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Electronic Detection of MRSA Infections in a National VA Population Augments Current Manual Process
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 ra...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5630941/ http://dx.doi.org/10.1093/ofid/ofx163.1706 |
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author | Strymish, Judith Jones, Makoto Evans, Martin Branch-Elliman, Westyn Robillard, Ernest Chan, Jeffrey Rosen, Amy Gupta, Kalpana |
author_facet | Strymish, Judith Jones, Makoto Evans, Martin Branch-Elliman, Westyn Robillard, Ernest Chan, Jeffrey Rosen, Amy Gupta, Kalpana |
author_sort | Strymish, Judith |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5630941 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-56309412017-11-07 Electronic Detection of MRSA Infections in a National VA Population Augments Current Manual Process Strymish, Judith Jones, Makoto Evans, Martin Branch-Elliman, Westyn Robillard, Ernest Chan, Jeffrey Rosen, Amy Gupta, Kalpana Open Forum Infect Dis Abstracts 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. Oxford University Press 2017-10-04 /pmc/articles/PMC5630941/ http://dx.doi.org/10.1093/ofid/ofx163.1706 Text en © The Author 2017. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Abstracts Strymish, Judith Jones, Makoto Evans, Martin Branch-Elliman, Westyn Robillard, Ernest Chan, Jeffrey Rosen, Amy Gupta, Kalpana Electronic Detection of MRSA Infections in a National VA Population Augments Current Manual Process |
title | Electronic Detection of MRSA Infections in a National VA Population Augments Current Manual Process |
title_full | Electronic Detection of MRSA Infections in a National VA Population Augments Current Manual Process |
title_fullStr | Electronic Detection of MRSA Infections in a National VA Population Augments Current Manual Process |
title_full_unstemmed | Electronic Detection of MRSA Infections in a National VA Population Augments Current Manual Process |
title_short | Electronic Detection of MRSA Infections in a National VA Population Augments Current Manual Process |
title_sort | electronic detection of mrsa infections in a national va population augments current manual process |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5630941/ http://dx.doi.org/10.1093/ofid/ofx163.1706 |
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