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A “resistance calculator”: Simple stewardship intervention for refining empiric practices of antimicrobials in acute-care hospitals

OBJECTIVE: In the era of widespread resistance, there are 2 time points at which most empiric prescription errors occur among hospitalized adults: (1) upon admission (UA) when treating patients at risk of multidrug-resistant organisms (MDROs) and (2) during hospitalization, when treating patients at...

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Autores principales: Zilberman-Itskovich, Shani, Strul, Nathan, Chedid, Khalil, Martin, Emily T., Shorbaje, Akram, Vitkon-Barkay, Itzhak, Marcus, Gil, Michaeli, Leah, Broide, Mor, Yekutiel, Matar, Zohar, Yarden, Razin, Hadas, Low, Amitai, Strulovici, Ariela, Israeli, Boaz, Geva, Gal, Katz, David E., Ben-Chetrit, Eli, Dodin, Mutaz, Dhar, Sorabh, Parsons, Leo Milton, Ramos-Mercado, Abdiel, Kaye, Keith S., Marchaim, Dror
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8459314/
https://www.ncbi.nlm.nih.gov/pubmed/33736724
http://dx.doi.org/10.1017/ice.2020.1372
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author Zilberman-Itskovich, Shani
Strul, Nathan
Chedid, Khalil
Martin, Emily T.
Shorbaje, Akram
Vitkon-Barkay, Itzhak
Marcus, Gil
Michaeli, Leah
Broide, Mor
Yekutiel, Matar
Zohar, Yarden
Razin, Hadas
Low, Amitai
Strulovici, Ariela
Israeli, Boaz
Geva, Gal
Katz, David E.
Ben-Chetrit, Eli
Dodin, Mutaz
Dhar, Sorabh
Parsons, Leo Milton
Ramos-Mercado, Abdiel
Kaye, Keith S.
Marchaim, Dror
author_facet Zilberman-Itskovich, Shani
Strul, Nathan
Chedid, Khalil
Martin, Emily T.
Shorbaje, Akram
Vitkon-Barkay, Itzhak
Marcus, Gil
Michaeli, Leah
Broide, Mor
Yekutiel, Matar
Zohar, Yarden
Razin, Hadas
Low, Amitai
Strulovici, Ariela
Israeli, Boaz
Geva, Gal
Katz, David E.
Ben-Chetrit, Eli
Dodin, Mutaz
Dhar, Sorabh
Parsons, Leo Milton
Ramos-Mercado, Abdiel
Kaye, Keith S.
Marchaim, Dror
author_sort Zilberman-Itskovich, Shani
collection PubMed
description OBJECTIVE: In the era of widespread resistance, there are 2 time points at which most empiric prescription errors occur among hospitalized adults: (1) upon admission (UA) when treating patients at risk of multidrug-resistant organisms (MDROs) and (2) during hospitalization, when treating patients at risk of extensively drug-resistant organisms (XDROs). These errors adversely influence patient outcomes and the hospital’s ecology. DESIGN AND SETTING: Retrospective cohort study, Shamir Medical Center, Israel, 2016. PATIENTS: Adult patients (aged >18 years) hospitalized with sepsis. METHODS: Logistic regressions were used to develop predictive models for (1) MDRO UA and (2) nosocomial XDRO. Their performances on the derivation data sets, and on 7 other validation data sets, were assessed using the area under the receiver operating characteristic curve (ROC AUC). RESULTS: In total, 4,114 patients were included: 2,472 patients with sepsis UA and 1,642 with nosocomial sepsis. The MDRO UA score included 10 parameters, and with a cutoff of ≥22 points, it had an ROC AUC of 0.85. The nosocomial XDRO score included 7 parameters, and with a cutoff of ≥36 points, it had an ROC AUC of 0.87. The range of ROC AUCs for the validation data sets was 0.7–0.88 for the MDRO UA score and was 0.66–0.75 for nosocomial XDRO score. We created a free web calculator (https://assafharofe.azurewebsites.net). CONCLUSIONS: A simple electronic calculator could aid with empiric prescription during an encounter with a septic patient. Future implementation studies are needed to evaluate its utility in improving patient outcomes and in reducing overall resistances.
