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872. PROPHETIC: Predicting Pneumonia in Hospitalized Patients in the ICU—A Model and Scoring System

BACKGROUND: Prospectively identifying patients at highest risk for hospital-acquired and ventilator-associated bacterial pneumonia (HABP/VABP) by implementing a risk assessment scoring tool may help focus prevention efforts, optimize the screening process to improve clinical trial feasibility, and e...

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Autores principales: Bergin, Stephen P, Coles, Adrian, Calvert, Sara B, Farley, John, Santiago, Jonas, Zervos, Marcus J, Bardossy, Ana Cecilia, Kollef, Marin, Durkin, Michael J, Sims, Matthew, Greenshields, Claire, Kabchi, Badih A, Donnelly, Helen K, III, John Powers, Tenaerts, Pamela, Gu, Peidi, Fowler, Vance G, Holland, Thomas L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6252450/
http://dx.doi.org/10.1093/ofid/ofy209.056
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author Bergin, Stephen P
Coles, Adrian
Calvert, Sara B
Farley, John
Santiago, Jonas
Zervos, Marcus J
Bardossy, Ana Cecilia
Kollef, Marin
Durkin, Michael J
Sims, Matthew
Greenshields, Claire
Kabchi, Badih A
Donnelly, Helen K
III, John Powers
Tenaerts, Pamela
Gu, Peidi
Fowler, Vance G
Holland, Thomas L
author_facet Bergin, Stephen P
Coles, Adrian
Calvert, Sara B
Farley, John
Santiago, Jonas
Zervos, Marcus J
Bardossy, Ana Cecilia
Kollef, Marin
Durkin, Michael J
Sims, Matthew
Greenshields, Claire
Kabchi, Badih A
Donnelly, Helen K
III, John Powers
Tenaerts, Pamela
Gu, Peidi
Fowler, Vance G
Holland, Thomas L
author_sort Bergin, Stephen P
collection PubMed
description BACKGROUND: Prospectively identifying patients at highest risk for hospital-acquired and ventilator-associated bacterial pneumonia (HABP/VABP) by implementing a risk assessment scoring tool may help focus prevention efforts, optimize the screening process to improve clinical trial feasibility, and enhance development of new antibacterial agents. METHODS: Within the intensive care units (ICU) of 28 US hospitals, between February 6, 2016 and October 7, 2016, patients hospitalized >48 hours and receiving high levels of respiratory support were prospectively followed for meeting the definition of HABP/VABP recommended in US FDA draft guidance. Patient demographics, medical comorbidities, and treatment exposures were recorded. The association between candidate risk factors and odds of developing HABP/VABP was evaluated with a multivariable logistic regression model. Risk factors were selected using backward selection with α = 0.1 for model inclusion. A web-based scoring system was developed to estimate the risk of HABP/VABP from the risk factors identified. RESULTS: A total of 5,101 patients were enrolled, of whom 1,005 (20%) developed HABP/VABp. 4,613 patients were included in the model, excluding 488 (10%) with HABP/VABP at or before enrollment. There are 15 variables included in the model. APACHE II admission score >20 (P < 0.001, OR 2.14, 95% CI 2.00–2.29), admission diagnosis of trauma (P < 0.001, OR 3.31, 95% CI 1.90–5.74), frequent oral or lower respiratory tract suctioning (P < 0.001, OR 2.33, 95% CI 1.81–2.99), and receipt of enteral nutrition (P < 0.001, OR 2.31, 95% CI 1.69–3.16) were the key drivers of increased pneumonia risk. The model demonstrated excellent discrimination (bias-corrected C-statistic 0.861, 95% CI 0.843–0.880). The web-based scoring system can be accessed via this link: https://ctti-habpvabp.shinyapps.io/web_based_tool/. CONCLUSION: Using a web-based scoring system, ICU patients at highest risk for developing HABP/VABP can be accurately identified. Prospective implementation of this tool may assist in focusing additional prevention efforts on the highest risk patients and enhance new drug development for HABP/VABP. DISCLOSURES: S. P. Bergin, CTTI: Investigator and Scientific Advisor, Research support and Travel to study related meetings. A. Coles, CTTI: Investigator and Scientific Advisor, Salary. S. B. Calvert, CTTI: Employee, Salary. M. J. Zervos, CTTI: Investigator, Research support. A. C. Bardossy, CTTI: Investigator, Research support. M. Kollef, CTTI: Investigator, Research