Clinical and laboratory predictors for bacteremia in critically ill calves.

Abstract

Background: Sepsis is a main contributor to calf mortality, but diagnosis is difficult.

Objectives: Develop and validate a predictive model for bacteremia in critically ill calves (CIC).

Animals: A total of 334 CIC, sampled for blood culture.

Methods: Cross-sectional study. Multivariable logistic regression and classification tree analysis on clinical, ultrasonographic, and laboratory variables were performed on a dataset including all animals. Model validation was done on 30% of the dataset. Similar statistics (except validation) were performed on a subset of the database (n = 143), in which presumed contaminants were excluded.

Results: The best performing model to predict bacteremia, taking all detected bacteria into account, included tachypnea, tachycardia, acidemia, hypoglycemia, venous hypoxemia, and hypoproteinemia. Sensitivity and specificity of this model were 70.6% and 98.0%, respectively, but decreased to 61.5% and 91.7% during model validation. The best-performing model, excluding presumed contaminants, included abnormal temperature, heart rate, absence of enteritis, hypocalcemia, and hyperlactatemia as risk factors for bacteremia. Sensitivity and specificity of this model were 71.4% and 93.9%, respectively. Both classification trees performed less well in comparison to logistic regression. The classification tree excluding presumed contaminants, featured hypoglycemia, absence of diarrhea, and hyperlactatemia as risk factors for bacteremia. Sensitivity and specificity were 39.4% and 92.7%, respectively.

Conclusions and clinical importance: Hypoglycemia, hyperlactatemia, and hypoproteinemia seem relevant in assessing bacteremia in CIC. The performance of these models based on basic clinical and blood variables remains insufficient to predict bacteremia.