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Background: Advances in computational methodologies have enabled processing of large datasets originating from imaging studies. However, most imaging biomarkers suffer from a lack of direct links with underlying biology, as they are only observationally correlated with pathophysiology. Purpose(s): To develop and validate a novel AI-assisted image analysis platform, by applying quantitative radiotranscriptomics that quantifies cytokinedriven vascular inflammation from routine CT angiograms (CTA) performed as part of clinical care in COVID-19. Method(s): We used this platform to train the radiotranscriptomic signature C19-RS, derived from the perivascular space around the aorta and the internal mammary artery in routine chest CTAs, to best describe cytokinedriven vascular inflammation, defined using transcriptomic profiles from RNA sequencing data from human arterial biopsies (A). This signature was validated externally in 358 clinically indicated CT pulmonary angiograms from patients with or without COVID-19 from 3 different geographical regions. Result(s): First, 22 patients who had a CTA before the pandemic underwent repeat CTA <6 months post COVID-19 infection (B). Compared with 22 controls (matched for age, gender, and BMI) C19-RS was increased only in the COVID-19 group (C). Next, C19-RS was calculated in a cohort of 331 patients hospitalised during the pandemic, and was higher in COVID-19 positives (adjusted OR=2.97 [95% CI: 1.43-6.27], p=0.004, D). C19-RS had prognostic value for in-hospital mortality in COVID-19, with HR=3.31 ([95% CI: 1.49-7.33], p=0.003) and 2.58 ([95% CI: 1.10-6.05], p=0.028) in two testing cohorts respectively (E, F), adjusted for clinical factors and biochemical biomarkers of inflammation and myocardial injury. The corrected HR for in-hospital mortality was 8.24 [95% CI: 2.16-31.36], p=0.002 for those who received no treatment with dexamethasone, but only 2.27 [95% CI: 0.69-7.55], p=0.18 in those who received dexamethasone subsequently to the C19-RS based image analysis, suggesting that vascular inflammation may have been a therapeutic target of dexamethasone in COVID-19. Finally, C19-RS was strongly associated (r=0.61, p=0.0003) with a whole blood transcriptional module representing dysregulation of coagulation and platelet aggregation pathways. Conclusion(s): We present the first proof of concept study that combines transcriptomics with radiomics to provide a platform for the development of machine learning derived radiotranscriptomics analysis of routine clinical CT scans for the development of non-invasive imaging biomarkers. Application in COVID-19 produced C19-RS, a marker of cytokine-driven inflammation driving systemic activation of coagulation, that predicts inhospital mortality and identifies people who will have better response to anti-inflammatory treatments, allowing targeted therapy. This AI-assisted image analysis platform may have applications across a wide range of vascular diseases, from infections to autoimmune diseases.
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Background/AimsPatients infected with severe acute respiratory syndrome coronavirus2 (SARS-CoV-2) may develop acute respiratory inflammation, due toan exaggerated immune response and some develop chroniccomplications. Neutrophils play a major role in the pathology ofinflammatory diseases and have been shown to contribute to lung andvascular damage in COVID-19. Our aim was to establish a relationshipbetween neutrophil phenotype and disease severity and to determinewhether neutrophil abnormalities persist in convalescent patients.MethodsPeripheral blood samples were obtained from acute COVID-19patients (n = 74), follow-up (FU) patients discharged following inpatientadmission (n = 56), a median of 87 days after discharge, and healthycontrols (HCs, n = 22). Patients were stratified by disease severitybased on inspired oxygen (FiO2) and admission to intensive care (ICU).Neutrophils were isolated from whole blood by negative selection forphenotyping and functional analysis. PBMC Isolation Tubes were usedto quantify and phenotype low density neutrophils (LDNs) within thePBMC fraction. For quantification of reactive oxygen species (ROS)production, isolated neutrophils were incubated with a ROS reactivedye, DHR-123 and stimulated with PMA. All samples were stained andfixed prior to analysis by flow cytometry.ResultsThere was a marked increase in neutrophils expressing the activationand degranulation markers, CD64 (P < 0.0001) and CD63 (P < 0.0001)and a reduction in neutrophils expressing the maturity markers, CD10(P < 0.0005) and CD101 (P < 0.0005) in patients with acute COVID-19compared to HCs. Increased frequency of neutrophils expressingCD64 (P < 0.005), CD63 (P < 0.01) and expressing decreased CD101(P < 0.0001) were also detected in FU patients compared to HCs.Notably, 42.3 4.4% of neutrophils were CD101lo in FU patients, compared to 29.0 3.7% in acute patients and 9.6 4.1% in HCs.These changes were most apparent in FU patients recovering fromsevere COVID-19 compared to mild or moderate disease. Thefrequency of LDNs in PBMCs from acute patients was significantlyhigher than HCs (P < 0.0001), and correlated with disease severity.Similarly, the frequency of LDNs in FU patients was significantly higherthan in HCs (P < 0.0005). We found a trend towards higher basal ROSproduction in acute and FU patients, but a blunted response to PMAstimulated ROS production in neutrophils from acute patients versusHCs (P < 0.0001). Impaired ROS production persisted in FU patientscompared to HCs (P < 0.01).ConclusionCirculating neutrophils in acute COVID-19 have an altered phenotypeand comprise immature and activated cells. This altered phenotypepersisted in convalescence and may contribute to the persistence ofsymptoms and an increased susceptibility to subsequent infections.Future work will aim to investigate the functional implications of thesefindings.