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1008. Cluster Analysis to Define Distinct Clinical Phenotypes Among Septic Patients With Bloodstream Infections

BACKGROUND: Prior attempts at identifying outcome determinants associated with bloodstream infection have employed a priori determined classification schemes based on readily identifiable microbiology, infection site, and patient characteristics. We hypothesized that even amongst this heterogeneous...

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Autores principales: Guillamet, M Cristina Vazquez, Bernauer, Michael, Micek, Scott, Kollef, Marin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6255486/
http://dx.doi.org/10.1093/ofid/ofy210.845
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author Guillamet, M Cristina Vazquez
Bernauer, Michael
Micek, Scott
Kollef, Marin
author_facet Guillamet, M Cristina Vazquez
Bernauer, Michael
Micek, Scott
Kollef, Marin
author_sort Guillamet, M Cristina Vazquez
collection PubMed
description BACKGROUND: Prior attempts at identifying outcome determinants associated with bloodstream infection have employed a priori determined classification schemes based on readily identifiable microbiology, infection site, and patient characteristics. We hypothesized that even amongst this heterogeneous population, clinically relevant groupings can be described that transcend old a priori classifications. METHODS: Cluster analysis was applied to variables from three domains: patient characteristics, acuity of illness/clinical presentation, and infection characteristics. RESULTS: Among 3,715 patients with bloodstream infections from Barnes-Jewish Hospital (2008 to 2015), the most stable cluster arrangement occurred with the formation of four clusters. This clustering arrangement resulted in an approximately uniform distribution of the population: Clusters One (21.5%), Two (27.9%), Three (28.7%), and Four (21.9%). Staphylococcus aureus distributed primarily to Clusters Three (40%) and Four (25%), while Enterobacteriaceae were divided predominantly into Clusters Two (34%), Three (30%), and Four (22%). Nonfermenting Gram-negative bacilli grouped mainly in Clusters Four and Two (30% and 31%). More than half of the pneumonia cases occurred in Cluster Four. Clusters One and Two contained 33% and 31%, respectively, of the individuals receiving inappropriate antibiotic administration. Mortality was greatest for Cluster Four (33.8%, 27.4%, 19.2%, 44.6%; P < 0.001), while Cluster One patients were most likely to be discharged to a nursing home. CONCLUSION: Our results support the potential for machine learning methods to identify homogenous groupings in infectious diseases that transcend old a priori classifications. These methods may allow new clinical phenotypes to be identified potentially improving the severity staging and treatment of complex infectious diseases. DISCLOSURES: All authors: No reported disclosures.
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spelling pubmed-62554862018-11-28 1008. Cluster Analysis to Define Distinct Clinical Phenotypes Among Septic Patients With Bloodstream Infections Guillamet, M Cristina Vazquez Bernauer, Michael Micek, Scott Kollef, Marin Open Forum Infect Dis Abstracts BACKGROUND: Prior attempts at identifying outcome determinants associated with bloodstream infection have employed a priori determined classification schemes based on readily identifiable microbiology, infection site, and patient characteristics. We hypothesized that even amongst this heterogeneous population, clinically relevant groupings can be described that transcend old a priori classifications. METHODS: Cluster analysis was applied to variables from three domains: patient characteristics, acuity of illness/clinical presentation, and infection characteristics. RESULTS: Among 3,715 patients with bloodstream infections from Barnes-Jewish Hospital (2008 to 2015), the most stable cluster arrangement occurred with the formation of four clusters. This clustering arrangement resulted in an approximately uniform distribution of the population: Clusters One (21.5%), Two (27.9%), Three (28.7%), and Four (21.9%). Staphylococcus aureus distributed primarily to Clusters Three (40%) and Four (25%), while Enterobacteriaceae were divided predominantly into Clusters Two (34%), Three (30%), and Four (22%). Nonfermenting Gram-negative bacilli grouped mainly in Clusters Four and Two (30% and 31%). More than half of the pneumonia cases occurred in Cluster Four. Clusters One and Two contained 33% and 31%, respectively, of the individuals receiving inappropriate antibiotic administration. Mortality was greatest for Cluster Four (33.8%, 27.4%, 19.2%, 44.6%; P < 0.001), while Cluster One patients were most likely to be discharged to a nursing home. CONCLUSION: Our results support the potential for machine learning methods to identify homogenous groupings in infectious diseases that transcend old a priori classifications. These methods may allow new clinical phenotypes to be identified potentially improving the severity staging and treatment of complex infectious diseases. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2018-11-26 /pmc/articles/PMC6255486/ http://dx.doi.org/10.1093/ofid/ofy210.845 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
Guillamet, M Cristina Vazquez
Bernauer, Michael
Micek, Scott
Kollef, Marin
1008. Cluster Analysis to Define Distinct Clinical Phenotypes Among Septic Patients With Bloodstream Infections
title 1008. Cluster Analysis to Define Distinct Clinical Phenotypes Among Septic Patients With Bloodstream Infections
title_full 1008. Cluster Analysis to Define Distinct Clinical Phenotypes Among Septic Patients With Bloodstream Infections
title_fullStr 1008. Cluster Analysis to Define Distinct Clinical Phenotypes Among Septic Patients With Bloodstream Infections
title_full_unstemmed 1008. Cluster Analysis to Define Distinct Clinical Phenotypes Among Septic Patients With Bloodstream Infections
title_short 1008. Cluster Analysis to Define Distinct Clinical Phenotypes Among Septic Patients With Bloodstream Infections
title_sort 1008. cluster analysis to define distinct clinical phenotypes among septic patients with bloodstream infections
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6255486/
http://dx.doi.org/10.1093/ofid/ofy210.845
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