Houy N, Flaig J. Optimal dynamic empirical therapy in a health care facility: A Monte-Carlo look-ahead method.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021;
198:105767. [PMID:
33086150 DOI:
10.1016/j.cmpb.2020.105767]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 09/18/2020] [Indexed: 06/11/2023]
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.
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