Comparing optimization criteria in antibiotic allocation protocols

Area of Science:

• Infectious Diseases
• Mathematical Biology
• Public Health

Background:

• Hospital antibiotic prescribing relies on treatment protocols to manage patient needs and antibiotic resistance.
• Existing criteria for evaluating these protocols often conflict, leading to differing recommendations.
• The evolution of antibiotic resistance and multi-resistant bacteria poses a significant long-term threat.

Purpose of the Study:

• To review and compare existing criteria for assessing antibiotic treatment protocols.
• To demonstrate the conflict between criteria prioritizing resistance evolution and patient health.
• To introduce a novel optimization criterion balancing present and future patient needs.

Main Methods:

• Comparative analysis of existing optimization criteria for antibiotic stewardship.
• Development and evaluation of a new optimization criterion using asymptotic methods.
• Modeling antibiotic cycling strategies to identify optimal switching times.

Main Results:

• Criteria focused solely on slowing resistance evolution are detrimental to both short-term and long-term patient health.
• The newly proposed criterion offers a more balanced approach to optimizing antibiotic protocols.
• Asymptotic methods provide novel insights into protocol evaluation compared to previous numerical methods.
• An optimal antibiotic switching time was identified for cycling strategies across various modeling assumptions.

Conclusions:

• Antibiotic stewardship protocols must carefully balance immediate patient care with the long-term threat of resistance.
• A new optimization framework is proposed to better achieve this balance.
• The findings suggest that resistance-focused strategies may inadvertently harm patients, necessitating a revised approach to antibiotic cycling and protocol design.