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1013. Electronic Assessment of Empiric Antibiotic Prescribing Using Diagnosis Codes
BACKGROUND: Antimicrobial stewardship programs (ASPs) must understand empiric choices for specific disease syndromes to assess adherence to local empiric treatment guidelines. Electronically-derived metrics to track empiric therapy choices would allow ASPs to target areas for intervention without si...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6810924/ http://dx.doi.org/10.1093/ofid/ofz360.877 |
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author | Dodds Ashley, Elizabeth Nelson, Alicia Johnson, Melissa D Jones, Travis M Davis, Angelina Dyer, April Moehring, Rebekah W |
author_facet | Dodds Ashley, Elizabeth Nelson, Alicia Johnson, Melissa D Jones, Travis M Davis, Angelina Dyer, April Moehring, Rebekah W |
author_sort | Dodds Ashley, Elizabeth |
collection | PubMed |
description | BACKGROUND: Antimicrobial stewardship programs (ASPs) must understand empiric choices for specific disease syndromes to assess adherence to local empiric treatment guidelines. Electronically-derived metrics to track empiric therapy choices would allow ASPs to target areas for intervention without significant data collection burden. METHODS: Admissions from 10 community hospitals between 7/2016 and December 2018 were reviewed to identify those with common infectious syndromes: pneumonia (PNA), urinary tract infection (UTI) and skin and soft-tissue infection (SSTI). Admissions with a syndrome of interest were identified using AHRQ clinical classifications software codes based on ICD-10 codes for infection at the time of discharge. Admissions were categorized as having the syndrome of interest with or without sepsis. Antibiotics received during the first 48 hours of inpatient admission were obtained from electronic medication administration records. The proportion of syndrome admissions receiving specific antibiotic agents was determined to evaluate initial treatment choices as compared with local empiric guidelines. Antibiotic categories were not mutually exclusive, admissions receiving combination therapy were included in the count for each individual agent as well as the combination group. The denominator was the count of admissions with the syndrome of interest. Distributions were tracked over time to observe the effects of ASP intervention. RESULTS: The analysis included 49,303 admissions. The most common diagnosis was UTI (30%) followed by PNA (23%). Empiric antibiotic use varied by syndrome (Figure 1). In general, patients with a targeted infectious diagnosis and sepsis received more broad-spectrum agents than those without sepsis. SSTI was an exception, but few patients admitted with SSTI did not also have presumed sepsis. Longitudinal analysis demonstrated shifts from less preferred agents to guideline-concordant choices. For example, for admissions with a diagnosis of PNA, we observed a steady year on year increase in ceftriaxone (preferred) while levofloxacin (avoided in local guidelines) declined. (Figure 2) CONCLUSION: Syndrome-specific diagnosis codes were helpful in assessing empiric antibiotic selection and may assist ASPs in improving empiric guideline adherence. [Image: see text] [Image: see text] DISCLOSURES: All authors: No reported disclosures. |
format | Online Article Text |
id | pubmed-6810924 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-68109242019-10-28 1013. Electronic Assessment of Empiric Antibiotic Prescribing Using Diagnosis Codes Dodds Ashley, Elizabeth Nelson, Alicia Johnson, Melissa D Jones, Travis M Davis, Angelina Dyer, April Moehring, Rebekah W Open Forum Infect Dis Abstracts BACKGROUND: Antimicrobial stewardship programs (ASPs) must understand empiric choices for specific disease syndromes to assess adherence to local empiric treatment guidelines. Electronically-derived metrics to track empiric therapy choices would allow ASPs to target areas for intervention without significant data collection burden. METHODS: Admissions from 10 community hospitals between 7/2016 and December 2018 were reviewed to identify those with common infectious syndromes: pneumonia (PNA), urinary tract infection (UTI) and skin and soft-tissue infection (SSTI). Admissions with a syndrome of interest were identified using AHRQ clinical classifications software codes based on ICD-10 codes for infection at the time of discharge. Admissions were categorized as having the syndrome of interest with or without sepsis. Antibiotics received during the first 48 hours of inpatient admission were obtained from electronic medication administration records. The proportion of syndrome admissions receiving specific antibiotic agents was determined to evaluate initial treatment choices as compared with local empiric guidelines. Antibiotic categories were not mutually exclusive, admissions receiving combination therapy were included in the count for each individual agent as well as the combination group. The denominator was the count of admissions with the syndrome of interest. Distributions were tracked over time to observe the effects of ASP intervention. RESULTS: The analysis included 49,303 admissions. The most common diagnosis was UTI (30%) followed by PNA (23%). Empiric antibiotic use varied by syndrome (Figure 1). In general, patients with a targeted infectious diagnosis and sepsis received more broad-spectrum agents than those without sepsis. SSTI was an exception, but few patients admitted with SSTI did not also have presumed sepsis. Longitudinal analysis demonstrated shifts from less preferred agents to guideline-concordant choices. For example, for admissions with a diagnosis of PNA, we observed a steady year on year increase in ceftriaxone (preferred) while levofloxacin (avoided in local guidelines) declined. (Figure 2) CONCLUSION: Syndrome-specific diagnosis codes were helpful in assessing empiric antibiotic selection and may assist ASPs in improving empiric guideline adherence. [Image: see text] [Image: see text] DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2019-10-23 /pmc/articles/PMC6810924/ http://dx.doi.org/10.1093/ofid/ofz360.877 Text en © The Author(s) 2019. 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 Dodds Ashley, Elizabeth Nelson, Alicia Johnson, Melissa D Jones, Travis M Davis, Angelina Dyer, April Moehring, Rebekah W 1013. Electronic Assessment of Empiric Antibiotic Prescribing Using Diagnosis Codes |
title | 1013. Electronic Assessment of Empiric Antibiotic Prescribing Using Diagnosis Codes |
title_full | 1013. Electronic Assessment of Empiric Antibiotic Prescribing Using Diagnosis Codes |
title_fullStr | 1013. Electronic Assessment of Empiric Antibiotic Prescribing Using Diagnosis Codes |
title_full_unstemmed | 1013. Electronic Assessment of Empiric Antibiotic Prescribing Using Diagnosis Codes |
title_short | 1013. Electronic Assessment of Empiric Antibiotic Prescribing Using Diagnosis Codes |
title_sort | 1013. electronic assessment of empiric antibiotic prescribing using diagnosis codes |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6810924/ http://dx.doi.org/10.1093/ofid/ofz360.877 |
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