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Clinical Sepsis Phenotypes in Critically Ill Patients
Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophy...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10538192/ https://www.ncbi.nlm.nih.gov/pubmed/37764009 http://dx.doi.org/10.3390/microorganisms11092165 |
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author | Papathanakos, Georgios Andrianopoulos, Ioannis Xenikakis, Menelaos Papathanasiou, Athanasios Koulenti, Despoina Blot, Stijn Koulouras, Vasilios |
author_facet | Papathanakos, Georgios Andrianopoulos, Ioannis Xenikakis, Menelaos Papathanasiou, Athanasios Koulenti, Despoina Blot, Stijn Koulouras, Vasilios |
author_sort | Papathanakos, Georgios |
collection | PubMed |
description | Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process. |
format | Online Article Text |
id | pubmed-10538192 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105381922023-09-29 Clinical Sepsis Phenotypes in Critically Ill Patients Papathanakos, Georgios Andrianopoulos, Ioannis Xenikakis, Menelaos Papathanasiou, Athanasios Koulenti, Despoina Blot, Stijn Koulouras, Vasilios Microorganisms Review Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process. MDPI 2023-08-27 /pmc/articles/PMC10538192/ /pubmed/37764009 http://dx.doi.org/10.3390/microorganisms11092165 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Papathanakos, Georgios Andrianopoulos, Ioannis Xenikakis, Menelaos Papathanasiou, Athanasios Koulenti, Despoina Blot, Stijn Koulouras, Vasilios Clinical Sepsis Phenotypes in Critically Ill Patients |
title | Clinical Sepsis Phenotypes in Critically Ill Patients |
title_full | Clinical Sepsis Phenotypes in Critically Ill Patients |
title_fullStr | Clinical Sepsis Phenotypes in Critically Ill Patients |
title_full_unstemmed | Clinical Sepsis Phenotypes in Critically Ill Patients |
title_short | Clinical Sepsis Phenotypes in Critically Ill Patients |
title_sort | clinical sepsis phenotypes in critically ill patients |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10538192/ https://www.ncbi.nlm.nih.gov/pubmed/37764009 http://dx.doi.org/10.3390/microorganisms11092165 |
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