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Electrocardiographic imaging (ECGI) is a non-invasive technique that combines patient-specific cardiac anatomy with numerous body-surface potentials to gain insight into the in vivo electrical epicardial activity across the whole heart. This review summarises some of the latest hardware developments for ECGI data acquisition as well as innovative software solutions to address the inverse problem of electrocardiography. It delves into some of the successful clinical applications of ECGI while highlighting the hurdles that still stand in the way of its more widespread roll-out to healthcare. As well as serving as a powerful tool for electrophysiological research, ECGI promises to play an increasingly valuable role in the personalised diagnosis and management of cardiac arrhythmias.

Cardiovascular magnetic resonance (CMR) enables assessment and quantification of morphological and functional parameters of the heart, including chamber size and function, diameters of the aorta and pulmonary arteries, flow and myocardial relaxation times. Knowledge of reference ranges (“normal values”) for quantitative CMR is crucial to interpretation of results and to distinguish normal from disease. Compared to the previous version of this review published in 2015, we present updated and expanded reference values for morphological and functional CMR parameters of the cardiovascular system based on the peer-reviewed literature and current CMR techniques. Further, databases and references for deep learning methods are included.

Cardiovascular magnetic resonance (CMR) is a key imaging technique for cardiac phenotyping with a major clinical role. It can assess advanced aspects of cardiac structure and function, scar burden and other myocardial tissue characteristics but there is new information that can now be derived. This can fill many of the gaps in our knowledge with the potential to change thinking, disease classifications and definitions as well as patient care. Established techniques such as the late gadolinium enhancement technique are now embedded in clinical care. New techniques are coming through. Myocardial tissue characterisation techniques, particularly myocardial mapping can precisely measure tissue magnetisation—T1, T2, T2* and also the extracellular volume. These change in disease. Key biological pathways are now open for scrutiny including focal fibrosis (scar) and diffuse fibrosis, inflammation, metabolism and infiltration. Other new areas to engage in where major insights are growing include detailed assessments of myocardial mechanics and performance, spectroscopy and hyperpolarised CMR. In spite of the advances, challenges remain, particularly surrounding utilisation, technical development to improve accuracy, reproducibility and deliverability, and the role of multidisciplinary research to understand the detailed pathological basis of the MR signal changes. Collectively, these new developments are galvanising CMR uptake and having a major translational impact on healthcare globally and it is steadily becoming key imaging tool.

Injured patients are at risk of developing acute kidney injury (AKI), which is associated with increased morbidity and mortality. The aim of this study is to describe the incidence, timing, and severity of AKI in a large trauma population, identify risk factors for AKI, and report mortality outcomes. A prospective observational study of injured adults, who met local criteria for trauma team activation, and were admitted to a UK Major Trauma Centre. AKI was defined by the Kidney Disease Improving Global Outcomes (KDIGO) criteria. Multivariable logistic regression and Cox proportional hazard modelling was used to analyse parameters associated with AKI and mortality. Of the 1410 patients enrolled in the study, 178 (12.6%) developed AKI. Age; injury severity score (ISS); admission systolic blood pressure, lactate and serum creatinine; units of Packed Red Blood Cells transfused in first 24 hours and administration of nephrotoxic therapy were identified as independent risk factors for the development of AKI. Patients that developed AKI had significantly higher mortality than those with normal renal function (47/178 [26.4%] versus 128/1232 [10.4%]; OR 3.09 [2.12 to 4.53]; p<0.0001). After adjusting for other clinical prognostic factors, AKI was an independent risk factor for mortality. AKI is a frequent complication following trauma and is associated with prolonged hospital length of stay and increased mortality. Future research is needed to improve our ability to rapidly identify those at risk of AKI, and develop resuscitation strategies that preserve renal function in trauma patients.

