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Background Ischemia-reperfusion injury remains a major clinical problem in patients with ST-elevation myocardial infarction (STEMI), leading to myocardial damage despite early reperfusion by primary percutaneous coronary intervention (PPCI). There are no effective therapies to limit ischemia-reperfusion injury, which is caused by multiple pathways activated by rapid tissue reoxygenation and the generation of reactive oxygen species (ROS). FDY-5301 contains sodium iodide, a ubiquitous inorganic halide and elemental reducing agent that can act as a catalytic anti-peroxidant. We tested the feasibility, safety and potential utility of FDY-5301 as a treatment to limit ischemia-reperfusion injury, in patients with first-time STEMI undergoing emergency PPCI. Methods STEMI patients (n = 120, median 62 years) presenting within 12 h of chest pain onset were randomized at 20 PPCI centers, in a double blind Phase 2 clinical trial, to receive FDY-5301 (0.5, 1.0 or 2.0 mg/kg) or placebo prior to reperfusion, to evaluate the feasibility endpoints. Participants underwent continuous ECG monitoring for 14 days after PPCI to address pre-specified cardiac arrhythmia safety end points and cardiac magnetic resonance imaging (MRI) at 72 h and at 3 months to assess exploratory efficacy end points. Results Intravenous FDY-5301 was delivered before re-opening of the infarct-related artery in 97% participants and increased plasma iodide levels ~1000-fold within 2 min. There was no significant increase in the primary safety end point of incidence of cardiac arrhythmias of concern. MRI at 3 months revealed median final infarct sizes in placebo vs. 2.0 mg/kg FDY-5301-treated patients of 14.9% vs. 8.5%, and LV ejection fractions of 53.9% vs. 63.2%, respectively, although the study was not powered to detect statistical significance. In patients receiving FDY-5301, there was a significant reduction in the levels of MPO, MMP2 and NTproBNP after PPCI, but no reduction with placebo. Conclusions Intravenous FDY-5301, delivered immediately prior to PPCI in acute STEMI, is feasible, safe, and shows potential efficacy. A larger trial is justified to test the effects of FDY-5301 on acute ischemia-reperfusion injury and clinical outcomes. Clinical Trial Registration: CT.gov NCT03470441 ; EudraCT 2017-000047-41

Contains fulltext : 286832.pdf (Publisher’s version ) (Open Access) BACKGROUND: Direct evaluation of vascular inflammation in patients with COVID-19 would facilitate more efficient trials of new treatments and identify patients at risk of long-term complications who might respond to treatment. We aimed to develop a novel artificial intelligence (AI)-assisted image analysis platform that quantifies cytokine-driven vascular inflammation from routine CT angiograms, and sought to validate its prognostic value in COVID-19. METHODS: For this prospective outcomes validation study, we developed a radiotranscriptomic platform that uses RNA sequencing data from human internal mammary artery biopsies to develop novel radiomic signatures of vascular inflammation from CT angiography images. We then used this platform to train a radiotranscriptomic signature (C19-RS), derived from the perivascular space around the aorta and the internal mammary artery, to best describe cytokine-driven vascular inflammation. The prognostic value of C19-RS was validated externally in 435 patients (331 from study arm 3 and 104 from study arm 4) admitted to hospital with or without COVID-19, undergoing clinically indicated pulmonary CT angiography, in three UK National Health Service (NHS) trusts (Oxford, Leicester, and Bath). We evaluated the diagnostic and prognostic value of C19-RS for death in hospital due to COVID-19, did sensitivity analyses based on dexamethasone treatment, and investigated the correlation of C19-RS with systemic transcriptomic changes. FINDINGS: Patients with COVID-19 had higher C19-RS than those without (adjusted odds ratio [OR] 2·97 [95% CI 1·43-6·27], p=0·0038), and those infected with the B.1.1.7 (alpha) SARS-CoV-2 variant had higher C19-RS values than those infected with the wild-type SARS-CoV-2 variant (adjusted OR 1·89 [95% CI 1·17-3·20] per SD, p=0·012). C19-RS had prognostic value for in-hospital mortality in COVID-19 in two testing cohorts (high [≥6·99] vs low [