support. M. J. Durkin, CTTI: Investigator, Research support. M. Sims, CTTI: Investigator, Research support. C. Greenshields, CTTI: Investigator, Research support. B. A. Kabchi, CTTI: Investigator, Research support. H. K. Donnelly, CTTI: Collaborator and Scientific Advisor, Research support and Salary. P. Tenaerts, CTTI: Employee, Salary. P. Gu, CTTI: Collaborator, Research support and Salary. V. G. Fowler Jr., CTTI: Investigator and Scientific Advisor, Research support and Salary. Merck: Consultant, Grant Investigator and Scientific Advisor, Consulting fee, Grant recipient and Research support. Cerexa/Actavis/Allegan: Grant Investigator, Grant recipient. Pfizer: Consultant and Grant Investigator, Consulting fee and Grant recipient. Advanced Liquid Logics: Grant Investigator, Grant recipient. NIH: Investigator, Grant recipient, Research support and Salary. MedImmune: Consultant and Grant Investigator, Consulting fee and Grant recipient. Basilea: Consultant and Grant Investigator, Consulting fee and Grant recipient. Karius: Grant Investigator, Grant recipient. Contrafect: Consultant and Grant Investigator, Consulting fee and Grant recipient. Regeneron: Grant Investigator, Grant recipient. Genentech: Consultant and Grant Investigator, Consulting fee and Grant recipient. Achaogen: Consultant, Consulting fee. Astellas: Consultant, Consulting fee. Arsanis: Consultant, Consulting fee. Affinergy: Consultant, Consulting fee. Bayer: Consultant, Consulting fee. Cerexa: Consultant, Consulting fee. Cubist: Consultant, Consulting fee. Debiopharm: Consultant, Consulting fee. Durata: Consultant, Consulting fee. Grifols: Consultant, Consulting fee. Medicines Co.: Consultant, Consulting fee. Novartis: Consultant, Consulting fee. Novadigm: Consultant, Consulting fee. Theravance: Consultant, Consulting fee and Speaker honorarium. xBiotech: Consultant, Consulting fee. Green Cross: Consultant, Speaker honorarium. T. L. Holland, CTTI: Investigator and Scientific Advisor, Research support and Salary.
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spelling pubmed-62524502018-11-28 872. PROPHETIC: Predicting Pneumonia in Hospitalized Patients in the ICU—A Model and Scoring System Bergin, Stephen P Coles, Adrian Calvert, Sara B Farley, John Santiago, Jonas Zervos, Marcus J Bardossy, Ana Cecilia Kollef, Marin Durkin, Michael J Sims, Matthew Greenshields, Claire Kabchi, Badih A Donnelly, Helen K III, John Powers Tenaerts, Pamela Gu, Peidi Fowler, Vance G Holland, Thomas L Open Forum Infect Dis Abstracts BACKGROUND: Prospectively identifying patients at highest risk for hospital-acquired and ventilator-associated bacterial pneumonia (HABP/VABP) by implementing a risk assessment scoring tool may help focus prevention efforts, optimize the screening process to improve clinical trial feasibility, and enhance development of new antibacterial agents. METHODS: Within the intensive care units (ICU) of 28 US hospitals, between February 6, 2016 and October 7, 2016, patients hospitalized >48 hours and receiving high levels of respiratory support were prospectively followed for meeting the definition of HABP/VABP recommended in US FDA draft guidance. Patient demographics, medical comorbidities, and treatment exposures were recorded. The association between candidate risk factors and odds of developing HABP/VABP was evaluated with a multivariable logistic regression model. Risk factors were selected using backward selection with α = 0.1 for model inclusion. A web-based scoring system was developed to estimate the risk of HABP/VABP from the risk factors identified. RESULTS: A total of 5,101 patients were enrolled, of whom 1,005 (20%) developed HABP/VABp. 4,613 patients were included in the model, excluding 488 (10%) with HABP/VABP at or before enrollment. There are 15 variables included in the model. APACHE II admission score >20 (P < 0.001, OR 2.14, 95% CI 2.00–2.29), admission diagnosis of trauma (P < 0.001, OR 3.31, 95% CI 1.90–5.74), frequent oral or lower respiratory tract suctioning (P < 0.001, OR 2.33, 95% CI 1.81–2.99), and receipt of enteral nutrition (P < 0.001, OR 2.31, 95% CI 1.69–3.16) were the key drivers of increased pneumonia risk. The model demonstrated excellent discrimination (bias-corrected C-statistic 0.861, 95% CI 0.843–0.880). The web-based scoring system can be accessed via this link: https://ctti-habpvabp.shinyapps.io/web_based_tool/. CONCLUSION: Using a web-based scoring system, ICU patients at highest risk for developing HABP/VABP can be accurately identified. Prospective implementation of this tool may assist in focusing additional prevention efforts on the highest risk patients and enhance new drug development for HABP/VABP. DISCLOSURES: S. P. Bergin, CTTI: Investigator and Scientific Advisor, Research support and Travel to study related meetings. A. Coles, CTTI: Investigator and Scientific Advisor, Salary. S. B. Calvert, CTTI: Employee, Salary. M. J. Zervos, CTTI: Investigator, Research support. A. C. Bardossy, CTTI: Investigator, Research support. M. Kollef, CTTI: Investigator, Research support. M. J. Durkin, CTTI: Investigator, Research support. M. Sims, CTTI: Investigator, Research support. C. Greenshields, CTTI: Investigator, Research support. B. A. Kabchi, CTTI: Investigator, Research support. H. K. Donnelly, CTTI: Collaborator and Scientific Advisor, Research support and Salary. P. Tenaerts, CTTI: Employee, Salary. P. Gu, CTTI: Collaborator, Research support and Salary. V. G. Fowler Jr., CTTI: Investigator and Scientific Advisor, Research support and Salary. Merck: Consultant, Grant Investigator and Scientific Advisor, Consulting fee, Grant recipient and Research support. Cerexa/Actavis/Allegan: Grant Investigator, Grant recipient. Pfizer: Consultant and Grant Investigator, Consulting fee and Grant recipient. Advanced Liquid Logics: Grant Investigator, Grant recipient. NIH: Investigator, Grant recipient, Research support and Salary. MedImmune: Consultant and Grant Investigator, Consulting fee and Grant recipient. Basilea: Consultant and Grant Investigator, Consulting fee and Grant recipient. Karius: Grant Investigator, Grant recipient. Contrafect: Consultant and Grant Investigator, Consulting fee and Grant recipient. Regeneron: Grant Investigator, Grant recipient. Genentech: Consultant and Grant Investigator, Consulting fee and Grant recipient. Achaogen: Consultant, Consulting fee. Astellas: Consultant, Consulting fee. Arsanis: Consultant, Consulting fee. Affinergy: Consultant, Consulting fee. Bayer: Consultant, Consulting fee. Cerexa: Consultant, Consulting fee. Cubist: Consultant, Consulting fee. Debiopharm: Consultant, Consulting fee. Durata: Consultant, Consulting fee. Grifols: Consultant, Consulting fee. Medicines Co.: Consultant, Consulting fee. Novartis: Consultant, Consulting fee. Novadigm: Consultant, Consulting fee. Theravance: Consultant, Consulting fee and Speaker honorarium. xBiotech: Consultant, Consulting fee. Green Cross: Consultant, Speaker honorarium. T. L. Holland, CTTI: Investigator and Scientific Advisor, Research support and Salary. Oxford University Press 2018-11-26 /pmc/articles/PMC6252450/ http://dx.doi.org/10.1093/ofid/ofy209.056 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
Bergin, Stephen P
Coles, Adrian
Calvert, Sara B
Farley, John
Santiago, Jonas
Zervos, Marcus J
Bardossy, Ana Cecilia
Kollef, Marin
Durkin, Michael J
Sims, Matthew
Greenshields, Claire
Kabchi, Badih A
Donnelly, Helen K
III, John Powers
Tenaerts, Pamela
Gu, Peidi
Fowler, Vance G
Holland, Thomas L
872. PROPHETIC: Predicting Pneumonia in Hospitalized Patients in the ICU—A Model and Scoring System
title 872. PROPHETIC: Predicting Pneumonia in Hospitalized Patients in the ICU—A Model and Scoring System
title_full 872. PROPHETIC: Predicting Pneumonia in Hospitalized Patients in the ICU—A Model and Scoring System
title_fullStr 872. PROPHETIC: Predicting Pneumonia in Hospitalized Patients in the ICU—A Model and Scoring System
title_full_unstemmed 872. PROPHETIC: Predicting Pneumonia in Hospitalized Patients in the ICU—A Model and Scoring System
title_short 872. PROPHETIC: Predicting Pneumonia in Hospitalized Patients in the ICU—A Model and Scoring System
title_sort 872. prophetic: predicting pneumonia in hospitalized patients in the icu—a model and scoring system
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6252450/
http://dx.doi.org/10.1093/ofid/ofy209.056
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