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1170. Applying Machine Learning Algorithms to Predict Multi-Drug-Resistant Bacterial Infections From Prior Drug Exposure

BACKGROUND: Multi-drug-resistant (MDR) infection in the acute care setting prolongs hospital stay and causes high mortality, especially in the pediatric population. Being able to predict MDR infection risk upon or during admission could help prevent and reduce morbidity and mortality in children req...

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Autores principales: Lendrum, Elizabeth, Haslam, David, Ambroggio, Lilliam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6252992/
http://dx.doi.org/10.1093/ofid/ofy210.1003
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author Lendrum, Elizabeth
Haslam, David
Ambroggio, Lilliam
author_facet Lendrum, Elizabeth
Haslam, David
Ambroggio, Lilliam
author_sort Lendrum, Elizabeth
collection PubMed
description BACKGROUND: Multi-drug-resistant (MDR) infection in the acute care setting prolongs hospital stay and causes high mortality, especially in the pediatric population. Being able to predict MDR infection risk upon or during admission could help prevent and reduce morbidity and mortality in children requiring acute care in the future. This study aimed to develop and validate a predictive model for MDR infection in the pediatric population using machine learning (ML) analysis. METHODS: The study population included hospitalized pediatric patients diagnosed with MDR infection between January 1, 2010 and March 8, 2018. All positive cultures during that period were coded as growing either an MDR or non-MDR organism. ML was performed with random forest (RF) analysis to determine whether hospital drug exposure in the 90 days prior to culture was able to accurately classify cultures as positive for an MDR or non-MDR organism. RESULTS: During the study period, 7,551 positive cultures were defined as MDR out of a total of 26,913 cultures (28% of all positive cultures). When all cultures were included in the analysis, RF was modestly successful at classifying MDR vs. non-MDR organisms. Significant improvements in classification accuracy were obtained by subdividing cultures based on growth of individual species. RF was able to classify MDR Enterococcus with accuracy = 0.87, positive predictive value of 0.81, and negative predictive value of 0.88. Surprisingly, exposure to many nonantibiotic drugs were important in predicting antibiotic resistance, indicating either that these drugs altered risk directly, or were correlated with MDR risk indirectly. CONCLUSION: Drugs without known antimicrobial activity were important predictors of MDR status. Nonantimicrobial drug exposure may be a marker for disease types or therapeutic interventions that place patients at higher risk of MDR infection. Monitoring antimicrobial and nonantimicrobial drug exposure may accurately identify patients at highest risk of MDR infection. DISCLOSURES: All authors: No reported disclosures.
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spelling pubmed-62529922018-11-28 1170. Applying Machine Learning Algorithms to Predict Multi-Drug-Resistant Bacterial Infections From Prior Drug Exposure Lendrum, Elizabeth Haslam, David Ambroggio, Lilliam Open Forum Infect Dis Abstracts BACKGROUND: Multi-drug-resistant (MDR) infection in the acute care setting prolongs hospital stay and causes high mortality, especially in the pediatric population. Being able to predict MDR infection risk upon or during admission could help prevent and reduce morbidity and mortality in children requiring acute care in the future. This study aimed to develop and validate a predictive model for MDR infection in the pediatric population using machine learning (ML) analysis. METHODS: The study population included hospitalized pediatric patients diagnosed with MDR infection between January 1, 2010 and March 8, 2018. All positive cultures during that period were coded as growing either an MDR or non-MDR organism. ML was performed with random forest (RF) analysis to determine whether hospital drug exposure in the 90 days prior to culture was able to accurately classify cultures as positive for an MDR or non-MDR organism. RESULTS: During the study period, 7,551 positive cultures were defined as MDR out of a total of 26,913 cultures (28% of all positive cultures). When all cultures were included in the analysis, RF was modestly successful at classifying MDR vs. non-MDR organisms. Significant improvements in classification accuracy were obtained by subdividing cultures based on growth of individual species. RF was able to classify MDR Enterococcus with accuracy = 0.87, positive predictive value of 0.81, and negative predictive value of 0.88. Surprisingly, exposure to many nonantibiotic drugs were important in predicting antibiotic resistance, indicating either that these drugs altered risk directly, or were correlated with MDR risk indirectly. CONCLUSION: Drugs without known antimicrobial activity were important predictors of MDR status. Nonantimicrobial drug exposure may be a marker for disease types or therapeutic interventions that place patients at higher risk of MDR infection. Monitoring antimicrobial and nonantimicrobial drug exposure may accurately identify patients at highest risk of MDR infection. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2018-11-26 /pmc/articles/PMC6252992/ http://dx.doi.org/10.1093/ofid/ofy210.1003 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
Lendrum, Elizabeth
Haslam, David
Ambroggio, Lilliam
1170. Applying Machine Learning Algorithms to Predict Multi-Drug-Resistant Bacterial Infections From Prior Drug Exposure
title 1170. Applying Machine Learning Algorithms to Predict Multi-Drug-Resistant Bacterial Infections From Prior Drug Exposure
title_full 1170. Applying Machine Learning Algorithms to Predict Multi-Drug-Resistant Bacterial Infections From Prior Drug Exposure
title_fullStr 1170. Applying Machine Learning Algorithms to Predict Multi-Drug-Resistant Bacterial Infections From Prior Drug Exposure
title_full_unstemmed 1170. Applying Machine Learning Algorithms to Predict Multi-Drug-Resistant Bacterial Infections From Prior Drug Exposure
title_short 1170. Applying Machine Learning Algorithms to Predict Multi-Drug-Resistant Bacterial Infections From Prior Drug Exposure
title_sort 1170. applying machine learning algorithms to predict multi-drug-resistant bacterial infections from prior drug exposure
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6252992/
http://dx.doi.org/10.1093/ofid/ofy210.1003
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