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.

背景和目的：已经提出了经验性的抗菌处方策略来抵消耐药病原菌的选择。这种策略各自的优点已被争论。与其比较有限数量的策略，我们采用一种优化方法并提出一种解决方案，以解决在医疗保健机构中发现经验疗法策略的问题，该策略可以最大程度地减少给定时间范围内累计的患者感染日数。方法：我们假设该模型的参数是已知的，并且在实施该策略时，所有患者在给定的时间都将接受相同的治疗。我们使用隔室模型的随机版本对人群中抗菌素耐药性的出现和扩散进行建模。该模型具有两种药物以及双重耐药性的可能性。我们的解决方法是推出算法。结果：在我们的示例中，与最佳标准疗法相比，使用此方法计算出的最佳策略可以将两年内的平均累积感染患者天数减少22％。考虑到规则性约束，我们可以得出具有固定周期且性能接近最佳策略的策略。使用最佳策略获得的两年中的平均累计感染患者天数比固定期限策略获得的平均患者感染天数低6％（显着低于95％阈值）。结论：我们的结果说明了高度灵活的解决方案方法的性能，该方法将有助于制定可实施的经验疗法。