Prediction of bacteremia using TREAT, a computerized decision-support system

Background. Prediction of bloodstream infection at the time of sepsis onset allows one to make appropriate and economical management decisions. Methods. The TREAT computerized decision-support system uses a causal probabilistic network, which is locally calibrated, to predict cases of bacteremia. We assessed the system's performance in 2 independent cohorts that included patients with suspected sepsis. Both studies were conducted in Israel, Italy, and Germany. Data were collected prospectively and were entered into the TREAT system at the time that blood samples were obtained for culture. Discriminative power was assessed using a receiver-operating characteristics curve. Results. In the first cohort, 790 patients were included. The area under the receiver-operating characteristics curve for prediction of bacteremia using the TREAT system was 0.68 (95% confidence interval [CI], 0.63-0.73). We used TREAT's prediction values to draw thresholds defining a low-,intermediate-, and high- risk groups for bacteremia, in which 3 (2.4%) of 123, 62 (12.8%) of 483, and 55 (29.9%) of 184 patients were bacteremic, respectively. In the second cohort, 1724 patients were included. The area under the receiver-operating characteristics curve was 0.70 ( 95% CI, 0.67-0.73). The prevalence of bacteremia observed in the low-, intermediate-, and high-risk groups defined by the first cohort were 1.3% ( 4 of 300 patients), 13.2% ( 150 of 1139 patients), and 28.1% ( 80 of 285 patients), respectively. The low- risk groups in the 2 cohorts comprised 15%-17% of all patients. Performance was stable in the 3 sites. Conclusions. Using variables available at the time that blood cultures were performed, the TREAT system successfully stratified patients on the basis of the risk for bacteremia. The system's predictions were stable in 3 locations. The TREAT system can define a low- risk group of inpatients with suspected sepsis for whom blood cultures may not be needed.