Rapid Identification of Hospitalized Patients at High Risk for MRSA Carriage

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 15; no. 4; pp. 506 - 512
• Main Authors: Evans, R. Scott, Wallace, Carrie Jane, Lloyd, James F., Taylor, Caroline W., Abouzelof, Rouett H., Sumner, Sharon, Johnson, Kyle V., Wuthrich, Amyanne, Harbarth, Stephan, Samore, Matthew H.
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 01.07.2008; American Medical Informatics Association
• Subjects: Algorithms; Application of Information Technology; Carrier State - diagnosis; Cross Infection - prevention & control; Decision Support Systems, Clinical; Disease Reservoirs; Disease Transmission, Infectious - prevention & control; Hospitalization; Humans; Infection Control - methods; Medical Records Systems, Computerized; Methicillin Resistance; Nose - microbiology; Population Surveillance - methods; Reminder Systems; Risk; Staphylococcal Infections - diagnosis; Staphylococcus aureus; Staphylococcus aureus - isolation & purification; Time Factors
• ISSN: 1067-5027; 1527-974X
• DOI: 10.1197/jamia.M2721

Patients who are asymptomatic carriers of methicillin-resistant Staphylococcus aureus (MRSA) are major reservoirs for transmission of MRSA to other patients. Medical personnel are usually not aware when these high-risk patients are hospitalized. We developed and tested an enterprise-wide electronic surveillance system to identify patients at high risk for MRSA carriage at hospital admission and during hospitalization. During a two-month study, nasal swabs from 153 high-risk patients were tested for MRSA carriage using polymerase chain reaction (PCR) of which 31 (20.3%) were positive compared to 12 of 293 (4.1%, p < 0.001) low-risk patients. The mean interval from admission to availability of PCR test results was 19.2 hours. Computer alerts for patients at high-risk of MRSA carriage were found to be reliable, timely and offer the potential to replace testing all patients. Previous MRSA colonization was the best predictor but other risk factors were needed to increase the sensitivity of the algorithm.