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...

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Main Authors: Georgios Papathanakos, Ioannis Andrianopoulos, Menelaos Xenikakis, Athanasios Papathanasiou, Despoina Koulenti, Stijn Blot, Vasilios Koulouras
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:Microorganisms
Subjects:
Online Access:https://www.mdpi.com/2076-2607/11/9/2165
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author Georgios Papathanakos
Ioannis Andrianopoulos
Menelaos Xenikakis
Athanasios Papathanasiou
Despoina Koulenti
Stijn Blot
Vasilios Koulouras
author_facet Georgios Papathanakos
Ioannis Andrianopoulos
Menelaos Xenikakis
Athanasios Papathanasiou
Despoina Koulenti
Stijn Blot
Vasilios Koulouras
author_sort Georgios Papathanakos
collection DOAJ
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.
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spelling doaj.art-397acca224fb4e1ab21d468a5d4de7a02023-11-19T12:01:29ZengMDPI AGMicroorganisms2076-26072023-08-01119216510.3390/microorganisms11092165Clinical Sepsis Phenotypes in Critically Ill PatientsGeorgios Papathanakos0Ioannis Andrianopoulos1Menelaos Xenikakis2Athanasios Papathanasiou3Despoina Koulenti4Stijn Blot5Vasilios Koulouras6Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, GreeceDepartment of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, GreeceDepartment of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, GreeceDepartment of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, GreeceUQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QL 4029, AustraliaDepartment of Internal Medicine & Pediatrics, Ghent University, 9000 Ghent, BelgiumDepartment of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, GreeceSepsis, 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.https://www.mdpi.com/2076-2607/11/9/2165sepsisheterogeneityphenotypemachine learningprecision medicine
spellingShingle Georgios Papathanakos
Ioannis Andrianopoulos
Menelaos Xenikakis
Athanasios Papathanasiou
Despoina Koulenti
Stijn Blot
Vasilios Koulouras
Clinical Sepsis Phenotypes in Critically Ill Patients
Microorganisms
sepsis
heterogeneity
phenotype
machine learning
precision medicine
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 sepsis
heterogeneity
phenotype
machine learning
precision medicine
url https://www.mdpi.com/2076-2607/11/9/2165
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