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

该项目将提供肺的计算模型，以支持针对新冠肺炎大流行制定针对患者的治疗策略。这些模型将i)自动分析受损的肺，提供更多的定量数据来支持关于病毒呈现的更可靠和更快速的结论；ii)提供对肺将如何响应不同的管理策略(补充氧气、机械通风、液体平衡)和恢复/REMAP-CAP试验中概述的潜在未来治疗策略(例如类固醇、抗炎药、抗生素和康复患者的血浆)的预测；创新地考虑特定参数，如体重、身高、年龄、一般健康状况和种族-这些无疑对恢复具有重要意义。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，在采用替代治疗策略之前，患者可以在无效的治疗路径上停留4-6个小时。该项目减少了等待时间，能够根据量化工具确定优先顺序。这些模型提高了对个体肺力学的理解，使临床医生能够快速做出更知情的治疗决策，以优化新冠肺炎的存活率。该模型将使用患者的CT数据、患者特定的校准因素(年龄、性别、大小)和风险因素(合并症、临床脆弱程度评分、运动耐量、APACHE-II、种族)、最先进的图像分析和计算机模拟，与3D LifePrints合作建立人类肺模型。患者数据将通过ICNARC和SAIL数据库访问。该模型将模拟肺结构和机械功能，考虑组织损伤的影响，并提供肺健康的动态反馈。

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.