Introduction: Epicardial adipose tissue (EAT) is a visceral fat deposit within the pericardial sac. The automated quantification of EAT volume is possible from routine CCTA scans via deep-learning. The use of automated EAT quantification for the assessment of cardiovascular disease (CVD) risk in addition to standard measures of obesity like BMI has not been fully explored. Purpose: To use deep-learning for automated segmentation of EAT from routine CCTA scans to assess the long-term CVD risk conveyed by EAT. Methods: A deep-learning automated EAT segmentation tool using a 3D Residual-U-Net neural network architecture for 3D volumetric segmentation of CCTA data was created and trained on over 2500 consecutive CCTAs from within the Oxford Risk Factors And Non Invasive Imaging (ORFAN) Study. External validation in 817 patients demonstrated excellent correlation between machine and human expert (CCC = 0.972). The prognostic value of deep-learning derived EAT volume was assessed against 5 years outcomes from the SCOTHEART trial (n=1588), with adjustment for CVD risk factors. An optimal cutoff was selected by identifying the EAT value that maximized the Youden’s J index (sum of sensitivity and specificity) for the three outcomes of interest - high risk was deemed to be EAT ≥ 170.5cm 3 . Results: There were 35 deaths (all-cause mortality), 35 non-fatal myocardial infarctions and 8 non-fatal strokes during the 5 years follow up period. By using multi-variable cox-regression, EAT volume was predictive of all-cause mortality (Figure 1A), non-fatal MI (Figure 1B), and non-fatal stroke (Figure 1C) independently from CVD risk factors. Conclusions: Automatically segmented EAT volume measured using a deep learning network, predicts 5-year all-cause mortality, heart attacks and stroke independently of BMI and clinical risk profile of the patients. This suggests that measures of visceral obesity will be of value in the interpretation of cardiovascular computed tomography.

Background Epicardial adipose tissue (EAT) is a visceral fat deposit within the pericardial sac which surrounds the heart myocardium and coronary arteries. EAT volume has been demonstrated to be strongly associated with the development and prognosis of cardiovascular diseases, but its measurement is subjective and challenging in practice. Purpose To develop a deep-learning approach for automated segmentation of EAT from routine CCTA scans, that could assist clinical interpretation of CCTA. Methods A deep-learning method using a 3D Residual-U-Net neural network architecture for 3D volumetric segmentation of CCTA data was created. The network was trained on a diverse sample of 1900 CCTAs, each manually segmented by a single expert, drawn from the UK sites of the Oxford Risk Factors And Non-invasive imaging (ORFAN) Study. Three iterations of feedback learning were used to fine tune the algorithm for the segmentation of the whole heart within the bounds of the pericardium. In each iteration, the machine analysed sets of 100–250 unannotated CCTAs unseen by the machine which were then corrected by experts. EAT volumes were calculated by automated thresholding of adipose tissue (−190HU through −30HU) from within the bound of the pericardial segment (Figure 1). The network was then applied to 817 unseen CCTAs from US sites of the ORFAN Study. These scans were also segmented for ground truth by two experts blind to all other data. Comparisons between machine vs expert total pericardial volume and EAT volume were made using Lin's concordance correlation coefficient (CCC). The algorithm was then applied externally in 1588 CCTAs from the SCOTHEART trial (UK), and the EAT volume was automatically calculated for each case. Cross-sectional associations between standardised EAT volumes and prevalent AF and CAD were performed. Results Within both the internal (UK ORFAN sites) and external (USA ORFAN sites) validation cohorts correlation between human and machine segmented total pericardium and EAT was excellent, with CCC of 0.97 for both volumes (external validation cohort shown in Figure 2A). Utilising SCOTHEART CCTAs with automatically segmented EAT volumes, a multivariable-adjusted logistic regression model accounting for risk factors of age, sex, BMI, hypertension, diabetes mellitus, valvular disease, and previous heart surgery found that EAT volumes were significantly associated with prevalent AF, with odds ratio (OR) per 1 SD increase of EAT volume of 1.20 (95% CI, 1.06 to 1.44; P=0.03). A similar model for prevalent CAD, adjusted for age, sex, BMI, hypertension, non-HDL cholesterol, diabetes mellitus, and coronary artery calcium score resulted in an OR per 1 SD increase of EAT volume of 1.26 (95% CI, 1.10 to 1.45; P=0.001) (Figure 2B). Conclusion Highly accurate, reproducible, and instantaneous EAT volume quantification is possible utilising deep-learning detection of the whole human heart within the pericardial sac. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): British Heart FoundationNational Institute for Health Research - Oxford University Hospitals Biomedical Research Centre Figure 1Figure 2