Hospital-acquired bacterial infections lead to prolonged hospital stays and increased mortality. The problem is exacerbated by antibiotic-resistant strains that delay or impede effective treatment. To ensure successful therapy and to manage antibiotic resistance, treatment protocols that draw on several different antibiotics might be used. This includes the administration of drug cocktails to individual patients (combination therapy) but also the random assignment of drugs to different patients (mixing) and a regular switch in the default drug used in the hospital from drug A to drug B and back (cycling). For more than 20 years, mathematical models have been used to assess the prospects of antibiotic combination therapy, mixing and cycling. But while tendencies in their ranking across studies have emerged, the picture remains surprisingly inconclusive and incomplete. In this article, we review existing modelling studies and demonstrate by means of examples how methodological factors complicate the emergence of a consistent picture. These factors include the choice of the criterion by which the effects of the protocols are compared, the model implementation and its analysis. We thereafter discuss how progress can be made and suggest future modelling directions.

医院获得性细菌感染导致住院时间延长和死亡率增加。延迟或阻碍有效治疗的抗生素耐药菌株加剧了这个问题。为了确保成功的治疗和管理抗生素耐药性，可能会使用利用几种不同抗生素的治疗方案。这包括向个别患者施用药物鸡尾酒（联合治疗），还包括将药物随机分配给不同患者（混合）以及将医院使用的默认药物从药物A定期转换为药物B然后返回（骑自行车）。20 多年来，数学模型已被用于评估抗生素联合治疗、混合和循环治疗的前景。但是，虽然他们在不同研究中的排名趋势已经出现，但情况仍然出人意料地没有定论和不完整。在本文中，我们回顾了现有的建模研究，并通过示例展示了方法因素如何使一致图片的出现复杂化。这些因素包括比较协议效果的标准的选择、模​​型实现及其分析。此后，我们将讨论如何取得进展并提出未来的建模方向。