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spelling pubmed-84593142021-10-01 A “resistance calculator”: Simple stewardship intervention for refining empiric practices of antimicrobials in acute-care hospitals Zilberman-Itskovich, Shani Strul, Nathan Chedid, Khalil Martin, Emily T. Shorbaje, Akram Vitkon-Barkay, Itzhak Marcus, Gil Michaeli, Leah Broide, Mor Yekutiel, Matar Zohar, Yarden Razin, Hadas Low, Amitai Strulovici, Ariela Israeli, Boaz Geva, Gal Katz, David E. Ben-Chetrit, Eli Dodin, Mutaz Dhar, Sorabh Parsons, Leo Milton Ramos-Mercado, Abdiel Kaye, Keith S. Marchaim, Dror Infect Control Hosp Epidemiol Original Article OBJECTIVE: In the era of widespread resistance, there are 2 time points at which most empiric prescription errors occur among hospitalized adults: (1) upon admission (UA) when treating patients at risk of multidrug-resistant organisms (MDROs) and (2) during hospitalization, when treating patients at risk of extensively drug-resistant organisms (XDROs). These errors adversely influence patient outcomes and the hospital’s ecology. DESIGN AND SETTING: Retrospective cohort study, Shamir Medical Center, Israel, 2016. PATIENTS: Adult patients (aged >18 years) hospitalized with sepsis. METHODS: Logistic regressions were used to develop predictive models for (1) MDRO UA and (2) nosocomial XDRO. Their performances on the derivation data sets, and on 7 other validation data sets, were assessed using the area under the receiver operating characteristic curve (ROC AUC). RESULTS: In total, 4,114 patients were included: 2,472 patients with sepsis UA and 1,642 with nosocomial sepsis. The MDRO UA score included 10 parameters, and with a cutoff of ≥22 points, it had an ROC AUC of 0.85. The nosocomial XDRO score included 7 parameters, and with a cutoff of ≥36 points, it had an ROC AUC of 0.87. The range of ROC AUCs for the validation data sets was 0.7–0.88 for the MDRO UA score and was 0.66–0.75 for nosocomial XDRO score. We created a free web calculator (https://assafharofe.azurewebsites.net). CONCLUSIONS: A simple electronic calculator could aid with empiric prescription during an encounter with a septic patient. Future implementation studies are needed to evaluate its utility in improving patient outcomes and in reducing overall resistances. Cambridge University Press 2021-09 2021-03-19 /pmc/articles/PMC8459314/ /pubmed/33736724 http://dx.doi.org/10.1017/ice.2020.1372 Text en © The Society for Healthcare Epidemiology of America 2021 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Zilberman-Itskovich, Shani
Strul, Nathan
Chedid, Khalil
Martin, Emily T.
Shorbaje, Akram
Vitkon-Barkay, Itzhak
Marcus, Gil
Michaeli, Leah
Broide, Mor
Yekutiel, Matar
Zohar, Yarden
Razin, Hadas
Low, Amitai
Strulovici, Ariela
Israeli, Boaz
Geva, Gal
Katz, David E.
Ben-Chetrit, Eli
Dodin, Mutaz
Dhar, Sorabh
Parsons, Leo Milton
Ramos-Mercado, Abdiel
Kaye, Keith S.
Marchaim, Dror
A “resistance calculator”: Simple stewardship intervention for refining empiric practices of antimicrobials in acute-care hospitals
title A “resistance calculator”: Simple stewardship intervention for refining empiric practices of antimicrobials in acute-care hospitals
title_full A “resistance calculator”: Simple stewardship intervention for refining empiric practices of antimicrobials in acute-care hospitals
title_fullStr A “resistance calculator”: Simple stewardship intervention for refining empiric practices of antimicrobials in acute-care hospitals
title_full_unstemmed A “resistance calculator”: Simple stewardship intervention for refining empiric practices of antimicrobials in acute-care hospitals
title_short A “resistance calculator”: Simple stewardship intervention for refining empiric practices of antimicrobials in acute-care hospitals
title_sort “resistance calculator”: simple stewardship intervention for refining empiric practices of antimicrobials in acute-care hospitals
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8459314/
https://www.ncbi.nlm.nih.gov/pubmed/33736724
http://dx.doi.org/10.1017/ice.2020.1372
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