Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

An agent-based model of leukocyte transendothelial migration during atherogenesis.

A vast amount of work has been dedicated to the effects of hemodynamics and cytokines on leukocyte adhesion and trans-endothelial migration (TEM) and subsequent accumulation of leukocyte-derived foam cells in the artery wall. However, a comprehensive mechanobiological model to capture these spatiotemporal events and predict the growth and remodeling of an atherosclerotic artery is still lacking. Here, we present a multiscale model of leukocyte TEM and plaque evolution in the left anterior descending (LAD) coronary artery. The approach integrates cellular behaviors via agent-based modeling (ABM) and hemodynamic effects via computational fluid dynamics (CFD). In this computational framework, the ABM implements the diffusion kinetics of key biological proteins, namely Low Density Lipoprotein (LDL), Tissue Necrosis Factor alpha (TNF-α), Interlukin-10 (IL-10) and Interlukin-1 beta (IL-1β), to predict chemotactic driven leukocyte migration into and within the artery wall. The ABM also considers wall shear stress (WSS) dependent leukocyte TEM and compensatory arterial remodeling obeying Glagov's phenomenon. Interestingly, using fully developed steady blood flow does not result in a representative number of leukocyte TEM as compared to pulsatile flow, whereas passing WSS at peak systole of the pulsatile flow waveform does. Moreover, using the model, we have found leukocyte TEM increases monotonically with decreases in luminal volume. At critical plaque shapes the WSS changes rapidly resulting in sudden increases in leukocyte TEM suggesting lumen volumes that will give rise to rapid plaque growth rates if left untreated. Overall this multi-scale and multi-physics approach appropriately captures and integrates the spatiotemporal events occurring at the cellular level in order to predict leukocyte transmigration and plaque evolution

Initial ABM parameters.

<p>Initial ABM parameters.</p

ABM-CFD predictions corroborate an experimental porcine model of atherogenesis.

<p>‘x’ represents mean plaque area from individual pigs, fed high cholesterol diet. ‘.‘represents predicted plaque area as a result of a 30% increase in LDLs.</p

List of rules used in the ABM.

<p>List of rules used in the ABM.</p

Eccentric plaque growth and remodeling prediction.

<p>Longitudinal (left) and corresponding transverse (right) views of an evolving artery where ECs, ACs and leukocytes are represented by green, red and yellow respectively. A) Initially the artery is impregnated with 15 leukocytes. B) At 6 months the plaque area is 40% of the lumen area and will start growing inside lumen according to Glagov’s phenomenon. C) At 7 months the plaque has grown inward and outward, changing the luminal geometry.</p

Representative cardiac cycle to pass spatial WSS profile into ABM.

<p>(A) Coronary blood flow rate profile used in CFD. Number of leukocytes undergoing TEM in 1 hour using instantaneous pulsatile flow over a spherical plaque with radius (B) 1.0 mm or (C) 1.5 mm. The average number leukocyte TEM per hour, over one cardiac cycle, is 16 for (B) and 26 for (C). The WSS profile at peak flow during systole corresponds to these average TEM values, as indicated by the dashed line and red ‘x’ marks.</p

Different types of leukocytes present in the plaque as it evolves.

<p>Before the plaque reduces the caliber of the lumen, as indicated by the vertical dashed red line, TEM is only due to endothelial activation by cytokines. Initially neutrophils constitute the majority of the plaque volume, followed by monocytes and monocyte-derived cells and then neutrophils again. When the plaque starts growing inside the lumen (vertical dashed line) leukocyte TEM is largely influenced by blood flow. With time the severity of stenosis increases and so does the region of low WSS. Therefore, the rate of leukocyte TEM is greater for all cells. Among these cells, the concentration of neutrophils (62%) in blood is higher than monocytes and lymphocytes (5.3% and 30% respectively). Also neutrophils adhere on the EC surface with WSS < 1.2 Pa whereas the monocytes and lymphocytes adhere with WSS < 1 Pa and < 0.4 Pa respectively. Thus there is a rapid increase of neutrophils immediately after 6 months whereas the rate for lymphocyte increases after several days when the plaque is bigger and WSS < 0.4 Pa.</p

When the degree of stenosis was below 8%, the level of leukocyte TEM was constant over an average change in lumen volume of 107±5 patches, followed by a rapid increase in TEM over the next 28±17 patches.

<p>(A) Scatter plot displaying leukocyte TEM and severity of stenosis as a function of luminal volume (patches). (B) Bar plot indicating the change in lumen volume after which a significant increase in leukocyte TEM was observed. (Mean ± SD, *p < 0.05). (C) Longitudinal cross sections of the ABM at ‘a’, ‘b’, and ‘c’ points from 5A, illustrating where the initial inward growth occurred. ACs, ECs, and leukocytes in the plaque are indicated in red, green and yellow, respectively. Black represents the new ECs added in the lumen. Inlet flow is at the bottom of each subfigure. (D) Subplot showing the number of patches (from ABM) with WSS < 1.2 Pa at specific plaque shapes (i.e., lumen volumes) corresponding to ‘a’, ‘b’, and ‘c’ points from 5A. Overlaid color contour plots of the WSS (from CFD) over the plaque at each point. The number of patches of low WSS (blue region) are almost constant (13 and 15 respectively) corresponding to nearly constant TEM. Then, the number of patches having low WSS increases (83) as does TEM. Inlet flow is at the left of each subfigure in (D).</p

Schematic illustrating the spatial distribution of each type of cell (i.e. agent) in the ABM.

<p>The bottom right schematic represents a luminal and artery wall patch where N is the number of leukocyte agents residing in this luminal patch. Among all leukocytes (N), M is the number that adheres to the endothelial cell surface area (A). V is the patch volume and h is 100μm or the distance between the patch centroids. All these parameters are used to find leukocyte adhesion probability (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005523#pcbi.1005523.e018" target="_blank">Eq 2</a>).</p