sensitivity (95% CI: 70.1-100%). Specificity was 90.9% (95% CI: 78.8-96.4%) and 93.2% (95% CI: 81.8-97.7%) for the BW and BW + MAT models, respectively. Our MLMs outperformed traditional acute serologic screening and can provide accurate early screening for the probable diagnosis of leptospirosis in dogs.