Model-Based Decisions in Sepsis

描述（由申请人提供）：免疫调节干预治疗急性炎症性疾病（如脓毒症）的大型随机临床试验记录令人沮丧。宿主-病原体相互作用的生物学复杂性以及成功治疗对医疗保健系统和社会的潜在巨大影响，将脓毒症等疾病定位为基于模型的治疗方法的理想试验床，如FDA关键路径文件和NIH路线图倡议中所提出的。然而，缺乏生物体水平的炎症计算模型。然而，更根本的是缺乏可以验证这些模型的人类数据集。这样一个数据集的汇集将是非常昂贵的，而且在没有经证明有效的模型的情况下，仅仅为了测试基于模型的干预措施而获得这样一个数据集是极不可能的。美国国立卫生研究院资助的早期感染性休克的协议化护理（ProCESS）研究目前正在研究早期复苏对1350例患者的严重脓毒症患者的影响前瞻性随机试验，并将产生一个粒度的数据集，不仅有助于了解脓毒症涉及的过程，还有助于了解生理目标导向治疗方案的生物学后果。本提案中概述的计划的总体目标是通过先进的数学和计算方法，使用ProCESS研究的数据验证人类脓毒症的计算模型。我们已经组建了一个跨学科的建模师和临床医生小组，他们在开发、校准和测试不同粒度的急性炎症（特别是脓毒症）的计算机模型方面有着成功的合作记录。我们认为，在一个大型的临床相关队列中验证计算机模型对于计算建模的合法化是绝对至关重要的，因为计算建模是一种技术，将被证明是设计更智能的随机干预性试验的关键，特别是个性化治疗。利用数据和初步分析，一方面从过程试验和广泛的现有的跨学科的努力，在扩大现有的计算模型的急性炎症反应，另一方面也将提供一个前所未有的机会，获得机制的理解的过程，导致器官衰竭和死亡，全身恢复和意外的失败。

DESCRIPTION (provided by applicant): Large randomized clinical trials of immunomodulatory interventions for acute inflammatory diseases such as sepsis have had a dismal track record. The biological complexity of the host-pathogen interaction and the potential large impact of a successful treatment on the health care system and society position diseases such as sepsis as ideal test beds for model-based therapeutic approaches, as proposed in the FDA critical path document and the NIH roadmap initiative. Yet, there is a paucity of organism-level computational models of inflammation. More fundamental however, is the lack of human data sets where such models could be validated. Such a data set would be extraordinarily expensive to assemble and is highly unlikely to be acquired merely for testing model-based interventions in the absence of models with demonstrated validity. The NIH-funded Protocolized Care for Early Septic Shock (ProCESS) study is currently examining the impact of early resuscitation in victims of severe sepsis in a 1350 patient prospective randomized trial and will produce a data set with a granularity that will not only help to understand the processes involved in sepsis, but also the biological consequences of a physiologic goal-directed treatment protocol. The overarching goal of the program outlined in this proposal is to validate computational models of human sepsis using data from the ProCESS study through advanced mathematical and computational methods. We have assembled a transdisciplinary group of modelers and clinicians with an eloquent track record of successful collaboration on developing, calibrating and testing in silico models of acute inflammation, and of sepsis in particular, of different levels of granularity. We believe that validation of in silico models in a large clinically relevant cohort is absolutely cruial to the legitimization of computational modeling as a technology that will prove pivotal to the design of smarter randomized interventional trials in general, and of personalized therapies in particular. Leveraging data and preliminary analyses from the ProCESS trial on the one hand and an extensive existing transdisciplinary effort at expanding existing computational models of the acute inflammatory response on the other will also provide an unprecedented opportunity to gain mechanistic understanding of the processes leading to organ failure and death, systemic recovery and unexpected failure.