| Literature DB >> 35433311 |
Juan Carlos Ruiz-Rodriguez1, Erika P Plata-Menchaca2, Luis Chiscano-Camón1, Adolfo Ruiz-Sanmartin1, Marcos Pérez-Carrasco1, Clara Palmada1, Vicent Ribas3, Mónica Martínez-Gallo4, Manuel Hernández-González4, Juan J Gonzalez-Lopez5, Nieves Larrosa5, Ricard Ferrer1.
Abstract
Sepsis is a heterogeneous disease with variable clinical course and several clinical phenotypes. As it is associated with an increased risk of death, patients with this condition are candidates for receipt of a very well-structured and protocolized treatment. All patients should receive the fundamental pillars of sepsis management, which are infection control, initial resuscitation, and multiorgan support. However, specific subgroups of patients may benefit from a personalized approach with interventions targeted towards specific pathophysiological mechanisms. Herein, we will review the framework for identifying subpopulations of patients with sepsis, septic shock, and multiorgan dysfunction who may benefit from specific therapies. Some of these approaches are still in the early stages of research, while others are already in routine use in clinical practice, but together will help in the effective generation and safe implementation of precision medicine in sepsis. ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.Entities:
Keywords: Biomarkers; Endotype; Organ dysfunction; Phenotype; Precision medicine; Sepsis; Septic shock
Year: 2022 PMID: 35433311 PMCID: PMC8788206 DOI: 10.5492/wjccm.v11.i1.1
Source DB: PubMed Journal: World J Crit Care Med ISSN: 2220-3141
Figure 1Strategies to create precision medicine in sepsis.
Clinical applicability of precision medicine strategies
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| Genomics and epigenomics | Genetic variants | Prognosis, severity |
| Genotypes | Susceptibility to sepsis | |
| Transcriptomics | Gene expression profiles, activity and regulation | Susceptibility to sepsis |
| Sepsis response signatures | Severity, prognosis | |
| Metabolomics | Small molecules produced by cells | Prognosis |
| Metabolomic profile | Response to treatment | |
| Proteomics | Proteins expressed by the genome under certain conditions | Diagnosis, Prognosis |
| Biomarkers | Diagnosis, prognosis | |
| Bioinformatics | Machine learning techniques | Diagnosis |
| Prediction of clinical trajectories | ||
| Assessment and treatment of organ dysfunction | ||
| Clinical phenotypes | ||
| Biomarkers | Levels of molecules (mostly inflammatory) | Phenotypes |
| Antimicrobial stewardship | ||
| Prediction of organ dysfuntion | ||
| Allocation of hospital resources | ||
| Diagnosis | ||
| Severity | ||
| Immunoglobulins | Immunoglobulin levels | Detection and treatment of sepsis-associated hypogammaglobulinemia |
| Endotoxin and hemoadsoption | Endotoxin levels and elimination by hemoadsoption | Rescue therapy |
| Cytokines and hemoadsoption | Cytokine levels and elimination by hemoadsoption | Rescue therapy |
| Immunoparalysis | mHLA-DR expression | Immunoparalysis detection |
| Immunoadjuvant treatment | ||
| Stratification of patients | ||
| GM-CSF therapy |
GM-CSF: Granulocyte-macrophage colony-stimulating factor.