COVID-19: Patient-specific lung models to guide interventions prior to clinical application

COVID-19：患者特异性肺模型，用于指导临床应用前的干预措施

• 批准号: EP/V041789/1
• 负责人: Hari Arora
• 金额: $ 33.05万
• 依托单位: Swansea University
• 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
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV041789%2F1
• 关键词: COVID; 19; Patient; specific; lung

This project will deliver computational models of the lung, to support the development ofpatient-specific treatment strategies for the COVID-19 pandemic. The models will i)automate analysis of the damaged lung, providing additional quantitative data to supportmore reliable and rapid conclusions about the presentation of the virus, ii) provide predictionsof how the lung will perform in response to different management strategies (supplementaloxygen, mechanical ventilation, fluid balance) and potential future treatment strategiesoutlined in the RECOVERY/REMAP-CAP trial (e.g. steroids, anti-inflammatories, antibioticsand plasma from recovered patients); innovatively factoring specific parameters such asweight, height, age, general fitness and ethnicity - which unquestionably have acuterelevance for recovery.COVID-19 is heterogenous - affecting everyone differently. Therefore, rapid andappropriate medical responses to individual cases are critical. Presently patients can remainon ineffective treatment pathways for 4-6 hours before alternative treatment strategies areemployed. This project reduces waiting times, enabling prioritisation based on quantitativetools. The models deliver heightened understanding of individual lung mechanics, enablingclinicians to quickly make better informed treatment decisions to optimise COVID-19 survivalrates.The model will use patient CT data, patient-specific calibration factors (age, sex, size) andrisk factors (comorbidities, clinical frailty score, exercise tolerance, APACHE-II, ethnicity),state-of-the-art image analysis and computer simulation, in collaboration with 3DLifePrintsto build human lung models. Patient data will be accessed via ICNARC and the SAILdatabank. The model will mimic lung structure and mechanical function, accounting for theeffect of tissue damage and providing dynamic feedback of lung health.

该项目将提供肺部的计算模型，以支持针对COVID-19大流行的患者特定治疗策略的开发。这些模型将i）自动分析受损的肺，提供额外的定量数据，以支持有关病毒表现的更可靠和快速的结论，ii）预测肺将如何响应不同的管理策略RECOVERY/REMAP-CAP试验中概述的（持续供氧、机械通气、液体平衡）和潜在的未来治疗策略（例如类固醇、抗炎药、抗肿瘤药和康复患者的血浆）;创新性地将体重、身高、年龄、总体健康状况和种族等特定参数考虑在内--这些参数无疑与康复有着密切的关系。COVID-19是异质性的--对每个人的影响都不同。因此，对个别病例作出迅速和适当的医疗反应至关重要。目前，患者在采用替代治疗策略之前可以在无效的治疗途径上停留4-6小时。该项目减少了等待时间，实现了基于量化工具的优先级排序。这些模型可以提高对个体肺力学的理解，使临床医生能够快速做出更明智的治疗决策，以优化COVID-19生存率。该模型将使用患者CT数据、患者特定校准因子（年龄、性别、体型）（合并症、临床虚弱评分、运动耐量、APACHE-II、种族）、最先进的图像分析和计算机模拟，in collaboration合作with 3DLifePrints打印to build建立human人lung肺models模型.将通过ICNARC和SAIL数据库访问患者数据。该模型将模拟肺的结构和机械功能，考虑组织损伤的影响，并提供肺健康的动态反馈。