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RAPID: Open Research Infrastructure for COVID-19 Ventilator Data

RAPID：COVID-19 呼吸机数据的开放研究基础设施

基本信息

批准号：
2031509
负责人：
G J Peter Elmer
金额：
$ 20万
依托单位：
Princeton University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-15 至 2022-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031509&HistoricalAwards=false
关键词：
RAPID Open Research Infrastructure COVID

项目摘要

The lungs are a key avenue of attack for the SARS-CoV-2 virus. Respiratory problems are primary symptoms of COVID-19, and early indication is that it does not behave like previous examples of Acute Respiratory Distress Syndrome (ARDS). A severe urgency exists to understand how to provide optimal care for patients requiring artificial ventilation, to minimize both mortality and adverse long-term effects on those patients who survive. The project will illuminate lung function under the stress of COVID-19 and provide open tools to engage the larger community to help understand this very urgent societal problem. The project output will include instrumentation advances, software and data, as well as models of lung function under the stress of COVID-19. The project will also inform the medical community as to how to treat COVID-19 patients, because COVID-19 differs notably from prior experience with ARDS.Respiration and lung function is fundamentally a dynamical physical system amenable to traditional pressure/volume/flow relationships, with a quantity called "lung compliance." COVID-19 is unique, in that the underlying biology can lead to changes in the parameters of this dynamical system that are surprisingly fast, and different from previous ARDS cases, on the time scale of hours or days. Medical personnel need to navigate the evolving nature of the consequences of the viral infection as well as mechanical ventilation induced lung inflammation and potential injury, with outcomes ranging from recovery with varying impacts on post-illness lung function to death. This project consists of three related activities: (1) Instrumentation: Continued development of a low-cost, open-source ventilator monitor, including additional options for readily sourceable parts, and related documentation on calibrations. (2) Data: Development, with the broader community, of open datasets of breathing and ventilator data, including flow, pressure, O2 levels, and derived quantities of interest to enable innovation and machine learning in a space that otherwise lacks open data. (3) Models: Development of open simulations, visualizations, and models for mechanical ventilation and the breathing process, under stresses like COVID-19, that enable a physicist's understanding of the system, enable innovation, and can potentially aid the medical community.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplement allocated to MPS.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

肺是SARS-CoV-2病毒的主要攻击途径。呼吸问题是COVID-19的主要症状，早期迹象是它的表现不像以前的急性呼吸窘迫综合征（ARDS）。迫切需要了解如何为需要人工通气的患者提供最佳护理，以最大限度地减少死亡率和对存活患者的长期不良影响。该项目将阐明在COVID-19压力下的肺功能，并提供开放的工具，让更大的社区参与进来，帮助了解这一非常紧迫的社会问题。项目成果将包括仪器进步、软件和数据，以及COVID-19压力下的肺功能模型。该项目还将告知医学界如何治疗COVID-19患者，因为COVID-19与之前的ARDS经验明显不同。呼吸和肺功能基本上是一个动态物理系统，符合传统的压力/体积/流量关系，其数量称为"肺顺应性"。“COVID-19是独一无二的，因为潜在的生物学可以导致这个动力系统的参数发生变化，这种变化速度惊人，与以前的ARDS病例不同，在几小时或几天的时间尺度上。医务人员需要了解病毒感染以及机械通气引起的肺部炎症和潜在损伤的后果的不断变化的性质，其结果范围从对病后肺功能产生不同影响的恢复到死亡。该项目包括三项相关活动：（1）仪器：继续开发低成本、开源的呼吸机监测器，包括易于获取的部件的额外选项，以及有关校准的相关文件。(2)数据类型：与更广泛的社区一起开发呼吸和呼吸机数据的开放数据集，包括流量，压力，O2水平和感兴趣的衍生量，以在缺乏开放数据的空间中实现创新和机器学习。(3)型号：在COVID-19等压力下，开发开放式模拟、可视化和机械通气和呼吸过程模型，使物理学家能够理解系统，实现创新，并可能帮助医学界。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。