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

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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.