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.