Investigating Strategies for Mechanical Ventilation in COVID-19 via Computational Simulation of Virtual Patients

通过虚拟患者的计算模拟研究 COVID-19 的机械通气策略

基本信息

  • 批准号:
    EP/V014455/1
  • 负责人:
  • 金额:
    $ 44.16万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2020
  • 资助国家:
    英国
  • 起止时间:
    2020 至 无数据
  • 项目状态:
    已结题

项目摘要

Initial clinical guidelines for mechanical ventilation of COVID-19 patients suggested following standard approaches used in the treatment of acute respiratory distress syndrome (ARDS). However, emerging clinical experience suggests that patients with COVID-19 pneumonia may present an atypical form of the syndrome, characterised (at least initially) by severe hypoxemia with relatively well-preserved lung mechanics (i.e. preserved lung compliance) - a combination that is rarely seen in ARDS [1]. The pathophysiological basis for this disease phenotype is currently unclear. A recent study also noted a significant time-related disease spectrum in COVID-19 patients, with at least two potential "sub-phenotypes": Type L, characterized by low elastance (i.e. high compliance), low ventilation to perfusion ratio, low lung weight and low recruitability by imaging; and a Type H, characterized by high elastance, high right-to-left shunt, high lung weight and high recruitability [2]. It is currently unclear whether a transition from Type L to Type H in some patients is due primarily to the evolution of the COVID-19 pneumonitis or to lung injury caused by injurious mechanical ventilation during the Type L phase. These issues (and others) will require extensive investigation using experimental (animal) models and ultimately clinical trials in human patients. However, clinicians need insights into the possible underlying disease pathophysiology of COVID-19 now, in order to develop appropriate strategies for ventilating patients' lungs. We propose adapting a state-of-the-art computational simulator, which has been developed to investigate mechanical ventilation in ARDS [3-4], to investigate a range of issues that are specific to mechanical ventilation of COVID-19 patients.
COVID-19患者机械通气的初步临床指南建议采用治疗急性呼吸窘迫综合征(ARDS)的标准方法。然而,新出现的临床经验表明,COVID-19肺炎患者可能表现为非典型形式的综合征,其特征(至少在最初)为严重低氧血症,肺力学(即保留的肺顺应性)相对良好-这在ARDS中很少见[1]。这种疾病表型的病理生理学基础目前尚不清楚。最近的一项研究还指出,COVID-19患者存在显著的时间相关性疾病谱,至少有两种潜在的“亚表型”:L型,其特征在于低弹性(即高顺应性)、低通气灌注比、低肺重量和低影像学可复张性; H型,其特征在于高弹性、高右向左分流、高肺重量和高复张性[2]。目前尚不清楚某些患者从L型转变为H型的主要原因是COVID-19肺炎的演变,还是L型阶段的损伤性机械通气导致的肺损伤。这些问题(以及其他问题)将需要使用实验(动物)模型进行广泛的研究,并最终在人类患者中进行临床试验。然而,临床医生现在需要深入了解COVID-19可能的潜在疾病病理生理学,以便制定适当的策略为患者的肺部通气。我们建议采用最先进的计算模拟器,该模拟器已被开发用于研究ARDS中的机械通气[3-4],以研究COVID-19患者机械通气特有的一系列问题。

项目成果

期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Modeling Mechanical Ventilation In Silico-Potential and Pitfalls.
  • DOI:
    10.1055/s-0042-1744446
  • 发表时间:
    2022-04
  • 期刊:
  • 影响因子:
    3.2
  • 作者:
    David M. Hannon;Sonal Mistry;Anup Das;Sina Saffaran;J. Laffey;B. Brook;J. Hardman;D. Bates
  • 通讯作者:
    David M. Hannon;Sonal Mistry;Anup Das;Sina Saffaran;J. Laffey;B. Brook;J. Hardman;D. Bates
High risk of patient self-inflicted lung injury in COVID-19 with frequently encountered spontaneous breathing patterns: a computational modelling study.
  • DOI:
    10.1186/s13613-021-00904-7
  • 发表时间:
    2021-07-13
  • 期刊:
  • 影响因子:
    8.1
  • 作者:
    Weaver L;Das A;Saffaran S;Yehya N;Scott TE;Chikhani M;Laffey JG;Hardman JG;Camporota L;Bates DG
  • 通讯作者:
    Bates DG
Why Reduced Inspiratory Pressure Could Determine Success of Non-Invasive Ventilation in Acute Hypoxic Respiratory Failure.
为什么降低吸气压可以决定急性缺氧性呼吸衰竭无创通气的成功。
In Silico Modeling of Coronavirus Disease 2019 Acute Respiratory Distress Syndrome: Pathophysiologic Insights and Potential Management Implications.
  • DOI:
    10.1097/cce.0000000000000202
  • 发表时间:
    2020-09
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Das A;Saffaran S;Chikhani M;Scott TE;Laviola M;Yehya N;Laffey JG;Hardman JG;Bates DG
  • 通讯作者:
    Bates DG
Additional file 1 of High risk of patient self-inflicted lung injury in COVID-19 with frequently encountered spontaneous breathing patterns: a computational modelling study
附加文件 1:COVID-19 患者自伤肺损伤且经常遇到自主呼吸模式的高风险:计算模型研究
  • DOI:
    10.6084/m9.figshare.14978552
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Weaver L
  • 通讯作者:
    Weaver L
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Declan Bates其他文献

