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 <1 month), prior infection with a CTX-RES strain, post-transplantation immunosuppressant use, residence in a nursing home or history of stroke with repeated choking, and poor oxygen saturation (<95%) at admission to ED. Cirrhosis showed a protective effect by reducing the odds of antimicrobial-resistant GNB. The area under receiver operating characteristic (ROC) curve for the CZ-RES model was 0.76 (95% confidence interval, 0.71-0.81). The CTX-RES model included all the variables that were in the CZ-RES model plus abnormal leukocyte count (<1000 or >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.