Predictive model of antimicrobial-resistant Gram-negative bacteremia at the ED

Background: Despite numerous studies identifying the risk factors related to Gram-negative antimicrobial resistance, an epidemiological model to reliably predict antimicrobial Gram-negative resistance in clinics, before the bacterial culture result is available, has not yet been developed. Objectives: The aim of this study was to develop a predictive model to assist physicians in selecting appropriate antimicrobial agents before the details of the microbiology and drug susceptibility are known. Materials and Methods: A prospective study was conducted between June 1, 2001, and May 31, 2002, at the emergency department (ED) of National Taiwan University Hospital. Enrollees were patients with Gram-negative bacteremia (GNB) at ED. Other information collected included demographic characteristics, underlying comorbidities, hospital exposure and health care-associated factors, and details of initial presentation. Two primary outcomes were defined, including cefazolin-resistant (CZ-RES) GNB and ceftriaxone-resistant (CTX-RES) GNB. Two thirds of the data was randomly allocated to a derivation data set (for developing predictive models), and the rest, to a validation data set (for testing model validity). Simplified models, using a coefficient-based scoring method, were also developed for clinical applications. Results: Based on 695 episodes of GNB, predictors of CZ-RES GNB were time since last hospitalization (increased risk for durations 15 000 /mm3) at entry to ED. In this case, however, previous hospitalization within the last 2 weeks was a key factor. The area under this ROC curve was 0.82 (95% confidence interval, 0.76-0.88). There was lacking of difference in the area under the ROC curve between the 2 final (simplified) models either based on the derivation or validation data sets. Conclusion: We have developed 2 models for predicting risk of antimicrobial Gram-negative infection by identifying and quantifying associated risk factors. These models could be used by physicians to determine the most appropriate choice of antibiotic for first-line therapy, particularly in situations where the culture result is not yet known.