Model-Based Decisions in Sepsis

脓毒症基于模型的决策

基本信息

批准号：
9249074
负责人：
Gilles Clermont
金额：
$ 27.77万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-05-01 至 2019-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9249074
关键词：
Acute Age Animal Model Beds Biological Caring Cessation of life Characteristics Clinical Medicine Clinical Research Clinical Trials Clinical Trials Design Collaborations Communities Complex Computer Simulation Computing Methodologies Critical Pathways Data Data Set Development Discipline Discrimination Disease Disease Outcome Epidemiology Etiology Failure Funding Genetic Polymorphism Goals Healthcare Systems Hour Human Individual Infection Inflammation Inflammatory Inflammatory Response Intervention Intervention Trial Knowledge Discovery Methods Modeling Organ failure Organism Outcome Patients Pharmacology Physiological Positioning Attribute Process Protocols documentation Race Randomized Randomized Clinical Trials Randomized Controlled Trials Recovery Research Resuscitation Sepsis Septic Shock Severity of illness Societies Subgroup System Techniques Technology Testing Therapeutic Therapeutic Agents Time Treatment Protocols United States National Institutes of Health Update Validation Weather arm base clinically relevant cohort control trial design human data immunoregulation improved individualized medicine insight inter-individual variation mathematical methods novel therapeutics pathogen personalized medicine pre-clinical programs prospective public health relevance randomized trial sex treatment strategy trial design

项目摘要

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.

描述（由申请人提供）：针对急性炎症性疾病（如脓毒症）的免疫调节干预措施的大型随机临床试验（败血症）的记录很沮丧。正如FDA关键路径文档和NIH RoadMap倡议中提出的那样，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位置疾病（例如败血症）的潜在巨大影响。然而，炎症的生物水平计算模型很少。但是，更基本的是缺乏可以验证此类模型的人类数据集。这样的数据集的组装将非常昂贵，并且在没有证明有效性的模型的情况下，不太可能仅用于测试基于模型的干预措施。 NIH资助的早期败血性休克（过程）研究的协议护理目前正在研究1350例患者前瞻性随机试验中早期复苏对严重败血症受害者的影响，并将产生具有颗粒状的数据，这些数据不仅会有助于理解脓毒症的过程，还可以帮助了解物理学靶向靶向治疗方案的生物学后果。该提案中概述的程序的总体目标是使用流程研究中的数据通过高级数学和计算方法来验证人类败血症的计算模型。我们已经组建了一个跨学科的建模者和临床医生组，并在急性炎症模型中，尤其是不同水平的粒度水平，在开发，校准和测试方面取得了成功的合作记录。我们认为，在大型临床相关的队列中，在计算机模型中的验证绝对是对计算建模的合法化，这将证明这对于一般智能随机介入试验的设计至关重要，通常是个性化疗法。一方面利用过程试验中的数据和初步分析，以及广泛的现有的跨学科努力，以扩大另一方面的急性炎症反应的现有计算模型，这也将提供前所未有的机会，以获得对导致器官失败和死亡，死亡，系统恢复和意外失败的过程的机械理解。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

APT-MCMC, a C++/Python implementation of Markov Chain Monte Carlo for parameter identification.

APT-MCMC，马尔可夫链蒙特卡罗的 C /Python 实现，用于参数识别。

DOI：
10.1016/j.compchemeng.2017.11.011
发表时间：
2018
期刊：
Computers & chemical engineering
影响因子：
4.3
作者：
Zhang,LiAng;Urbano,Alisa;Clermont,Gilles;Swigon,David;Banerjee,Ipsita;Parker,RobertS
通讯作者：
Parker,RobertS

A One-Nearest-Neighbor Approach to Identify the Original Time of Infection Using Censored Baboon Sepsis Data.

一种使用截尾狒狒脓毒症数据识别原始感染时间的最近邻方法。

DOI：
10.1097/ccm.0000000000001623
发表时间：
2016
期刊：
Critical care medicine
影响因子：
8.8
作者：
Zhang,LiAng;Parker,RobertS;Swigon,David;Banerjee,Ipsita;Bahrami,Soheyl;Redl,Heinz;Clermont,Gilles
通讯作者：
Clermont,Gilles

Mathematical modeling of energy consumption in the acute inflammatory response.

急性炎症反应中能量消耗的数学模型。