Identifying groups at high risk for carriage of antibiotic-resistant bacteria

Background: No simple, cost-effective methods exist to identify patients at high risk for methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci colonization outside intensive care settings. Without such methods, colonized patients are entering hospitals undetected and transmitting these bacteria to other patients. We aimed to develop a highly sensitive, simple-to-administer prediction rule to identify subpopulations of patients at high risk for colonization on hospital admission. Methods: We conducted a prospective cohort study of adult patients admitted to the general medical and surgical wards of a tertiary-care facility. Data were collected using electronic medical records and an investigator-administered questionnaire. Cultures of anterior nares and the perirectal area were also collected within 48 hours of admission. Results: Among 699 patients who enrolled in this study, 697 underwent nasal cultures; 555, perirectal cultures; and 553, both. Patient self-report of a hospital admission in the previous year was the most sensitive variable in identifying patients colonized with methicillin-resistant Staphylococcus aureus or with either organism (sensitivity, 76% and 90%, respectively). A prediction rule requiring patients to self-report having received antibiotics and a hospital admission in the previous year would have identified 100% of patients colonized with vancomycin-resistant enterococci. In the high-risk groups defined by the prediction rule, the prevalence of colonization by methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci, or either organism were 8.1%, 10.2%, and 15.0%, respectively. Conclusion: Patients with a self-reported previous admission within 1 year may represent a high-risk group for colonization by methicillin-resistant Staphylococcus aureus or vancomycin-resistant enterococci at hospital admission and should be considered for targeted active surveillance culturing.