Abstract
BACKGROUND: ESKAPE bacteria are thought to be especially resistant to antibiotics, and their resistance and prevalence in bloodstream infections are rising. Large studies are needed to better characterize the clinical impact of these bacteria and to develop algorithms that alert clinicians when patients are at high risk of an ESKAPE infection. METHODS: From a US data set of >1.1 M patient encounters, we evaluated if ESKAPE pathogens produced worse outcomes than non-ESKAPE pathogens and if an ESKAPE infection could be predicted using simple word group algorithms built from decision trees. RESULTS: We found that ESKAPE pathogens represented 42.2% of species isolated from bloodstream infections and, compared with non-ESKAPE pathogens, were associated with a 3.3-day increase in length of stay, a $5500 increase in cost of care, and a 2.1% absolute increase in mortality (P < 1e-99). ESKAPE pathogens were not universally more resistant to antibiotics, but only to select antibiotics (P < 5e-6), particularly against common empiric therapies. In addition, simple word group algorithms predicted ESKAPE pathogens with a positive predictive value of 7.9% to 56.2%, exceeding 4.8% by random guessing (P < 1e-99). CONCLUSIONS: Taken together, these data highlight the pathogenicity of ESKAPE bacteria, potential mechanisms of their pathogenicity, and the potential to predict ESKAPE infections upon admission. Implementing word group algorithms could enable earlier and targeted therapies against ESKAPE bacteria and thus reduce their burden on the health care system.