The effects of high-flow nasal oxygen during apnoea in obesity: a computational modelling investigation
  • DOI:
    10.1016/j.tacc.2019.12.067
  • 发表时间:
    2020-02-01
  • 期刊:
  • 影响因子:
  • 作者:
    Husam Alahmadi;Marianna Laviola;Declan Bates;Jonathan Hardman
  • 通讯作者:
    Jonathan Hardman

Declan Bates的其他文献

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{{ truncateString('Declan Bates', 18)}}的其他基金

15 NSFBIO: Rewritable biocomputers in mammalian cells
15 NSFBIO:哺乳动物细胞中的可重写生物计算机
  • 批准号:
    BB/P011926/1
  • 财政年份:
    2017
  • 资助金额:
    $ 44.16万
  • 项目类别:
    Research Grant
Personalised Simulation Technologies for Optimising Treatment in the Intensive Care Unit: Realising Industrial and Medical Applications
用于优化重症监护病房治疗的个性化仿真技术:实现工业和医疗应用
  • 批准号:
    EP/P023444/1
  • 财政年份:
    2017
  • 资助金额:
    $ 44.16万
  • 项目类别:
    Research Grant
Development, validation and application of population-based pulmonary disease models using robustness analysis and ensemble forecasting
使用稳健性分析和集合预测开发、验证和应用基于人群的肺部疾病模型
  • 批准号:
    EP/I036680/2
  • 财政年份:
    2013
  • 资助金额:
    $ 44.16万
  • 项目类别:
    Research Grant
Development, validation and application of population-based pulmonary disease models using robustness analysis and ensemble forecasting
使用稳健性分析和集合预测开发、验证和应用基于人群的肺部疾病模型
  • 批准号:
    EP/I036680/1
  • 财政年份:
    2011
  • 资助金额:
    $ 44.16万
  • 项目类别:
    Research Grant
PREVENTING VENTILATOR-ASSOCIATED LUNG INJURY USING FEEDBACK CONTROL ENGINEERING
利用反馈控制工程预防呼吸机相关的肺损伤
  • 批准号:
    EP/F057016/2
  • 财政年份:
    2010
  • 资助金额:
    $ 44.16万
  • 项目类别:
    Research Grant
Post-transcriptional feedback control of polyamine metabolism in yeast: an integrated modelling and experimental investigation
酵母多胺代谢的转录后反馈控制:综合建模和实验研究
  • 批准号:
    BB/F019602/2
  • 财政年份:
    2010
  • 资助金额:
    $ 44.16万
  • 项目类别:
    Research Grant
IMPROVING THE CLINICAL APPLICABILITY OF PATHOPHYSIOLOGICAL MODELLING OF HYPOXAEMIA USING ROBUSTNESS ANALYSIS
利用稳健性分析提高低氧血症病理生理学模型的临床适用性
  • 批准号:
    EP/E055486/1
  • 财政年份:
    2008
  • 资助金额:
    $ 44.16万
  • 项目类别:
    Research Grant
PREVENTING VENTILATOR-ASSOCIATED LUNG INJURY USING FEEDBACK CONTROL ENGINEERING
利用反馈控制工程预防呼吸机相关的肺损伤
  • 批准号:
    EP/F057016/1
  • 财政年份:
    2008
  • 资助金额:
    $ 44.16万
  • 项目类别:
    Research Grant
Post-transcriptional feedback control of polyamine metabolism in yeast: an integrated modelling and experimental investigation
酵母多胺代谢的转录后反馈控制:综合建模和实验研究
  • 批准号:
    BB/F019602/1
  • 财政年份:
    2008
  • 资助金额:
    $ 44.16万
  • 项目类别:
    Research Grant
ANALYSIS OF BIOCHEMICAL NETWORK MODELS USING ROBUST CONTROL THEORY
使用鲁棒控制理论分析生化网络模型
  • 批准号:
    BB/D015340/1
  • 财政年份:
    2007
  • 资助金额:
    $ 44.16万
  • 项目类别:
    Research Grant

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Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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