Deaths from COVID-19 in healthcare workers in Italy - what can we learn?

This letter examines healthcare worker deaths by category and medical speciality during the COVID-19 emergency in Italy, and underlines factors that may have contributed to the elevated number of fatalities among healthcare personnel. These data are now available because Italy was the first western country to be severely affected. These are matters for urgent discussion as development goes forward

A deep learning pipeline to simulate fluorodeoxyglucose (FDG) uptake in head and neck cancers using non-contrast CT images without the administration of radioactive tracer

Objectives: Positron emission tomography (PET) imaging is a costly tracer-based imaging modality used to visualise abnormal metabolic activity for the management of malignancies. The objective of this study is to demonstrate that non-contrast CTs alone can be used to differentiate regions with different Fluorodeoxyglucose (FDG) uptake and simulate PET images to guide clinical management. Methods: Paired FDG-PET and CT images (n = 298 patients) with diagnosed head and neck squamous cell carcinoma (HNSCC) were obtained from The cancer imaging archive. Random forest (RF) classification of CT-derived radiomic features was used to differentiate metabolically active (tumour) and inactive tissues (ex. thyroid tissue). Subsequently, a deep learning generative adversarial network (GAN) was trained for this CT to PET transformation task without tracer injection. The simulated PET images were evaluated for technical accuracy (PERCIST v.1 criteria) and their ability to predict clinical outcome [(1) locoregional recurrence, (2) distant metastasis and (3) patient survival]. Results: From 298 patients, 683 hot spots of elevated FDG uptake (elevated SUV, 6.03 ± 1.71) were identified. RF models of intensity-based CT-derived radiomic features were able to differentiate regions of negligible, low and elevated FDG uptake within and surrounding the tumour. Using the GAN-simulated PET image alone, we were able to predict clinical outcome to the same accuracy as that achieved using FDG-PET images. Conclusion: This pipeline demonstrates a deep learning methodology to simulate PET images from CT images in HNSCC without the use of radioactive tracer. The same pipeline can be applied to other pathologies that require PET imaging

A deep learning approach to visualize aortic aneurysm morphology without the use of intravenous contrast agents

Background: Intravenous contrast agents are routinely used in CT imaging to enable the visualization of intravascular pathology, such as with abdominal aortic aneurysms. However, the injection is contraindicated in patients with iodine allergy and is associated with renal complications. Objectives: In this study, we investigate if the raw data acquired from a noncontrast CT image contains sufficient information to differentiate blood and other soft tissue components. A deep learning pipeline underpinned by generative adversarial networks was developed to simulate contrast enhanced CTA images using noncontrast CTs. Methods and Results: Two generative models (cycle- and conditional) are trained with paired noncontrast and contrast enhanced CTs from seventy-five patients (total of 11,243 pairs of images) with abdominal aortic aneurysms in a 3-fold cross-validation approach with a training/testing split of 50:25 patients. Subsequently, models were evaluated on an independent validation cohort of 200 patients (total of 29,468 pairs of images). Both deep learning generative models are able to perform this image transformation task with the Cycle-generative adversarial network (GAN) model outperforming the Conditional-GAN model as measured by aneurysm lumen segmentation accuracy (Cycle-GAN: 86.1% ± 12.2% vs Con-GAN: 85.7% ± 10.4%) and thrombus spatial morphology classification accuracy (Cycle-GAN: 93.5% vs Con-GAN: 85.7%). Conclusion: This pipeline implements deep learning methods to generate CTAs from noncontrast images, without the need of contrast injection, that bear strong concordance to the ground truth and enable the assessment of important clinical metrics. Our pipeline is poised to disrupt clinical pathways requiring intravenous contrast

Refining the Enrolment Process in Emergency Medicine Research

Research in the emergency setting involving patients with acute clinical conditions is needed if there are to be advances in diagnosis and treatment. But research in these areas poses ethical and practical challenges. One of these is the general inability to obtain informed consent due to the patient’s lack of mental capacity and insufficient time to contact legal representatives. Regulatory frameworks which allow this research to proceed with a consent ‘waiver’, provided patients lack mental capacity, miss important ethical subtleties. One of these is the varying nature of mental capacity among emergency medicine patients. Not only is their capacity variable and often unclear, but some patients are also likely to be able to engage with the researcher and the context to varying degrees. In this paper we describe the key elements of a novel enrolment process for emergency medicine research that refines the consent waiver and fully engages with the ethical rationale for consent and, in this context, its waiver. The process is verbal but independently documented during the ‘emergent’ stages of the research. It provides appropriate engagement with the patient, is context-sensitive and better addresses ethical subtleties. In line with regulation, full written consent for on-going participation in the research is obtained once the emergency is passed

