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Background The combined therapy with venoarterial extracorporeal membrane oxygenation and Impella (ECPella) has been shown to improve survival in acute cardiogenic shock (CS) in adult patient. Only three paediatric cases have been reported in a multicentre study. Case summary We present our case, the first described to our knowledge in the UK, of a 15-year-old adolescent of Afro-Caribbean descent, weight 75 kg, who received extracorporeal cardiopulmonary resuscitation (E-CPR) and ECPella implantation. The patient suffered a sudden cardiac arrest at home; his mother called for an ambulance that arrived within 10 min and commenced life support with a LUCAS device. He received three doses of adrenaline and three desynchronized shocks for an underlying rhythm of ventricular fibrillation (VF) after which return of spontaneous circulation was achieved. He was then transferred to his local hospital where he had another VF arrest with successful cardioversion and was then transferred to our institution where on arrival he had another VF arrest and received E-CPR and ECPella implantation under the institutional adult shock programme within 3 h of in-hospital cardiac arrest. Following weaning from ECPella, the patient underwent cardiac and brain magnetic resonance imaging and serial echocardiograms with complete recovery of ventricular function. After implantation of cardiac defibrillator, he was discharged home without neurological sequelae. He remains asymptomatic from a cardiac perspective, with a normal cardiac examination and with no neurological sequelae at 2-year follow-up. Discussion This is the first case description of ECPella use in a child in the UK and highlighted the importance of timely institution of E-CPR on survival benefit in fatal CS. The outcome success of post-resuscitation ECPella strategy in this adolescent was through collaborative interprofessional engagement of multiple supra-specialists within acute cardiology and critical care across paediatric and adult services and alignment with the institutional adult shock programme. [ABSTRACT FROM AUTHOR]

Background: It remains unknown how the patterns of change of social isolation and loneliness are associated with the onset of cardiovascular disease (CVD) and mortality. We aimed to investigate the longitudinal association of changes in social isolation and loneliness with incident CVD, all-cause mortality, CVD mortality and subsequent cardiac function. Methods: This prospective cohort study included 18,258 participants aged 38–73 years who participated in visit 0 (2006–2010) and visit 1 (2012–2013) using UK Biobank (mean age 57.1, standard deviation [SD] 7.4; 48.7% males). Social isolation or loneliness was categorized into four patterns: never, transient, incident, and persistent. Incident CVD, all-cause and CVD mortality were ascertained through linkage data. Cardiac function was assessed by cardiovascular magnetic resonance imaging in a subsample (N = 5188; visit 2, since 2014). Results: Over a median follow-up of 8.3 (interquartile range [IQR] 8.1–8.6) years, compared with never social isolation, persistent social isolation was associated with the higher risk of incident CVD (hazard ratio [HR] 1.17, 95% confidence interval [CI] 1.03–1.33), all-cause (1.42, 1.12–1.81) and CVD (1.53, 1.05–2.23) mortality. Likewise, persistent loneliness was strongly associated with the greater risk of incident CVD (1.13, 1.00–1.27), all-cause (1.28, 1.02–1.61) and CVD mortality (1.52, 1.06–2.18). Conclusions: Persistent social isolation and loneliness posed a substantially higher risk for incident CVD, all-cause and CVD mortality, and cardiac dysfunction than other patterns. Persistent social isolation and loneliness, along with an increasing cumulative score, are associated with lower cardiac function. [ABSTRACT FROM AUTHOR]

Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk. In this study, a deep learning-based model of left atrial size in UK Biobank enabled genome-wide association studies in 35,049 healthy participants. Several lines of evidence, including the PITX2 locus, linked left atrial dysfunction to atrial fibrillation risk. [ABSTRACT FROM AUTHOR]

Models that utilize self-attention mechanisms, including but not limited to Vision Transformers (ViTs), have shown promising performance in visual tasks like semantic segmentation. This is attributed to their capacity to capture global features of images, enabling them to learn more comprehensive representations. However, transformer-based models typically demand a considerable amount of training data to achieve satisfactory performance, while being deficient in the ability to efficiently extract local image features. As a result, these models may not be as effective in some computer vision tasks that involve small-scale datasets, like medical image segmentation. To address these issues, this paper proposes a dual-stream encoding-based transformer dubbed as Dual-stream Transformer (DS-Former). The dual-stream module in DS-Former can simultaneously acquire local and global features in the image and construct relation between the two kinds of features via self-attention. Compared with the simple splicing or serial connection, the dual-stream module can extract more comprehensive and hierarchical feature information from the fusion interaction of the two features. Our method is evaluated on the UK Biobank (UKBB) cardiac magnetic resonance imaging (CMR) dataset and The Beyond the Cranial Vault (BTCV) abdominal challenge dataset. The experimental results indicate that our DS-Former outperforms other state-of-the-art approaches on both datasets, indicating its potential for medical images semantic segmentation. [ABSTRACT FROM AUTHOR]