Lamins A and C are intermediate filament nuclear envelope proteins encoded by the LMNA gene. Mutations in LMNA cause autosomal dominant severe heart disease, accounting for 10% of dilated cardiomyopathy (DCM). Characterised by progressive conduction system disease, arrhythmia and systolic impairment, lamin A/C heart disease is more malignant than other common DCMs due to high event rates even when the left ventricular impairment is mild. It has several phenotypic mimics, but overall it is likely to be an under-recognised cause of DCM. In certain clinical scenarios, particularly familial DCM with early conduction disease, the pretest probability of finding an LMNA mutation may be quite high.Recognising lamin A/C heart disease is important because implantable cardioverter defibrillators need to be implanted early. Promising oral drug therapies are within reach thanks to research into the mitogen-activated protein kinase (MAPK) and affiliated pathways. Personalised heart failure therapy may soon become feasible for LMNA, alongside personalised risk stratification, as variant-related differences in phenotype severity and clinical course are being steadily elucidated.Genotyping and family screening are clinically important both to confirm and to exclude LMNA mutations, but it is the three-pronged integration of such genetic information with functional data from in vivo cardiomyocyte mechanics, and pathological data from microscopy of the nuclear envelope, that is properly reshaping our LMNA knowledge base, one variant at a time. This review explains the biology of lamin A/C heart disease (genetics, structure and function of lamins), clinical presentation (diagnostic pointers, electrocardiographic and imaging features), aspects of screening and management, including current uncertainties, and future directions.

In this Perspectives article, Captur et al . explain the fundamental principles of fractal geometry, and summarize cardiovascular studies in which fractal methods have been used to investigate disease mechanisms. The authors propose that clinical researchers can deploy innovative fractal solutions to common cardiac problems, which might ultimately translate into advancements in patient care. For clinicians grappling with quantifying the complex spatial and temporal patterns of cardiac structure and function (such as myocardial trabeculae, coronary microvascular anatomy, tissue perfusion, myocyte histology, electrical conduction, heart rate, and blood-pressure variability), fractal analysis is a powerful, but still underused, mathematical tool. In this Perspectives article, we explain some fundamental principles of fractal geometry and place it in a familiar medical setting. We summarize studies in the cardiovascular sciences in which fractal methods have successfully been used to investigate disease mechanisms, and suggest potential future clinical roles in cardiac imaging and time series measurements. We believe that clinical researchers can deploy innovative fractal solutions to common cardiac problems that might ultimately translate into advancements for patient care.

Measurement of cardiac structure and function from images (e.g. volumes, mass and derived parameters such as left ventricular (LV) ejection fraction [LVEF]) guides care for millions. This is best assessed using cardiovascular magnetic resonance (CMR), but image analysis is currently performed by individual clinicians, which introduces error. We sought to develop a machine learning algorithm for volumetric analysis of CMR images with demonstrably better precision than human analysis. A fully automated machine learning algorithm was trained on 1923 scans (10 scanner models, 13 institutions, 9 clinical conditions, 60,000 contours) and used to segment the LV blood volume and myocardium. Performance was quantified by measuring precision on an independent multi-site validation dataset with multiple pathologies with n = 109 patients, scanned twice. This dataset was augmented with a further 1277 patients scanned as part of routine clinical care to allow qualitative assessment of generalization ability by identifying mis-segmentations. Machine learning algorithm (‘machine’) performance was compared to three clinicians (‘human’) and a commercial tool (cvi42, Circle Cardiovascular Imaging). Machine analysis was quicker (20 s per patient) than human (13 min). Overall machine mis-segmentation rate was 1 in 479 images for the combined dataset, occurring mostly in rare pathologies not encountered in training. Without correcting these mis-segmentations, machine analysis had superior precision to three clinicians (e.g. scan-rescan coefficients of variation of human vs machine: LVEF 6.0% vs 4.2%, LV mass 4.8% vs. 3.6%; both P < 0.05), translating to a 46% reduction in required trial sample size using an LVEF endpoint. We present a fully automated algorithm for measuring LV structure and global systolic function that betters human performance for speed and precision.