A histopathological classification scheme for abdominal aortic aneurysm disease

Objective: Two consensus histopathological classifications for thoracic aortic aneurysms (TAAs) and inflammatory aortic diseases have been issued to facilitate clinical decision-making and inter-study comparison. However, these consensus classifications do not specifically encompass abdominal aortic aneurysms (AAAs). Given its high prevalence and the existing profound pathophysiologic knowledge gaps, extension of the consensus classification scheme to AAAs would be highly instrumental. The aim of this study was to test the applicability of, and if necessary to adapt, the issued consensus classification schemes for AAAs. Methods: Seventy-two AAA anterolateral wall samples were collected during elective and emergency open aneurysm repair performed between 2002 and 2013. Histologic analysis (hematoxylin and eosin and Movat Pentachrome) and (semi-quantitative and qualitative) grading were performed in order to map the histological aspects of AAA. Immuno- histochemistry was performed for visualization of aspects of the adaptive and innate immune system, and for a more detailed analysis of atherosclerotic lesions in AAA. Results: Because the existing consensus classification schemes do not adequately capture the aspects of AAA disease, an AAA-specific 11-point histopathological consensus classification was devised. Systematic application of this classification indicated several universal features for AAA (eg, [almost] complete elastolysis), but considerable variation for other aspects (eg, inflammation and atherosclerotic lesions). Conclusions: This first multiparameter histopathological AAA consensus classification illustrates the sharp histological contrasts between thoracic and abdominal aneurysms. The value of the proposed scoring system for AAA disease is illustrated by its discriminatory capacity to identify samples from patients with a nonclassical (genetic) variant of AAA disease. (JVSeVascular Science 2021;2:260-73.) Clinical Relevance: The pathophysiology of abdominal aortic aneurysm (AAA) disease remains an enigma. Histological evaluation is critical for appreciation of the tissue remodeling and spatial aspects of AAA disease. Histopathological classification schemes have been devised for aortic pathology. Unfortunately, these schemes do not specifically address the most common aortic pathology, AAA disease. In order to facilitate interstudy comparisons and pathophysiologic understanding of AAA disease, we here present a multiparameter consensus histopathological AAA classification scheme. The scheme clearly discriminates AAA disease from thoracic aortic aneurysm disease. Systematic implementation of this AAA classification system indicates a substantial biological variation for AAA disease, and stresses the need for adequate group sizes in order to adequately cover the natural variability. Examples illustrating the diagnostic value of the classification system are provided

Metabolomic Profiling in Acute ST-Segment-Elevation Myocardial Infarction Identifies Succinate as an Early Marker of Human Ischemia-Reperfusion Injury.

BACKGROUND: Ischemia-reperfusion injury following ST-segment-elevation myocardial infarction (STEMI) is a leading determinant of clinical outcome. In experimental models of myocardial ischemia, succinate accumulation leading to mitochondrial dysfunction is a major cause of ischemia-reperfusion injury; however, the potential importance and specificity of myocardial succinate accumulation in human STEMI is unknown. We sought to identify the metabolites released from the heart in patients undergoing primary percutaneous coronary intervention for emergency treatment of STEMI. METHODS AND RESULTS: Blood samples were obtained from the coronary artery, coronary sinus, and peripheral vein in patients undergoing primary percutaneous coronary intervention for acute STEMI and in control patients undergoing nonemergency coronary angiography or percutaneous coronary intervention for stable angina or non-STEMI. Plasma metabolites were analyzed by targeted liquid chromatography and mass spectrometry. Metabolite levels for coronary artery, coronary sinus, and peripheral vein were compared to derive cardiac and systemic release ratios. In STEMI patients, cardiac magnetic resonance imaging was performed 2 days and 6 months after primary percutaneous coronary intervention to quantify acute myocardial edema and final infarct size, respectively. In total, 115 patients undergoing acute STEMI and 26 control patients were included. Succinate was the only metabolite significantly increased in coronary sinus blood compared with venous blood in STEMI patients, indicating cardiac release of succinate. STEMI patients had higher succinate concentrations in arterial, coronary sinus, and peripheral venous blood than patients with non-STEMI or stable angina. Furthermore, cardiac succinate release in STEMI correlated with the extent of acute myocardial injury, quantified by cardiac magnetic resonance imaging. CONCLUSION: Succinate release by the myocardium correlates with the extent of ischemia