Title: HypErpolarised Xenon Magnetic Resonance PuLmonary Imaging in PAtIeNts with Long-COVID (EXPLAIN)
Funder: Department of Health and Social Care

Abstract: We will examine the hypothesis that in a proportion of non-hospitalised patients with Post-COVID-19 syndrome (NHLC) and a normal CT scan, persistent exertional dyspnoea results from damage to the alveolar membrane/pulmonary vasculature identifiable by hyperpolarised Xenon MRI, including a perfusion MRI scan (HPX-pMRI). Using this technique we have demonstrated the presence of persistent abnormalities in dyspneoic post-hospitalised COVID-19 patients and in two NHLC patients. The work programme consists of: Recruiting 200 NHLC patients from dedicated Post-COVID clinics in Oxford/Sheffield with documented exertional dyspnoea without an identifiable cause on CT Recruiting 50 NHLC patients from the Post-COVID clinic who do not have symptoms of exertional dyspnoea. Recruiting 50 non-hospitalised patients with prior proven SARS-CoV-2 infection without symptoms of Long-COVID. Performing low dose CT (if not already performed) and HPX-pMRI on these patients Performing Lung function and exercise tests and validated questionnaires in study participants Taking a blood sample for future analysis A subset of each cohort will have combined HPX-pMRI and cardiac MRI We will: Test the hypothesis that exertional dyspnoea in NHLC is contributed by alveolar diffusion/perfusion defects detected on HPX-pMRI imaging, and assess the duration of the abnormalities detected Characterise and quantify the alveolar perfusion/diffusion abnormality caused by SARS-CoV-2 infection using HPX-pMRI Determine the specificity of the findings by comparing the imaging data in the three study cohorts, and to two already ongoing HPX-pMRI studies using the same scan protocols - 50 post-hospitalised patients in the C-MORE-POST study and to the NIHR funded Post COVID UK-ILD study. Determine whether a positive/negative HPX-pMRI is associated with an abnormal cardiac MRI Dependent upon whether HPX-pMRI differentiates these groups: develop a CT AI algorithm to improve detection of abnormalities on CT by mapping CT to the ground truth HPX-pMRI data, and perform immune profiling on the stored blood samples.

Lay abstract: We wish to understand why some Long-COVID sufferers struggle with breathlessness on exertion and have a reduced ability to exercise. To do this, we will use MRI scanning and a special gas (hyperpolarised xenon) which is breathed in during the scan. The xenon gas is harmless in the quantity we use. This technique shows the movement of xenon within the lungs and moving out of the lungs into the bloodstream, similar to how oxygen is absorbed. The scans take about 15 minutes. In patients hospitalised with COVID-19, we found that scans several months after discharge showed evidence of persisting lung damage, even when other tests were normal. Importantly, on follow-up scanning, some have remained abnormal. We will use the same scan technique in Long-COVID sufferers not admitted to hospital, who have ongoing breathlessness on exertion to see if they have lung damage. If lung abnormalities are found, we will assess their severity and whether they improve over time. To do this, we will compare scans in the different groups of participants enabling us to see if their breathing problems are related to the lung damage. We will also include an MRI scan of the heart in some of the trial participants to see if the heart has also been damaged. If the Xenon MRI scans separate patients with and without lung disease, we will then analyse the CT scans using artificial intelligence and the blood samples to see if we can find associated abnormalities. We aim to provide a much-needed explanation to sufferers of Long-COVID struggling with breathlessness. Learning more about the nature of damage within the lungs through xenon MRI may help with the future development of treatments, and provide a reliable way of measuring lung damage changes over time.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: INFECTION