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Glucocorticoid therapy for sepsis in the AI era: a survey on current and future approaches.

Authors :
Liang C
Pan S
Wu W
Chen F
Zhang C
Zhou C
Gao Y
Ruan X
Quan S
Zhao Q
Pan J
Source :
Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2024 Apr 12; Vol. 24, pp. 292-305. Date of Electronic Publication: 2024 Apr 12 (Print Publication: 2024).
Publication Year :
2024

Abstract

Sepsis, a life-threatening medical condition, manifests as new or worsening organ failures due to a dysregulated host response to infection. Many patients with sepsis have manifested a hyperinflammatory phenotype leading to the identification of inflammatory modulation by corticosteroids as a key treatment modality. However, the optimal use of corticosteroids in sepsis treatment remains a contentious subject, necessitating a deeper understanding of their physiological and pharmacological effects. Our study conducts a comprehensive review of randomized controlled trials (RCTs) focusing on traditional corticosteroid treatment in sepsis, alongside an analysis of evolving clinical guidelines. Additionally, we explore the emerging role of artificial intelligence (AI) in medicine, particularly in diagnosing, prognosticating, and treating sepsis. AI's advanced data processing capabilities reveal new avenues for enhancing corticosteroid therapeutic strategies in sepsis. The integration of AI in sepsis treatment has the potential to address existing gaps in knowledge, especially in the application of corticosteroids. Our findings suggest that combining corticosteroid therapy with AI-driven insights could lead to more personalized and effective sepsis treatments. This approach holds promise for improving clinical outcomes and presents a significant advancement in the management of this complex and often fatal condition.<br />Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.<br /> (© 2024 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

Details

Language :
English
ISSN :
2001-0370
Volume :
24
Database :
MEDLINE
Journal :
Computational and structural biotechnology journal
Publication Type :
Academic Journal
Accession number :
38681133
Full Text :
https://doi.org/10.1016/j.csbj.2024.04.020