Background: Prediction of bacterial infections and their pathogens allows for early, directed investigation and treatment. We assessed the ability of TREAT, a computerized decision support system, to predict specific pathogens.
Methods: TREAT uses data available within the first few hours of infection presentation in a causal probabilistic network to predict sites of infection and specific pathogens. We included 3529 patients (920 with microbiologically documented infections) participating in the observational and interventional trials of the TREAT system in Israel, Germany and Italy. Discriminatory performance of TREAT to predict individual pathogens was expressed by the AUC with 95% confidence intervals. Calibration was assessed using the Hosmer-Lemeshow goodness-of-fit statistic.
Results: The AUCs for Gram-negative bacteria, including Pseudomonas aeruginosa, Acinetobacter baumannii, Klebsiella spp. and Escherichia coli, ranged between 0.70 and 0.80 (all significant). Adequate calibration was demonstrated for any Gram-negative infection and individual bacteria, except for E. coli. Discrimination and calibration were acceptable for Enterococcus spp. (AUC 0.71, 0.65-0.78), but not for Staphylococcus aureus (AUC 0.63, 0.55-0.71). The few infections caused by Candida spp. and Clostridium difficile were well predicted (AUCs 0.74, 0.54-0.95; and 0.94, 0.88-1.00, respectively). The coverage with TREAT's recommendation exceeded that observed with physicians' treatment for all pathogens, except Candida spp.
Conclusions: TREAT predicted individual pathogens causing infection well. Prediction of S. aureus was inferior to that observed with other pathogens. TREAT can be used to triage patients by the risk for specific pathogens. The system's predictions enable it to prescribe appropriate antibiotic treatment prior to pathogen identification.