| EPSRC Reference: |
EP/V041789/1 |
| Title: |
COVID-19: Patient-specific lung models to guide interventions prior to clinical application |
| Principal Investigator: |
Arora, Dr H |
| Other Investigators: |
|
| Researcher Co-Investigators: |
|
| Project Partners: |
|
| Department: |
College of Engineering |
| Organisation: |
Swansea University |
| Scheme: |
Standard Research |
| Starts: |
01 November 2020 |
Ends: |
30 April 2022 |
Value (£): |
259,369
|
| EPSRC Research Topic Classifications: |
| Biomechanics & Rehabilitation |
Med.Instrument.Device& Equip. |
| Medical science & disease |
|
|
| EPSRC Industrial Sector Classifications: |
|
| Related Grants: |
|
| Panel History: |
|
|
Summary on Grant Application Form |
This project will deliver computational models of the lung, to support the development of
patient-specific treatment strategies for the COVID-19 pandemic. The models will i)
automate analysis of the damaged lung, providing additional quantitative data to support
more reliable and rapid conclusions about the presentation of the virus, ii) provide predictions
of how the lung will perform in response to different management strategies (supplemental
oxygen, mechanical ventilation, fluid balance) and potential future treatment strategies
outlined in the RECOVERY/REMAP-CAP trial (e.g. steroids, anti-inflammatories, antibiotics
and plasma from recovered patients); innovatively factoring specific parameters such as
weight, height, age, general fitness and ethnicity - which unquestionably have acute
relevance for recovery.
COVID-19 is heterogenous - affecting everyone differently. Therefore, rapid and
appropriate medical responses to individual cases are critical. Presently patients can remain
on ineffective treatment pathways for 4-6 hours before alternative treatment strategies are
employed. This project reduces waiting times, enabling prioritisation based on quantitative
tools. The models deliver heightened understanding of individual lung mechanics, enabling
clinicians to quickly make better informed treatment decisions to optimise COVID-19 survival
rates.
The model will use patient CT data, patient-specific calibration factors (age, sex, size) and
risk factors (comorbidities, clinical frailty score, exercise tolerance, APACHE-II, ethnicity),
state-of-the-art image analysis and computer simulation, in collaboration with 3DLifePrints
to build human lung models. Patient data will be accessed via ICNARC and the SAIL
databank. The model will mimic lung structure and mechanical function, accounting for the
effect of tissue damage and providing dynamic feedback of lung health.
|
|
Key Findings |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Potential use in non-academic contexts |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Impacts |
|
| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
|
| Date Materialised |
|
|
|
|
Sectors submitted by the Researcher |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
| Project URL: |
|
| Further Information: |
|
| Organisation Website: |
http://www.swan.ac.uk |