Identifying patient-level risk factors associated with non-β-lactam resistance outcomes in invasive methicillin-resistant Staphylococcus aureus infections in the United States using chain graphs

Abstract

ABSTRACTMethicillin-resistant Staphylococcus aureus (MRSA) is one of the most common causes of hospital- and community-acquired infections. MRSA is resistant to many antibiotics, including ß-lactam antibiotics, fluoroquinolones, lincosamides, macrolides, aminoglycosides, tetracyclines, and chloramphenicol. Graphical models such as chain graphs can be used to quantify and visualize the interactions among multiple resistant outcomes and their explanatory variables. In this study, we analyzed MRSA surveillance data collected by the Centers for Disease Control and Prevention (CDC) as part of the Emerging Infections Program (EIP) using chain graphs with the objective of identifying risk factors for individual phenotypic resistance outcomes (reported as minimum inhibitory concentration, MIC) while considering the correlations among themselves. Some phenotypic resistances have low connectivity to other outcomes or predictors (e.g. tetracycline, vancomycin, doxycycline, and rifampin). Levofloxacin was the only resistant associated with healthcare use. Blood culture was the most common predictor of MIC. Patients with positive blood culture had significantly increased MIC to chloramphenicol, erythromycin, gentamicin, lincomycin, and mupirocin, and decreased daptomycin and rifampin MICs. Chain graphs show the unique and common risk factors associated with resistance outcomes.