Biomarkers for surgical sepsis. A review of foreign scientific and medical publications

remained unchanged for over a decade, and early recognition continues to be the most crucial factor in survival outcome. Early and accurate diagnosis of infection and organ dysfunction remains problematic, as evidenced by numerous interventional trials that have not resulted in improved outcomes. Th...

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Bibliographic Details
Main Authors: Sergey G. Sсherbak, Andrey M. Sarana, Dmitry A. Vologzhanin, Aleksandr S. Golota, Aleksandr A. Rud’, Tatiana A. Kamilova
Format: Article
Language:English
Published: Eco-vector 2023-07-01
Series:Клиническая практика
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Online Access:https://journals.eco-vector.com/clinpractice/article/viewFile/346695/pdf
Description
Summary:remained unchanged for over a decade, and early recognition continues to be the most crucial factor in survival outcome. Early and accurate diagnosis of infection and organ dysfunction remains problematic, as evidenced by numerous interventional trials that have not resulted in improved outcomes. These failures are partly because of the belated intervention, when the patient developed multiple-organ failure and the therapeutic window of opportunity closed. The success of immunomodulatory and other therapeutic strategies, which is often achieved in preclinical models of sepsis, depends on their use in the early stages of sepsis development or even proactive action. Predicting the development of sepsis in surgical patients using laboratory analysis of plasma may be useful for doctors in the intensive care unit and resuscitation. Significant efforts are being made to develop biomarkers for the early stages of sepsis with high sensitivity and specificity. For early and accurate diagnosis, effective treatment of sepsis requires a deep understanding of the pathogenetic mechanisms. Dysregulation of the patients response to infection leading to sepsis and septic shock is studied using ohmic approaches: proteomics, transcriptomics, and metabolomics. Owing to the complexity and large volume of data sets, special data analysis tools, the so-called machine learning, become necessary.
ISSN:2220-3095
2618-8627