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Use of a Precision Antibiotic Therapy (PAT) Prediction Model to Identify Multidrug-Resistant (MDR) Enterobacteriaceae
BACKGROUND: Emergence of multidrug-resistant (MDR) Enterobacteriaceae complicates the selection of empiric antibiotic therapy. Software called Precision Antibiotic Therapy (PAT) (Teqqa, LLC; Jackson, WY) operationalizes a predictive model using patient factors to make real-time, personalized predict...
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/PMC5631741/ http://dx.doi.org/10.1093/ofid/ofx163.775 |
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author | Varga, Alexandra Cressman, Leigh Lautenbach, Ebbing Cluzet, Valerie Tolomeo, Pam Bilker, Warren Hamilton, Keith |
author_facet | Varga, Alexandra Cressman, Leigh Lautenbach, Ebbing Cluzet, Valerie Tolomeo, Pam Bilker, Warren Hamilton, Keith |
author_sort | Varga, Alexandra |
collection | PubMed |
description | BACKGROUND: Emergence of multidrug-resistant (MDR) Enterobacteriaceae complicates the selection of empiric antibiotic therapy. Software called Precision Antibiotic Therapy (PAT) (Teqqa, LLC; Jackson, WY) operationalizes a predictive model using patient factors to make real-time, personalized predictions of antibiotic susceptibility for each antibiotic, allowing prescribers to choose empiric therapy for patients at risk for resistant infections. The purpose of this study was to determine the performance of PAT software in identifying MDR Enterobacteriaceaebloodstream infections (BSI) as well as to determine optimal thresholds of predicted antibiotic susceptibility to choose a broader-spectrum antibiotic. METHODS: We conducted a retrospective cohort study including 475 unique patients with BSIs caused by Enterobacteriaceaefrom January 1, 2016 through December 31, 2016. First-line antibiotic therapy for BSI was defined as cefepime, piperacillin-tazobactam, levofloxacin, or aztreonam. Susceptibilities predicted by PAT were compared with known susceptibilities determined by routine laboratory testing. PAT thresholds for broadening antibiotics were assessed when predicted susceptibilities were 80%, 85%, 90%, and 95% using receiver-operating characteristic (ROC) curves. Performance characteristics were calculated for each threshold. Brier score calculations were then used to compare the accuracy of PAT predictions using the optimized predicted susceptibility threshold, to that of aggregate institutional susceptibility data. RESULTS: ROC curve analysis demonstrated an area under the curve of 0.82 for the 95% threshold. The sensitivity for the PAT prediction utilizing the 95% threshold was 91.7% with a specificity of 74.3%. The Brier score for the 2016 antibiogram to determine antibiotic therapy was 0.085, whereas the Brier score using PAT software was 0.071, representing a 16% improvement in accuracy. CONCLUSION: PAT software demonstrated excellent capability to discriminate between Enterobacteriaceae BSIs resistant and susceptible to first-line therapy. A predicted susceptibility threshold of 95% should be used to indicate a need for escalation of empiric antibiotic therapy using PAT. DISCLOSURES: All authors: No reported disclosures. |
format | Online Article Text |
id | pubmed-5631741 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-56317412017-11-07 Use of a Precision Antibiotic Therapy (PAT) Prediction Model to Identify Multidrug-Resistant (MDR) Enterobacteriaceae Varga, Alexandra Cressman, Leigh Lautenbach, Ebbing Cluzet, Valerie Tolomeo, Pam Bilker, Warren Hamilton, Keith Open Forum Infect Dis Abstracts BACKGROUND: Emergence of multidrug-resistant (MDR) Enterobacteriaceae complicates the selection of empiric antibiotic therapy. Software called Precision Antibiotic Therapy (PAT) (Teqqa, LLC; Jackson, WY) operationalizes a predictive model using patient factors to make real-time, personalized predictions of antibiotic susceptibility for each antibiotic, allowing prescribers to choose empiric therapy for patients at risk for resistant infections. The purpose of this study was to determine the performance of PAT software in identifying MDR Enterobacteriaceaebloodstream infections (BSI) as well as to determine optimal thresholds of predicted antibiotic susceptibility to choose a broader-spectrum antibiotic. METHODS: We conducted a retrospective cohort study including 475 unique patients with BSIs caused by Enterobacteriaceaefrom January 1, 2016 through December 31, 2016. First-line antibiotic therapy for BSI was defined as cefepime, piperacillin-tazobactam, levofloxacin, or aztreonam. Susceptibilities predicted by PAT were compared with known susceptibilities determined by routine laboratory testing. PAT thresholds for broadening antibiotics were assessed when predicted susceptibilities were 80%, 85%, 90%, and 95% using receiver-operating characteristic (ROC) curves. Performance characteristics were calculated for each threshold. Brier score calculations were then used to compare the accuracy of PAT predictions using the optimized predicted susceptibility threshold, to that of aggregate institutional susceptibility data. RESULTS: ROC curve analysis demonstrated an area under the curve of 0.82 for the 95% threshold. The sensitivity for the PAT prediction utilizing the 95% threshold was 91.7% with a specificity of 74.3%. The Brier score for the 2016 antibiogram to determine antibiotic therapy was 0.085, whereas the Brier score using PAT software was 0.071, representing a 16% improvement in accuracy. CONCLUSION: PAT software demonstrated excellent capability to discriminate between Enterobacteriaceae BSIs resistant and susceptible to first-line therapy. A predicted susceptibility threshold of 95% should be used to indicate a need for escalation of empiric antibiotic therapy using PAT. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2017-10-04 /pmc/articles/PMC5631741/ http://dx.doi.org/10.1093/ofid/ofx163.775 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 Varga, Alexandra Cressman, Leigh Lautenbach, Ebbing Cluzet, Valerie Tolomeo, Pam Bilker, Warren Hamilton, Keith Use of a Precision Antibiotic Therapy (PAT) Prediction Model to Identify Multidrug-Resistant (MDR) Enterobacteriaceae |
title | Use of a Precision Antibiotic Therapy (PAT) Prediction Model to Identify Multidrug-Resistant (MDR) Enterobacteriaceae |
title_full | Use of a Precision Antibiotic Therapy (PAT) Prediction Model to Identify Multidrug-Resistant (MDR) Enterobacteriaceae |
title_fullStr | Use of a Precision Antibiotic Therapy (PAT) Prediction Model to Identify Multidrug-Resistant (MDR) Enterobacteriaceae |
title_full_unstemmed | Use of a Precision Antibiotic Therapy (PAT) Prediction Model to Identify Multidrug-Resistant (MDR) Enterobacteriaceae |
title_short | Use of a Precision Antibiotic Therapy (PAT) Prediction Model to Identify Multidrug-Resistant (MDR) Enterobacteriaceae |
title_sort | use of a precision antibiotic therapy (pat) prediction model to identify multidrug-resistant (mdr) enterobacteriaceae |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5631741/ http://dx.doi.org/10.1093/ofid/ofx163.775 |
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