Integrating Precision Sepsis Risk Stratification Into Systems-Based Antimicrobial Stewardship

Area of Science:

• Critical Care Medicine
• Infectious Diseases
• Health Systems Engineering

Background:

• Early broad-spectrum antimicrobial administration in sepsis is linked to better survival rates.
• Mandated antibiotic timing has increased antimicrobial use, fueling global antimicrobial resistance.
• This creates a tension between optimizing individual patient outcomes and preserving long-term antimicrobial effectiveness.

Purpose of the Study:

• To frame the "sepsis-antimicrobial stewardship tension" as a systems-level issue.
• To review evidence on the heterogeneous benefits of early antibiotics in sepsis.
• To explore how advanced diagnostics and AI can enable precision antimicrobial timing.

Main Methods:

• Perspective piece analyzing the sepsis-antimicrobial stewardship tension.
• Review of emerging evidence on early antibiotic benefits.
• Discussion of diagnostic uncertainty, incentives, and guideline implementation.
• Exploration of AI, machine learning, and host-response diagnostics.

Main Results:

• Rigid quality metrics, like SEP-1, may inadvertently increase unnecessary antimicrobial use.
• Heterogeneity exists in the benefits derived from early antibiotic administration in sepsis.
• Advances in diagnostics and AI offer potential for risk-guided antimicrobial timing.

Conclusions:

• Addressing the sepsis-antimicrobial stewardship tension requires a systems-level approach.
• Precision diagnostics and AI can help balance immediate sepsis care with long-term antimicrobial stewardship.
• Integrating these tools via health systems engineering is crucial for combating antimicrobial resistance.