A frailty index (FI) counts health deficit accumulation. Besides traditional risk factors, it is unknown whether the health deficit burden is related to the appearance of cardiovascular disease. In order to answer this question, the same multidimensional FI looking at 45-health deficits was serially calculated per participant at 4 time periods (0–16, 19–44, 45–54 and 60–64 years) using data from the 1946 Medical Research Council (MRC) British National Survey of Health and Development (NSHD)—the world’s longest running longitudinal birth cohort with continuous follow-up. From these the mean and total FI for the life-course, and the step change in deficit accumulation from one time period to another was derived. Echocardiographic data at 60–64 years provided: ejection fraction (EF), left ventricular mass indexed to body surface area (LVmassi, BSA), myocardial contraction fraction indexed to BSA (MCF i ) and E/e′. Generalized linear models assessed the association between FIs and echocardiographic parameters after adjustment for relevant covariates. 1375 participants were included. For each single new deficit accumulated at any one of the 4 time periods, LVmass i increased by 0.91–1.44% ( p  < 0.013), while MCF i decreased by 0.6–1.02% ( p  < 0.05). A unit increase in FI at age 45–54 and 60–64, decreased EF by 11–12% ( p  < 0.013). A single health deficit step change occurring between 60 and 64 years and one of the earlier time periods, translated into higher odds (2.1–78.5, p  < 0.020) of elevated LV filling pressure. Thus, the accumulation of health deficits at any time period of the life-course associates with a maladaptive cardiac phenotype in older age, dominated by myocardial hypertrophy and poorer function.

BackgroundKey workers played a pivotal role during the national lockdown in the UK’s response to the COVID-19 pandemic. Although protective measures have been taken, the impact of the pandemic on key workers is yet to be fully elucidated.MethodsParticipants were from four longitudinal age-homogeneous British cohorts (born in 2001, 1990, 1970 and 1958). A web-based survey provided outcome data during the first UK national lockdown (May 2020) on COVID-19 infection status, changes in financial situation, trust in government, conflict with people around, household composition, psychological distress, alcohol consumption, smoking and sleep duration. Generalised linear models with logit link assessed the association between being a key worker and the above outcomes. Adjustment was made for cohort design, non-response, sex, ethnicity, adult socioeconomic position (SEP), childhood SEP, the presence of a chronic illness and receipt of a shielding letter. Meta-analyses were performed across the cohorts.Findings13 736 participants were included. During lockdown, being a key worker was associated with increased chances of being infected with COVID-19 (OR 1.43, 95% CI 1.22 to 1.68) and experiencing conflict with people around (OR 1.19, 95% CI 1.03 to 1.37). However, key workers were less likely to be worse off financially (OR 0.32, 95% CI 0.24 to 0.65), to consume more alcohol (OR 0.88, 95% CI 0.79 to 0.98) or to smoke more (OR 0.60, 95% CI 0.44 to 0.80) during lockdown. Interestingly, being a key worker was not associated with psychological distress (OR 0.95, 95% CI 0.85 to 1.05).InterpretationBeing a key worker during the first UK COVID-19 lockdown was a double-edged sword, with both benefits and downsides. The UK government had the basic duty to protect its key workers from SARS-CoV-2 infection, but it may have failed to do so, and there is an urgent need to rectify this in light of the ongoing third wave.

We compared speckle tracking echocardiography (STE) and feature tracking cardiovascular magnetic resonance (FT-CMR) in patients with hypertrophic cardiomyopathy (HC) with a varying extent of fibrosis as defined by late gadolinium enhancement to look at the level of agreement between methods and their ability to relate those to myocardial fibrosis. At 2 reference centers, 79 patients with HC and 16 volunteers (the control group) underwent STE and CMR with late gadolinium enhancement and FT-CMR. Patients were classified into 3 categories: no detectable, limited, and extensive fibrosis. Global longitudinal strain (GLS) and global radial strain (GRS) were derived using FT-CMR and STE. STE-derived GRS was decreased in all HC categories compared with the control group (p <0.001), whereas FT-CMR GRS was reduced only in patients with HC with fibrosis (p <0.05). Reduced STE-derived GLS was associated with extensive fibrosis (p <0.05) and a value less than −15.2% identified those with extensive fibrosis (sensitivity 79%, specificity 92%, area under the curve 0.863, 95% confidence interval [CI] 0.76 to 0.97, p <0.001). Inter-modality agreement was moderate for STE versus CMR-GLS (overall population intra-class correlation coefficient = 0.615, 95% CI 0.42 to 0.75, p <0.001; patients with HC 0.63, 0.42 to 0.76, p <0.001) and GRS (overall population intra-class correlation coefficient = 0.601, 95% CI 0.397 to 0.735, p <0.001). A low level of agreement for GRS was seen between methods in patients with HC. In conclusion, strain indexes measured using echocardiography and CMR are reduced in patients with HC compared with the control group and correlate well with the burden of myocardial fibrosis. Reduced STE-GLS can identify patients with extensive fibrosis, but whether there is an added value for risk stratification for sudden cardiac death remains to be determined.