Steffen E. Petersen

Bio: Steffen E. Petersen is an academic researcher from Queen Mary University of London. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 58, co-authored 415 publications receiving 16004 citations. Previous affiliations of Steffen E. Petersen include Aarhus University Hospital & University of Mainz.

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Abstract: Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

2020 ESC Guidelines for the management of adult congenital heart disease.

Abstract: a Interní oddělení Nemocnice Přerov, AGEL Středomoravská nemocniční, a.s., a I. interní klinika – kardiologická, Fakultní nemocnice Olomouc b Kardiocentrum Lipsko, Německo c Kardiologické oddělení, Nemocnice Na Homolce, Praha d Centrum pro vrozené srdeční vady v dospělosti, Oddělení kardiochirurgie, Nemocnice Na Homolce, Praha e Centrum pro vrozené srdeční vady, Klinika kardiovaskulární chirurgie, 2. lékařská fakulta Univerzity Karlovy a Fakultní nemocnice v Motole, Praha f Interní kardiologická klinika, Fakultní nemocnice Brno a Centrum komplexní péče o vrozené srdeční vady v dospělosti, Brno

Third universal definition of myocardial infarction

Abstract: ACCF : American College of Cardiology Foundation ACS : acute coronary syndrome AHA : American Heart Association CAD : coronary artery disease CABG : coronary artery bypass grafting CKMB : creatine kinase MB isoform cTn : cardiac troponin CT : computed tomography CV : coefficient of variation ECG : electrocardiogram ESC : European Society of Cardiology FDG : fluorodeoxyglucose h : hour(s) HF : heart failure LBBB : left bundle branch block LV : left ventricle LVH : left ventricular hypertrophy MI : myocardial infarction mIBG : meta-iodo-benzylguanidine min : minute(s) MONICA : Multinational MONItoring of trends and determinants in CArdiovascular disease) MPS : myocardial perfusion scintigraphy MRI : magnetic resonance imaging mV : millivolt(s) ng/L : nanogram(s) per litre Non-Q MI : non-Q wave myocardial infarction NSTEMI : non-ST-elevation myocardial infarction PCI : percutaneous coronary intervention PET : positron emission tomography pg/mL : pictogram(s) per millilitre Q wave MI : Q wave myocardial infarction RBBB : right bundle branch block sec : second(s) SPECT : single photon emission computed tomography STEMI : ST elevation myocardial infarction ST–T : ST-segment –T wave URL : upper reference limit WHF : World Heart Federation WHO : World Health Organization Myocardial infarction (MI) can be recognised by clinical features, including electrocardiographic (ECG) findings, elevated values of biochemical markers (biomarkers) of myocardial necrosis, and by imaging, or may be defined by pathology. It is a major cause of death and disability worldwide. MI may be the first manifestation of coronary artery disease (CAD) or it may occur, repeatedly, in patients with established disease. Information on MI rates can provide useful information regarding the burden of CAD within and across populations, especially if standardized data are collected in a manner that …

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The UK Biobank resource with deep phenotyping and genomic data

Abstract: The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.