1165. Comparing Patient Risk Factors, Sequence Type, and Resistance Loci Identification Approaches for Predicting Antibiotic Resistance in Escherichia coli Bloodstream Infections

Abstract

BackgroundTo improve the adequacy of empiric antibiotic therapy, an important predictor of clinical outcome, rapid diagnostic tests of antibiotic resistance are increasingly being developed that identify the presence or absence of antibiotic resistance genes/Loci. Few approaches have utilized other sources of predictive information, which could be identified in shorter time periods, including patient epidemiologic risk factors for antibiotic resistance and markers of lineage (e.g., sequence type).MethodsUsing a dataset of 414 Escherichia coli isolated from separate episodes of bacteremia at a single academic institution in Toronto, Canada between 2010 and 2015, we compared the potential predictive ability of three approaches (epidemiologic, sequence type, and gene identification) for classifying antibiotic resistance to three commonly used classes of broad-spectrum antibiotic therapy (third-generation cephalosporins, fluoroquinolones, and aminoglycosides). We used logistic regression models with binary predictor variables to generate model receiver operating characteristic curves. Predictive discrimination was measured using apparent and corrected (bootstrapped) area under the curves (AUCs).ResultsUsing two simple epidemiologic risk factors (prior antibiotic exposure and recent prior Gram-negative susceptibility), modest predictive discrimination was achieved (AUCs 0.65–0.74). Sequence type demonstrated strong discrimination (AUCs 0.84–0.94) across all three antibiotic classes. Epidemiologic risk factors significantly improved sequence-type prediction for cephalosporins and aminoglycosides (P < 0.05). Gene identification approaches provided the highest degree of discrimination (AUCs 0.73–0.99), with no statistically significant benefit of adding epidemiologic predictors.ConclusionRapid identification of sequence type, or other lineage-based classification, could produce excellent discrimination of antibiotic resistance, and may be improved by incorporating readily available epidemiologic predictors. Disclosures All authors: No reported disclosures.