Association of folate concentrations with clinical signs and laboratory markers of chronic enteropathy in dogs.

BACKGROUND: Serum folate is considered a biomarker of chronic enteropathy (CE) in dogs, but few studies have examined associations with markers of CE. HYPOTHESIS/OBJECTIVES: To evaluate serum folate concentrations in dogs with and without CE and associations with sample hemolysis and selected markers of CE. We hypothesized that hypofolatemia would be more common in dogs with CE and associated with hypocobalaminemia, higher CIBDAI, and hypoalbuminemia. ANIMALS: Six hundred seventy-three dogs with available serum folate measurements performed at an academic veterinary hospital between January 2016 and December 2019. METHODS: Medical records were retrospectively reviewed to categorize cases as CE or non-CE and record clinical details and laboratory markers. Relationships between serum folate, cobalamin, and CE variables were assessed using chi-square, Kruskal-Wallis, or Spearman's correlation tests. RESULTS: Of the 673 dogs, 99 CE were compared to 95 non-CE. In the overall cohort, serum folate concentration did not correlate with sample hemolysis (P = .75). In the CE subset, serum folate and cobalamin concentrations were positively associated (rho = 0.34, FDR = 0.02). However, serum folate concentrations (median [25th, 75th percentiles]) were higher (CE: 12.1 (8.9, 16.1), non-CE: 10.4 (7.2, 15.5); P = .04) and cobalamin concentrations were lower (CE: 343 (240, 597), non-CE: 550 (329, 749); P = .001) in the CE vs non-CE group. Serum folate was not associated with markers of CE, but serum cobalamin was associated with albumin (P = .04) and cholesterol (P = .03). CONCLUSIONS AND CLINICAL IMPORTANCE: Hypofolatemia is an inferior biomarker of CE compared to hypocobalaminemia.

Use of machine-learning algorithms to aid in the early detection of leptospirosis in dogs.

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.