Fig. 5

Classification and regression tree analysis to predict bacterial community-acquired pneumonia in patients presenting with lower respiratory tract infection at outpatient clinics in Tanzania. a All variables (vital signs and biomarkers) were added to the model. b Forced first respiratory rate and PCT were added to the model. c Forced first respiratory rate and PCT cut-off 0.25 µg/l were added to the model. For all models, the cost of misclassifying a patient that had bacterial community-acquired pneumonia as 10 times the cost of misclassifying patients that had other lower respiratory tract infection. Cut points selected by the analysis are indicated between the parent and child nodes. Below each terminal node, the predicted categorization for those patients is indicated. Algorithm performance characteristics are presented in Table 3. PCT procalcitonin