MRC Clinical Trials Unit at University College London, London, UK; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
MRC Clinical Trials Unit at University College London, London, UK.
Lancet Infect Dis. 2021 Jun;21(6):e175-e181. doi: 10.1016/S1473-3099(20)30791-X. Epub 2021 Apr 21.
Antimicrobial resistance is impacting treatment decisions for, and patient outcomes from, bacterial infections worldwide, with particular threats from infections with carbapenem-resistant Enterobacteriaceae, Acinetobacter baumanii, or Pseudomonas aeruginosa. Numerous areas of clinical uncertainty surround the treatment of these highly resistant infections, yet substantial obstacles exist to the design and conduct of treatment trials for carbapenem-resistant bacterial infections. These include the lack of a widely acceptable optimised standard of care and control regimens, varying antimicrobial susceptibilities and clinical contraindications making specific intervention regimens infeasible, and diagnostic and recruitment challenges. The current single comparator trials are not designed to answer the urgent public health question, identified as a high priority by WHO, of what are the best regimens out of the available options that will significantly reduce morbidity, costs, and mortality. This scenario has an analogy in network meta-analysis, which compares multiple treatments in an evidence synthesis to rank the best of a set of available treatments. To address these obstacles, we propose extending the network meta-analysis approach to individual randomisation of patients. We refer to this approach as a Personalised RAndomised Controlled Trial (PRACTical) design that compares multiple treatments in an evidence synthesis, to identify, overall, which is the best treatment out of a set of available treatments to recommend, or how these different treatments rank against each other. In this Personal View, we summarise the design principles of personalised randomised controlled trial designs. Specifically, of a network of different potential regimens for life-threatening carbapenem-resistant infections, each patient would be randomly assigned only to regimens considered clinically reasonable for that patient at that time, incorporating antimicrobial susceptibility, toxicity profile, pharmacometric properties, availability, and physician assessment. Analysis can use both direct and indirect comparisons across the network, analogous to network meta-analysis. This new trial design will maximise the relevance of the findings to each individual patient, and enable the top-ranked regimens from any personalised randomisation list to be identified, in terms of both efficacy and safety.
抗菌药物耐药性正在影响全球范围内细菌感染的治疗决策和患者预后，碳青霉烯类耐药肠杆菌科、鲍曼不动杆菌或铜绿假单胞菌感染尤其构成威胁。这些高度耐药感染的治疗存在许多临床不确定性，但设计和开展碳青霉烯类耐药细菌感染治疗试验存在巨大障碍。这些障碍包括缺乏广泛可接受的优化标准护理和对照方案、抗菌药物敏感性和临床禁忌不同，使得特定干预方案不可行，以及诊断和招募挑战。目前的单一比较试验设计无法回答世卫组织确定的紧迫公共卫生问题，即从现有选择中，哪些方案是最佳方案，能显著降低发病率、成本和死亡率。这种情况与网络荟萃分析类似，后者在证据综合中比较多种治疗方法，以对一组现有治疗方法中的最佳治疗方法进行排名。为了解决这些障碍，我们建议将网络荟萃分析方法扩展到患者的个体随机分组。我们将这种方法称为个性化随机对照试验（PRACTical）设计，该设计在证据综合中比较多种治疗方法，以确定总体而言，在一组现有治疗方法中，哪种治疗方法最好推荐，或者这些不同治疗方法如何相互排名。在本个人观点中，我们总结了个性化随机对照试验设计的设计原则。具体来说，对于危及生命的碳青霉烯类耐药感染的多种潜在治疗方案网络，每个患者仅随机分配到当时认为对该患者临床合理的方案，包括抗菌药物敏感性、毒性特征、药代动力学特性、可用性和医生评估。分析可以使用网络内的直接和间接比较，类似于网络荟萃分析。这种新的试验设计将最大限度地提高研究结果对每个个体患者的相关性，并能够根据疗效和安全性确定任何个性化随机分组列表中的排名最高的方案。
