Total thyroxine, triiodothyronine, and thyrotropin concentrations during acute nonthyroidal illness and recovery in dogs.

BACKGROUND: Acute illness can result in changes in serum total thyroxine (tT4), total triiodothyronine (tT3), and thyrotropin (TSH) concentrations in euthyroid dogs defined as nonthyroidal illness syndrome, but longitudinal evaluation of these hormones during the recovery phase is lacking. OBJECTIVES: To longitudinally evaluate serum tT4, tT3, and TSH concentrations during the acute phase and recovery from acute illness in dogs. ANIMALS: Nineteen euthyroid client-owned dogs hospitalized for acute illness at a veterinary teaching hospital. METHODS: Prospective longitudinal study. Serum tT4, tT3, and TSH concentrations were measured at the admission (T0), at last day of hospitalization (T1), and during the recovery phase at 3, 7, 14, and 21 days after the discharge (T2, T3, T4, and T5), respectively. RESULTS: tT4 and tT3 were below the reference interval (RI) at T0 in 3 (16%) and 18 (95%) dogs, respectively; tT4 normalized in all dogs early in the recovery phase, while low tT3 persisted at the end of the study in 16 (83%) dogs. Median TSH concentrations were increased at T5 compared with T1 (0.19 ng/mL [range 0.03-0.65] vs 0.11 ng/mL [range (0.05-0.26)], mean difference = 0.09 ng/mL; P = .03). Five (26%) dogs had TSH above the RI at least at 1 time point during the recovery phase. None of the dogs had concurrent low tT4 and high TSH during the study. CONCLUSIONS AND CLINICAL RELEVANCE: In euthyroid dogs acute illness can interfere with evaluation of thyroid function up to 21 days during the recovery phase. Thyroid testing should be avoided or postponed in these dogs.

Detection of Anaplasma spp. and Ehrlichia spp. in dogs from a veterinary teaching hospital in Italy: a retrospective study 2012-2020.

Anaplasma phagocytophilum, Anaplasma platys and Ehrlichia canis, responsible of diseases in dogs, are tick-borne pathogens with a proven or potential zoonotic role that have shown increasing prevalence worldwide. The aims of this retrospective study were to assess the frequency of Anaplasma spp. and Ehrlichia spp. exposure in dogs tested in a veterinary teaching hospital in Italy over a 9-year period, to compare the performance of the diagnostic tests used, to evaluate correlations with clinical data, and to genetically analyse the identified bacteria. During the study period, 1322 dogs tested by at least one of the rapid immunoenzymatic test, indirect immunofluorescent antibody test or end-point PCR assay for Anaplasmataceae detection were included. Dogs were tested if they had clinical signs or clinicopathological alteration or risk factors related to infection, and if they were potential blood-donor animals. Ninety-four of 1322 (7.1%) dogs tested positive for at least one pathogen: 53 (4.3%) for A. phagocytophilum, one (0.1%) for A. platys and 63 (4.6%) for E. canis. The number of dogs tested increased and the positivity rate progressively declined over the years. Comparison of tests showed a near-perfect agreement between serological tests and a poor agreement between PCR and indirect assays. A breed predisposition has been highlighted for A. phagocytophilum infection in hunting breed dogs and for E. canis infection in mixed breed dogs. Phylogeny confirmed potential zoonotic implications for A. phagocytophilum and showed no correlation of the identified bacteria with the geographical origin. Our study provides new insights into possible risk factors in dogs and evidenced discordant results between different tests, suggesting that a combination of serological and molecular assays is preferable for a correct diagnosis.

Development and internal validation of diagnostic prediction models using machine-learning algorithms in dogs with hypothyroidism.

Introduction: Hypothyroidism can be easily misdiagnosed in dogs, and prediction models can support clinical decision-making, avoiding unnecessary testing and treatment. The aim of this study is to develop and internally validate diagnostic prediction models for hypothyroidism in dogs by applying machine-learning algorithms. Methods: A single-institutional cross-sectional study was designed searching the electronic database of a Veterinary Teaching Hospital for dogs tested for hypothyroidism. Hypothyroidism was diagnosed based on suggestive clinical signs and thyroid function tests. Dogs were excluded if medical records were incomplete or a definitive diagnosis was lacking. Predictors identified after data processing were dermatological signs, alopecia, lethargy, hematocrit, serum concentrations of cholesterol, creatinine, total thyroxine (tT4), and thyrotropin (cTSH). Four models were created by combining clinical signs and clinicopathological variables expressed as quantitative (models 1 and 2) and qualitative variables (models 3 and 4). Models 2 and 4 included tT4 and cTSH, models 1 and 3 did not. Six different algorithms were applied to each model. Internal validation was performed using a 10-fold cross-validation. Apparent performance was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). Results: Eighty-two hypothyroid and 233 euthyroid client-owned dogs were included. The best performing algorithms were naive Bayes in model 1 (AUROC = 0.85; 95% confidence interval [CI] = 0.83-0.86) and in model 2 (AUROC = 0.98; 95% CI = 0.97-0.99), logistic regression in model 3 (AUROC = 0.88; 95% CI = 0.86-0.89), and random forest in model 4 (AUROC = 0.99; 95% CI = 0.98-0.99). Positive predictive value was 0.76, 0.84, 0.93, and 0.97 in model 1, 2, 3, and 4, respectively. Negative predictive value was 0.89, 0.89, 0.99, and 0.99 in model 1, 2, 3, and 4, respectively. Discussion: Machine learning-based prediction models were accurate in predicting and quantifying the likelihood of hypothyroidism in dogs based on internal validation performed in a single-institution, but external validation is required to support the clinical applicability of these models.

Are there sex differences in physiological parameters and reaction time responses to overload in firefighters?

Male and female firefighters work side-by-side in the same in strenuous and risky conditions. Anthropometrics, physiological, and reaction time (mean of reaction time -MRT-, and errors made -E) parameters of 12 Female and 13 Male firefighters were compared. Effect of overload (step test with and without equipment) on the MRT and E were analyzed on 3 trials (T1 = 1-1s, T2 = 0.5-1s, T3 = 0.5-0.5s), compared with a pre-test condition (basal). T-test between males and females was applied to assess differences (p<0.05) in all parameters. ANOVA with repeated measures and Bonferroni on 3 conditions of step test between males and females was applied in reaction time variables. Between MRT and E, in T1, T2 and T3 trials and the 3 test conditions, ANCOVA models with interactions were used. Differences (p<0.05) in anthropometric, physiological and reaction time data emerged across groups, and on the 3rd trials (T3 vs T1 and T2) in reaction time parameters of each group. ANCOVA showed differences (p<0.001) in E among trials. Post hoc showed significant differences in T1vsT3 and T1vsT2. MRT x trial interaction was extremely significant (P<0.001). Implementing fitness and reaction time exercise programs is important to decrease the injury risk and increase work capacity in firefighters with reference to female workers.