Statistical Analysis
Categorical data is summarized as number (percentage), and continuous data as mean (standard deviation). Significance was determined by Chi-square test (Fisher exact test) for categorical variables and either Kruskal-Wallis (non-parametric) or 1-way ANOVA (parametric) testing for continuous variables. Normality for all continuous variables was determined using skewness and kurtosis. Initial regression analysis and ROC curve generation for predictors of blood culture positivity were performed using stepwise binary logistic regression (ɑ to enter model = 0.05 and ɑ to exit model = 0.1). Machine learning via a classification and regression tree (CART) analysis was performed to model non-linear relationships between predictor variables of interest and bacteremia and to generate an optimal decision tree diagram. The CART model was first constructed using a training data set (70% of study population data) and then its efficacy tested via a testing data set (30% of study population data). Statistical significance was defined as P ≤ 0.05. All statistical analysis and figures were obtained using Minitab® Statistical Software version 21.1.0.