Left ventricular regional wall thickness (LVRWT) is an independent predictor of morbidity and mortality in cardiovascular diseases (CVDs). To identify specific genetic influences on individual LVRWT, we established a novel deep learning algorithm to calculate 12 LVRWTs accurately in 42,194 individuals from the UK Biobank with cardiac magnetic resonance (CMR) imaging. Genome-wide association studies of CMR-derived 12 LVRWTs identified 72 significant genetic loci associated with at least one LVRWT phenotype (P < 5 × 10−8), which were revealed to actively participate in heart development and contraction pathways. Significant causal relationships were observed between the LVRWT traits and hypertrophic cardiomyopathy (HCM) using genetic correlation and Mendelian randomization analyses (P < 0.01). The polygenic risk score of inferoseptal LVRWT at end systole exhibited a notable association with incident HCM, facilitating the identification of high-risk individuals. The findings yield insights into the genetic determinants of LVRWT phenotypes and shed light on the biological basis for HCM etiology. Changes of left ventricular structure are used to predict morbidity and mortality in cardiovascular diseases. Here the authors conducted a study using advanced deep learning technology to analyze left ventricular regional wall thickness (LVRWT) in a large population, identifying 72 significant genetic loci linked to LVRWT traits. [ABSTRACT FROM AUTHOR]

Human activities have raised the atmosphere's carbon dioxide (CO2) content by 50% in less than 200 years and by 10% in the last 15 years. Climate change is a great threat and presents a unique opportunity to protect cardiovascular health in the next decades. CO2 equivalent emission is the most convenient unit for measuring the greenhouse gas footprint corresponding to ecological cost. Medical imaging contributes significantly to the CO2 emissions responsible for climate change, yet current medical guidelines ignore the carbon cost. Among the common cardiac imaging techniques, CO2 emissions are lowest for transthoracic echocardiography (0.5–2 kg per exam), increase 10-fold for cardiac computed tomography angiography, and 100-fold for cardiac magnetic resonance. A conservative estimate of 10 billion medical examinations per year worldwide implies that medical imaging accounts for approximately 1% of the overall carbon footprint. In 2016, CO2 emissions from magnetic resonance imaging and computed tomography, calculated in 120 countries, accounted for 0.77% of global emissions. A significant portion of global greenhouse gas emissions is attributed to health care, which ranges from 4% in the United Kingdom to 10% in the United States. Assessment of carbon cost should be a part of the cost-benefit balance in medical imaging. [ABSTRACT FROM AUTHOR]

Aortic dimensions and distensibility are key risk factors for aortic aneurysms and dissections, as well as for other cardiovascular and cerebrovascular diseases. We present genome-wide associations of ascending and descending aortic distensibility and area derived from cardiac magnetic resonance imaging (MRI) data of up to 32,590 Caucasian individuals in UK Biobank. We identify 102 loci (including 27 novel associations) tagging genes related to cardiovascular development, extracellular matrix production, smooth muscle cell contraction and heritable aortic diseases. Functional analyses highlight four signalling pathways associated with aortic distensibility (TGF-β, IGF, VEGF and PDGF). We identify distinct sex-specific associations with aortic traits. We develop co-expression networks associated with aortic traits and apply phenome-wide Mendelian randomization (MR-PheWAS), generating evidence for a causal role for aortic distensibility in development of aortic aneurysms. Multivariable MR suggests a causal relationship between aortic distensibility and cerebral white matter hyperintensities, mechanistically linking aortic traits and brain small vessel disease. Aortic distensibility is a risk factor for multiple cardiovascular events, but the genetic etiology is not well understood. Here, the authors identify genetic variants linked to aortic distensibility, highlighting mechanistic pathways and causal relationships between distensibility and both aortic aneurysms and brain small vessel disease. [ABSTRACT FROM AUTHOR]

Dilated cardiomyopathy (DCM) is an important cause of heart failure and the leading indication for heart transplantation. Many rare genetic variants have been associated with DCM, but common variant studies of the disease have yielded few associated loci. As structural changes in the heart are a defining feature of DCM, we report a genome-wide association study of cardiac magnetic resonance imaging (MRI)-derived left ventricular measurements in 36,041 UK Biobank participants, with replication in 2184 participants from the Multi-Ethnic Study of Atherosclerosis. We identify 45 previously unreported loci associated with cardiac structure and function, many near well-established genes for Mendelian cardiomyopathies. A polygenic score of MRI-derived left ventricular end systolic volume strongly associates with incident DCM in the general population. Even among carriers of TTN truncating mutations, this polygenic score influences the size and function of the human heart. These results further implicate common genetic polymorphisms in the pathogenesis of DCM. Structural changes to the left ventricle are characteristic of dilated cardiomyopathy (DCM), a disease for which many rare genetic variants are known. Here, Pirruccello et al. report GWAS of seven cardiac MRI measurements in the left ventricle and describe shared loci and polygenic association with DCM. [ABSTRACT FROM AUTHOR]