Innovations in Sepsis Management in the Emergency Department: Rapid Diagnostics, Antimicrobial Resistance, Precision Medicine, and AI

Sepsis remains one of the leading causes of mortality and healthcare burden worldwide, with the emergency department (ED) representing a critical gateway for timely recognition and management. Despite advances in critical care, early diagnosis and targeted therapy in the ED continue to pose significant challenges. The growing threat of antimicrobial resistance (AMR), the limitations of conventional culture-based diagnostics, and the heterogeneity of septic patients underscore the urgent need for innovative strategies.

Recent developments in syndromic molecular panels, point-of-care testing, and biomarker-driven algorithms have the potential to revolutionize the early etiological diagnosis of sepsis. At the same time, the increasing prevalence of multidrug-resistant organisms in community and healthcare-associated infections requires robust antimicrobial stewardship strategies, particularly in time-sensitive ED settings. Moreover, the application of artificial intelligence, machine learning, and precision medicine approaches may allow clinicians to identify distinct sepsis phenotypes, enabling more personalized and effective treatments.

This Research Topic aims to gather original research, reviews, and perspectives exploring:

- The role of rapid molecular diagnostics and novel point-of-care technologies for early sepsis identification in the ED.

- Strategies to address antimicrobial resistance and optimize empiric therapy within the framework of antimicrobial stewardship.

- Advances in predictive analytics, machine learning, and personalized medicine approaches applied to septic patients in acute care.

- Clinical trials, implementation studies, and real-world evidence assessing the impact of innovative tools on outcomes such as mortality, length of stay, and resource utilization.

By integrating these perspectives, this collection seeks to advance the translation of cutting-edge diagnostic and therapeutic innovations into clinical practice, ultimately improving the timely management of sepsis in emergency care.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Study Protocol
• Systematic Review

Keywords: Sepsis, Machine Learning, Precision medicine, Antimicrobial resistance, Rapid molecular diagnosis

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.