The Clinical Research Investigation and Systems Modeling of Acute Illness Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
Berry Consultants, LLC, Austin, Texas.
Ann Am Thorac Soc. 2020 Jul;17(7):879-891. doi: 10.1513/AnnalsATS.202003-192SD.
There is broad interest in improved methods to generate robust evidence regarding best practice, especially in settings where patient conditions are heterogenous and require multiple concomitant therapies. Here, we present the rationale and design of a large, international trial that combines features of adaptive platform trials with pragmatic point-of-care trials to determine best treatment strategies for patients admitted to an intensive care unit with severe community-acquired pneumonia. The trial uses a novel design, entitled "a randomized embedded multifactorial adaptive platform." The design has five key features: ) randomization, allowing robust causal inference; ) embedding of study procedures into routine care processes, facilitating enrollment, trial efficiency, and generalizability; ) a multifactorial statistical model comparing multiple interventions across multiple patient subgroups; ) response-adaptive randomization with preferential assignment to those interventions that appear most favorable; and ) a platform structured to permit continuous, potentially perpetual enrollment beyond the evaluation of the initial treatments. The trial randomizes patients to multiple interventions within four treatment domains: antibiotics, antiviral therapy for influenza, host immunomodulation with extended macrolide therapy, and alternative corticosteroid regimens, representing 240 treatment regimens. The trial generates estimates of superiority, inferiority, and equivalence between regimens on the primary outcome of 90-day mortality, stratified by presence or absence of concomitant shock and proven or suspected influenza infection. The trial will also compare ventilatory and oxygenation strategies, and has capacity to address additional questions rapidly during pandemic respiratory infections. As of January 2020, REMAP-CAP (Randomized Embedded Multifactorial Adaptive Platform for Community-acquired Pneumonia) was approved and enrolling patients in 52 intensive care units in 13 countries on 3 continents. In February, it transitioned into pandemic mode with several design adaptations for coronavirus disease 2019. Lessons learned from the design and conduct of this trial should aid in dissemination of similar platform initiatives in other disease areas.Clinical trial registered with www.clinicaltrials.gov (NCT02735707).
人们对改进方法以生成有关最佳实践的可靠证据非常感兴趣,尤其是在患者病情异质性且需要多种同时进行的治疗方法的情况下。在这里,我们提出了一项大型国际试验的原理和设计,该试验结合了适应性平台试验和实用即时护理试验的特点,以确定患有重症社区获得性肺炎的重症监护病房患者的最佳治疗策略。该试验采用了一种新颖的设计,称为“随机嵌入式多因素适应性平台”。该设计具有五个关键特征:)随机化,允许进行稳健的因果推理;)将研究程序嵌入常规护理过程中,便于入组、提高试验效率和推广性;)多因素统计模型比较多个干预措施在多个患者亚组中的效果;)响应适应性随机化,优先分配给那些效果看起来最有利的干预措施;)平台结构允许在评估初始治疗方法之外持续、潜在地无限期入组。该试验将患者随机分配到四个治疗领域的多种干预措施中:抗生素、流感的抗病毒治疗、延长大环内酯类药物治疗的宿主免疫调节和替代皮质类固醇治疗方案,共 240 种治疗方案。该试验根据是否存在合并性休克以及是否存在已证实或疑似流感感染,对 90 天死亡率的主要结局进行分层,评估方案之间的优越性、劣效性和等效性。该试验还将比较通气和氧合策略,并在大流行呼吸道感染期间迅速解决其他问题的能力。截至 2020 年 1 月,REMAP-CAP(社区获得性肺炎的随机嵌入式多因素适应性平台)已获得批准,并在 3 大洲 13 个国家的 52 个重症监护病房入组患者。2 月,它过渡到大流行模式,针对 2019 年冠状病毒病进行了几项设计调整。从该试验的设计和实施中吸取的经验教训应有助于在其他疾病领域推广类似的平台计划。该试验已在 www.clinicaltrials.gov 上注册(NCT02735707)。