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Title: An agent-based model on antimicrobial de-escalation in intensive care units: Implications on clinical trial design
Antimicrobial de-escalation refers to reducing the spectrum of antibiotics used in treating bacterial infections. This strategy is widely recommended in many antimicrobial stewardship programs and is believed to reduce patients’ exposure to broad-spectrum antibiotics and prevent resistance. However, the ecological benefits of de-escalation have not been universally observed in clinical studies. This paper conducts computer simulations to assess the ecological effects of de-escalation on the resistance prevalence ofPseudomonas aeruginosa—a frequent pathogen causing nosocomial infections. Synthetic data produced by the models are then used to estimate the sample size and study period needed to observe the predicted effects in clinical trials. Our results show that de-escalation can reduce colonization and infections caused by bacterial strains resistant to the empiric antibiotic, limit the use of broad-spectrum antibiotics, and avoid inappropriate empiric therapies. Further, we show that de-escalation could reduce the overall super-infection incidence, and this benefit becomes more evident under good compliance with hand hygiene protocols among health care workers. Finally, we find that any clinical study aiming to observe the essential effects of de-escalation should involve at least ten arms and last for four years—a size never attained in prior studies. This study explains the controversial findings of de-escalation in previous clinical studies and illustrates how mathematical models can inform outcome expectations and guide the design of clinical studies.  more » « less
Award ID(s):
2052648
PAR ID:
10520441
Author(s) / Creator(s):
;
Editor(s):
Yang, Junyuan
Publisher / Repository:
PLOS ONE
Date Published:
Journal Name:
PLOS ONE
Volume:
19
Issue:
4
ISSN:
1932-6203
Page Range / eLocation ID:
e0301944
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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