Suspected sepsis: patient assessment and management in the emergency department.
Published In: Emergency Nurse, 2025, v. 33, n. 3. P. 34 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Hird, Clare; Parker, Mike 3 of 3
Abstract
Why you should read this article: • To refresh your knowledge of the pathophysiology and signs and symptoms of sepsis • To enhance your understanding of the management of suspected sepsis in the emergency department • To contribute towards revalidation as part of your 35 hours of CPD (UK readers) • To contribute towards your professional development and local registration renewal requirements (non-UK readers). Sepsis is a potentially life-threatening condition triggered by infection that is responsible for an estimated 48,000 deaths in the UK each year. Its pathophysiology is complex, its symptomology non-specific and its clinical presentations extremely varied. Despite numerous campaigns to raise awareness of sepsis, it still goes undetected. In 2024, the National Institute for Health and Clinical Excellence revised its guideline on sepsis and the UK Sepsis Trust published the seventh edition of its Sepsis Manual. This article discusses the pathophysiology of sepsis and how emergency nurses should assess and manage patients with suspected sepsis. It describes the tools available to them, including the National Early Warning Score 2 and the Sepsis 6, and emphasises the importance of early antibiotic administration, serial lactate measurements, source control and antimicrobial stewardship. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Emergency Nurse. 2025/05, Vol. 33, Issue 3, p34
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2025
- ISSN:1354-5752
- DOI:10.7748/en.2025.e2221
- Accession Number:184955459
- Copyright Statement:Copyright of Emergency Nurse is the property of Royal College of Nursing of the United Kingdom (The) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.