1166. Development of a Bedside Tool to Predict the Probability of Drug-Resistant Pathogens Among an Adult Population With Gram-Negative Infections

Abstract

Background. Identification of infections caused by antimicrobial-resistant microorganisms is critical to administration of early appropriate antibiotic therapy. We developed a clinical bedside tool to estimate the probability of carbapenem- resistant Enterobacteriaceae (CRE), extended spectrum β-lactamase-producing Enterobacteriaceae (ESBL), and multidrug-resistant Pseudomonas aeruginosa (MDRP) among hospitalized adult patients with Gram-negative infections. Methods. A retrospective observational study of the Premier Hospital Database (PHD) was conducted. The study included adult hospitalized patients with complicated urinary tract infection (cUTI), complicated intraabdominal infection (cIAI), bloodstream infections (BSI), or hospital-acquired/ventilator-associated pneumonia (HAP/VAP) with a culture-confirmed Gram-negative infection in PHD from 2011 to 2015. Model development steps are shown in Figure 1. The study population was split into training and test cohorts. Prediction models were developed using logistic regression in the training cohort (Figure 1). For each resistant phenotype (CRE, ESBL, and MDRP), a separate model was developed for community-acquired (index culture ≤3 days of admission) and hospital-acquired (index culture >3 days of admission) infections (six models in total). The predictive performance of the models was assessed in the training and test cohorts. Models were converted to a singular user-friendly interface for use at the bedside. Results. The most important predictors of antibiotic-resistant Gram-negative bacterial infection were prior number of antibiotics, infection site, prior infection in the last 3 months, hospital prevalence of each resistant pathogen (CRE, ESBL, and MDRP), and age (Figure 2). The predictive performance was highly acceptable for all six models (Figure 3). Conclusion. We developed a clinical prediction tool to estimate the probability of CRE, ESBL, and MDRP among hospitalized adult patients with community- and hospital- acquired Gram-negative infections. Our predictive model has been implemented as a user-friendly bedside tool for use by clinicians to predict the probability of resistant infections in individual patients, to guide early appropriate therapy. (Figure Presented).