Informed and uninformed empirical therapy policies.

Area of Science:

• Infectious disease modeling
• Health economics
• Pharmacology

Background:

• Empirical therapy policies are essential for managing infections in healthcare settings.
• Understanding pathogen transmission and drug resistance is critical for effective treatment.
• Information uncertainty impacts the optimal choice of empirical therapy.

Purpose of the Study:

• To differentiate between informed and uninformed decision-making in empirical therapy.
• To estimate the value of additional information on pathogens and resistance.
• To guide research priorities in antimicrobial stewardship.

Main Methods:

• Utilized a stochastic compartmental model to simulate pathogen spread and drug resistance in healthcare facilities.
• Focused on information and uncertainty related to model parameters.
• Evaluated a family of adaptive empirical therapy policies.

Main Results:

• The best uninformed adaptive policy reduced average cumulative infected patient days by 39.3% compared to standard combination therapy.
• Informed decision-making, using exact parameter values, further decreased patient days by 3.9%.
• The potential benefits of perfect information may be counterbalanced by increased drug consumption.

Conclusions:

• A clear distinction between informed and uninformed empirical therapy decision-making is necessary.
• This distinction aids in valuing information and setting research priorities for combating infections and resistance.
• Optimizing empirical therapy involves balancing treatment efficacy with resource utilization and drug consumption.