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1821. Understanding the Components and Calculation of the SAAR, Illustrative Data
BACKGROUND: The standardized antimicrobial administration ratio (SAAR) compares each hospital’s observed to predicted days of antimicrobial therapy. However, confusion exists about how hospital-level, seasonal, and hospital-peer-based variations in antibiotic use might impact an institution’s SAAR....
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6254700/ http://dx.doi.org/10.1093/ofid/ofy210.1477 |
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author | Avedissian, Sean Rhodes, Nathaniel Liu, Jiajun Aljefri, Doaa Postelnick, Michael Sutton, Sarah Zembower, Teresa Martin, David Pais, Gwendolyn Cruce, Caroline Scheetz, Marc H |
author_facet | Avedissian, Sean Rhodes, Nathaniel Liu, Jiajun Aljefri, Doaa Postelnick, Michael Sutton, Sarah Zembower, Teresa Martin, David Pais, Gwendolyn Cruce, Caroline Scheetz, Marc H |
author_sort | Avedissian, Sean |
collection | PubMed |
description | BACKGROUND: The standardized antimicrobial administration ratio (SAAR) compares each hospital’s observed to predicted days of antimicrobial therapy. However, confusion exists about how hospital-level, seasonal, and hospital-peer-based variations in antibiotic use might impact an institution’s SAAR. We characterized the impact of each of these three types of variation on predicted SAARs utilizing local NHSN data. METHODS: Analysis of antibiotic consumption data from an academic medical center in Chicago, IL was conducted. SAAR and antimicrobial days per 1,000 days present (AD/1,000DP) were compiled in monthly increments from 2014 to 2016.Antimicrobial consumption was aggregated and classified into agent categories according to NHSN criteria. Month-to-month changes in both the SAAR and AD/1,000DP were evaluated. Azithromycin AD/1,000DP from 2012 through 2017 were explored for seasonal variation as defined as >20% increase in AD/1,000DP from each quarter to the overall mean AD/1,000DP for all months. A simulation was performed to explore the potential effect of seasonality on the SAAR. Demographic covariates within the SAAR model were altered while holding constant observed antibiotic use; thus we were able to observe the potential impact of demographics. Finally, a simulation explored the effect of altered consumption at other hospitals on a local institution’s SAAR. RESULTS: Across all antibiotic agent categories for both ICU (n = 4) and general wards (n = 4), the average matched-month percent change in AD/1,000DP was highly predicted and correlated with the corresponding change in SAAR (Figure 1, Pearson’s r = 0.99). The monthly mean ± SD AD/1,000DP was 235.0 (range 47.2–661.5), and the mean ± SD SAAR was 1.09 ± 0.26 (range 0.79–1.09) across the NHSN antibiotic agent categories. Five quarters were found to have seasonal variation in AD/1000DP for azithromycin (Figure 2). Simulations demonstrated that changing antimicrobial usage at comparator hospitals does not impact the local SAAR, and seasonal variation may cause fluctuating SAARs. CONCLUSION: Month-to-month changes in the SAAR mirror monthly changes in an institution’s AD/1,000DP. Seasonal variation can impact the SAAR, and the effect changing peer hospital antibiotic consumption is not currently captured by the SAAR methodology. [Image: see text] [Image: see text] DISCLOSURES: J. Liu, Merck: Grant fund from Merck, Research grant. D. Martin, Syneos Health: Employee, Salary. GlaxoSmithKline: Independent Contractor, Salary. M. H. Scheetz, Merck & Co., Inc.: Grant Investigator, Grant recipient. Bayer: Consultant, Consulting fee. |
format | Online Article Text |
id | pubmed-6254700 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-62547002018-11-28 1821. Understanding the Components and Calculation of the SAAR, Illustrative Data Avedissian, Sean Rhodes, Nathaniel Liu, Jiajun Aljefri, Doaa Postelnick, Michael Sutton, Sarah Zembower, Teresa Martin, David Pais, Gwendolyn Cruce, Caroline Scheetz, Marc H Open Forum Infect Dis Abstracts BACKGROUND: The standardized antimicrobial administration ratio (SAAR) compares each hospital’s observed to predicted days of antimicrobial therapy. However, confusion exists about how hospital-level, seasonal, and hospital-peer-based variations in antibiotic use might impact an institution’s SAAR. We characterized the impact of each of these three types of variation on predicted SAARs utilizing local NHSN data. METHODS: Analysis of antibiotic consumption data from an academic medical center in Chicago, IL was conducted. SAAR and antimicrobial days per 1,000 days present (AD/1,000DP) were compiled in monthly increments from 2014 to 2016.Antimicrobial consumption was aggregated and classified into agent categories according to NHSN criteria. Month-to-month changes in both the SAAR and AD/1,000DP were evaluated. Azithromycin AD/1,000DP from 2012 through 2017 were explored for seasonal variation as defined as >20% increase in AD/1,000DP from each quarter to the overall mean AD/1,000DP for all months. A simulation was performed to explore the potential effect of seasonality on the SAAR. Demographic covariates within the SAAR model were altered while holding constant observed antibiotic use; thus we were able to observe the potential impact of demographics. Finally, a simulation explored the effect of altered consumption at other hospitals on a local institution’s SAAR. RESULTS: Across all antibiotic agent categories for both ICU (n = 4) and general wards (n = 4), the average matched-month percent change in AD/1,000DP was highly predicted and correlated with the corresponding change in SAAR (Figure 1, Pearson’s r = 0.99). The monthly mean ± SD AD/1,000DP was 235.0 (range 47.2–661.5), and the mean ± SD SAAR was 1.09 ± 0.26 (range 0.79–1.09) across the NHSN antibiotic agent categories. Five quarters were found to have seasonal variation in AD/1000DP for azithromycin (Figure 2). Simulations demonstrated that changing antimicrobial usage at comparator hospitals does not impact the local SAAR, and seasonal variation may cause fluctuating SAARs. CONCLUSION: Month-to-month changes in the SAAR mirror monthly changes in an institution’s AD/1,000DP. Seasonal variation can impact the SAAR, and the effect changing peer hospital antibiotic consumption is not currently captured by the SAAR methodology. [Image: see text] [Image: see text] DISCLOSURES: J. Liu, Merck: Grant fund from Merck, Research grant. D. Martin, Syneos Health: Employee, Salary. GlaxoSmithKline: Independent Contractor, Salary. M. H. Scheetz, Merck & Co., Inc.: Grant Investigator, Grant recipient. Bayer: Consultant, Consulting fee. Oxford University Press 2018-11-26 /pmc/articles/PMC6254700/ http://dx.doi.org/10.1093/ofid/ofy210.1477 Text en © The Author(s) 2018. 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 Avedissian, Sean Rhodes, Nathaniel Liu, Jiajun Aljefri, Doaa Postelnick, Michael Sutton, Sarah Zembower, Teresa Martin, David Pais, Gwendolyn Cruce, Caroline Scheetz, Marc H 1821. Understanding the Components and Calculation of the SAAR, Illustrative Data |
title | 1821. Understanding the Components and Calculation of the SAAR, Illustrative Data |
title_full | 1821. Understanding the Components and Calculation of the SAAR, Illustrative Data |
title_fullStr | 1821. Understanding the Components and Calculation of the SAAR, Illustrative Data |
title_full_unstemmed | 1821. Understanding the Components and Calculation of the SAAR, Illustrative Data |
title_short | 1821. Understanding the Components and Calculation of the SAAR, Illustrative Data |
title_sort | 1821. understanding the components and calculation of the saar, illustrative data |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6254700/ http://dx.doi.org/10.1093/ofid/ofy210.1477 |
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