Personalised antimicrobial susceptibility testing with clinical prediction modelling informs appropriate antibiotic...

Area of Science:

• Clinical microbiology
• Infectious diseases
• Health informatics

Background:

• Antimicrobial resistance (AMR) is a growing global health threat.
• Current diagnostic microbiology laboratory approaches for antimicrobial susceptibility testing (AST) are often standardized and lack personalization.
• This can lead to suboptimal treatment choices for infections like urinary tract infections (UTIs).

Purpose of the Study:

• To develop and evaluate a personalized antimicrobial susceptibility testing (AST) approach for UTIs.
• To maximize appropriate antibiotic treatment options for individual patients.
• To prioritize the use of World Health Organization (WHO) Access category antibiotics to combat AMR.

Main Methods:

• Development and assessment of susceptibility prediction models for 12 antibiotics.
• Utilized real-world healthcare data for an individual-level simulation study.
• Integrated decision thresholds prioritizing WHO Access antibiotics.

Main Results:

• The personalized AST approach yielded more susceptible results for WHO Access antibiotics per specimen compared to standard testing.
• The personalized approach did not compromise the overall number of susceptible results provided.
• This demonstrates the potential for improved antibiotic stewardship.

Conclusions:

• Personalized AST can enhance the selection of appropriate and effective antibiotic treatments for UTIs.
• This tailored strategy supports the fight against antimicrobial resistance by optimizing antibiotic use.
• Personalized AST offers a safe and effective way to increase clinician access to vital Access category antibiotics.