LAUSR

Abstract Background Automated measurement of hospital-acquired infections (HAIs) can improve the efficiency and reliability of surveillance. Within the VA, inpatient MRSA HAIs are manually reviewed and reported to the Inpatient Evaluation Center (IPEC). These MRSA HAI metrics are used as part of facility rankings to compare quality. However, IPEC uses CDC surveillance definitions which may vary in interpretation across facilities and not reflect all clinically relevant MRSA events. Thus, we sought to compare this manual process to a previously-developed electronic algorithm for detecting clinical MRSA infections to evaluate whether the algorithm could be used to expand MRSA surveillance activities. Methods Electronic data were extracted from the national VA healthcare system during the period from January 1, 2014–December 31, 2014. The electronic detection algorithm defined MRSA infections as a culture positive for MRSA from a sterile site or from a non-sterile site with receipt of an antimicrobial with MRSA activity ± 5 days from the date of culture collection. Cultures obtained ≥48 hours after admission were classified as HAI. IPEC data for five facilities was extracted and IPEC rates were compared with rates estimated by the electronic algorithm. Flagged infections at one facility were manually reviewed to evaluate any discordances. Results N = 14,260 MRSA clinical cultures were identified in 9,209 unique patients. Of these, 1,703 met definition for MRSA HAI infection. Electronic algorithm detected MRSA HAI rates varied widely across 137 facilities (Figure 1), ranked by rate per 1,000 patient-days. IPEC rates were universally lower than estimates derived using the MRSA electronic detection tool. Discordance in the estimates was attributable to infections present on admission, differences in capture of surgical site infections, and differences between clinical and surveillance definitions of infection. Conclusion Applying the MRSA algorithm provided additional information about the burden of MRSA infections across the VA. This algorithm could be used as a tool to complement IPEC reporting and further inform infection prevention activities. Disclosures All authors: No reported disclosures.