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 highrisk 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. Risk stratification at the time of disease onset allows for appropriate and economical management decisions. Among patients with infection, bacteremia portends a poor prognosis, and clinicians' ability to predict its presence is low [1, 2] . Blood cultures are routinely performed in hospitals for patients with possible bacteremia, even when the risk is low. It is currently accepted that only ∼10%-15% of blood culture results are positive, of which 30%-50% of the results represent contaminants [3] [4] [5] . The costs of performing and handling negative and false-positive blood culture results are significant. Bates et al. [6] reported a 20% independent increase in total hospital charges for patients with contaminated blood cultures associated with additional testing, empirical treatment, and increased hospital stay. Empirical treatment frequently entails unnecessary use of vancomycin [7] . Defining of a group of patients with a very low probability of bacteremia would reduce costs and prevent unnecessary antibiotic treatment. Alternatively, selection of a group with a high likelihood for bacteremia caused by specific pathogens may assist health care providers in choosing treatment and determining whether to perform new, costly tests. Molecular methods for rapid pathogen identification or resistance testing may be used clinically [8] , but they are likely to be costeffective only among preselected patients [9] . . The basic units of the model are pathogens (boxes) for which the probabilities are determined by site-specific risk factors (e.g., presence of a urinary catheter, place of infection acquisition, sex, and age for urinary tract infections). Pathogens cause local infections, which cause local signs or symptoms (diamonds) and systemic signs of sepsis through sepsis mediators. For simplicity, single variables are shown: 1 pathogen (Escherichia coli), 2 sites of infection (urinary tract and abdominal infections), a single sepsis mediator, and 2 sepsis signs (fever and chills). Pathogens causing infections may be found in local samples (Ecoli_urin; Ecoli_local) and in the blood. The presence of bacteria in blood may be detected in aerobic and anaerobic bottles (Ecoli_Aerob; Ecoli_Anaerob). Each CPN node represents a probability table. Additional explanation can be found in the Appendix We developed a computerized decision support system (TREAT) based on a causal probabilistic network for diagnosis and treatment of infections among inpatients [10] [11] [12] . The network predicts the probabilities of the presence of pathogens on the basis of clinical variables available at the time treatment is initiated. It also predicts the probabilities of pathogen growth in the bloodstream and pathogen detection in blood culture bottles. We assessed the ability of TREAT to predict bacteremia in a prospective cohort of inpatients. With use of TREAT's bacteremia prediction values, we determined thresholds to define 3 patient risk groups with increasing risks and prevalences of bacteremia. We applied the same thresholds to a second, independent, prospective cohort and assessed the prevalence of bacteremia within each of the 3 TREAT risk groups to test the stability of TREAT's means of bacteremia prediction. METHODS The TREAT system is based on a causal probabilistic network (CPN) [10] [11] [12] . A CPN consists of a set of variables and directed links between these variables. Links reflect cause-effect relationships. The strength of an effect is modeled as a probability. The basic units of the TREAT model are pathogens. The model follows the pathogenesis of infection (figure 1). Additional explanation can be found in the Appendix. We differentiated between universal and local probabilities. Universal probabilities do not change locally or temporally. These include sensitivities and specificities of symptoms, signs, and diagnostic tests; the probability for bacteremia given a known site of infection; and baseline prevalence of specific conditions or factors (e.g., presence of prosthetic heart valve). The probabilities for these variables were based on published systematic reviews or our own compilation of available literature. The majority of the variables in the CPN are calibrated locally; these include pathogen prevalence in the different sites of infection, pathogen prevalence for blood culture contamination, antibiotic susceptibilities, baseline prevalence of certain underlying conditions (e.g., drug abuse), etc. TREAT was calibrated to 3 hospitals using local data. The system may be used at any decision point during the course of antibiotic therapy. The present study addressed the patient-physician encounter at the time that blood samples were drawn for culture. Input data included the patients' demographic characteristics, background conditions, devices (e.g., presence of catheter), vital signs, laboratory test results, symptoms and signs relevant to infection, and available radiological (e.g., chest radiography) and microbiological (e.g., local Gram
Another Bayesian-network-based decision-support system, called TREAT, aiming at predicting bacteremia in patients is described in REF .
Prediction of Bacteremia Using TREAT, a Computerized Decision-Support System
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Medicine