Abstract：
BACKGROUND AND OBJECTIVES:Empirical antimicrobial prescription strategies have been proposed to counteract the selection of resistant pathogenic strains. The respective merits of such strategies have been debated. Rather than comparing a finite number of policies, we take an optimization approach and propose a solution to the problem of finding an empirical therapy policy in a health care facility that minimizes the cumulative infected patient-days over a given time horizon. METHODS:We assume that the parameters of the model are known and that when the policy is implemented, all patients receive the same treatment at a given time. We model the emergence and spread of antimicrobial resistance at the population level with the stochastic version of a compartmental model. The model features two drugs and the possibility of double resistance. Our solution method is a rollout algorithm. RESULTS:In our example, the optimal policy computed with this method allows to reduce the average cumulative infected patient-days over two years by 22% compared to the best standard therapy. Considering regularity constraints, we could derive a policy with a fixed period and a performance close to that of the optimal policy. The average cumulative infected patient-days over two years obtained with the optimal policy is 6% lower (significantly at the 95% threshold) than that obtained with the fixed period policy. CONCLUSION:Our results illustrate the performance of a highly flexible solution method that will contribute to the development of implementable empirical therapy policies.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Houy N,Flaig Jdoi
10.1016/j.cmpb.2020.105767subject
Has Abstractpub_date
2021-01-01 00:00:00pages
105767eissn
0169-2607issn
1872-7565pii
S0169-2607(20)31600-Xjournal_volume
198pub_type
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