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Background Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) offers the potential to noninvasively characterize the phenotypic substrate for sudden cardiac death (SCD). Objectives The authors assessed the utility of infarct characterization by CMR, including scar microstructure analysis, to predict SCD in patients with coronary artery disease (CAD). Methods Patients with stable CAD were prospectively recruited into a CMR registry. LGE quantification of core infarction and the peri-infarct zone (PIZ) was performed alongside computational image analysis to extract morphologic and texture scar microstructure features. The primary outcome was SCD or aborted SCD. Results Of 437 patients (mean age: 64 years; mean left ventricular ejection fraction [LVEF]: 47%) followed for a median of 6.3 years, 49 patients (11.2%) experienced the primary outcome. On multivariable analysis, PIZ mass and core infarct mass were independently associated with the primary outcome (per gram: HR: 1.07 [95% CI: 1.02-1.12]; P = 0.002 and HR: 1.03 [95% CI: 1.01-1.05]; P = 0.01, respectively), and the addition of both parameters improved discrimination of the model (Harrell’s C-statistic: 0.64-0.79). PIZ mass, however, did not provide incremental prognostic value over core infarct mass based on Harrell’s C-statistic or risk reclassification analysis. Severely reduced LVEF did not predict the primary endpoint after adjustment for scar mass. On scar microstructure analysis, the number of LGE islands in addition to scar transmurality, radiality, interface area, and entropy were all associated with the primary outcome after adjustment for severely reduced LVEF and New York Heart Association functional class of >1. No scar microstructure feature remained associated with the primary endpoint when PIZ mass and core infarct mass were added to the regression models. Conclusions Comprehensive LGE characterization independently predicted SCD risk beyond conventional predictors used in implantable cardioverter-defibrillator (ICD) insertion guidelines. These results signify the potential for a more personalized approach to determining ICD candidacy in CAD.

Background:Cardiovascular disease is a major cause of excess mortality in people with schizophrenia. Several factors are responsible, including lifestyle and metabolic effects of antipsychotics. However, variations in cardiac structure and function are seen in people with schizophrenia in the absence of cardiovascular disease risk factors and after accounting for lifestyle and medication. Therefore, we aimed to explore whether shared genetic causes contribute to these cardiac variations. Methods:For this observational study, we used data from the UK Biobank and included White British or Irish individuals without diagnosed schizophrenia with variable polygenic risk scores for the condition. To test the association between polygenic risk score for schizophrenia and cardiac phenotype, we used principal component analysis and regression. Robust regression was then used to explore the association between the polygenic risk score for schizophrenia and individual cardiac phenotypes. We repeated analyses with fibro-inflammatory pathway-specific polygenic risk scores for schizophrenia. Last, we investigated genome-wide sharing of common variants between schizophrenia and cardiac phenotypes using linkage disequilibrium score regression. The primary outcome was principal component regression. Findings:Of 33 353 individuals recruited, 32 279 participants had complete cardiac MRI data and were included in the analysis, of whom 16 625 (51·5%) were female and 15 654 (48·5%) were male. 1074 participants were excluded on the basis of incomplete cardiac MRI data (for all phenotypes). A model regressing polygenic risk scores for schizophrenia onto the first five cardiac principal components of the principal components analysis was significant (F=5·09; p=0·00012). Principal component 1 captured a pattern of increased cardiac volumes, increased absolute peak diastolic strain rates, and reduced ejection fractions; polygenic risk scores for schizophrenia and principal component 1 were negatively associated (β=–0·01 [SE 0·003]; p=0·017). Similar to the principal component analysis results, for individual cardiac phenotypes, we observed negative associations between polygenic risk scores for schizophrenia and indexed right ventricular end-systolic volume (β=–0·14 [0·04]; p=0·0013, pFDR=0·015), indexed right ventricular end-diastolic volume (β=–0·17 [0·08]); p=0·025; pFDR=0·082), and absolute longitudinal peak diastolic strain rates (β=–0·01 [0·003]; p=0·0024, pFDR=0·015), and a positive association between polygenic risk scores for schizophrenia and right ventricular ejection fraction (β=0·09 [0·03]; p=0·0041, pFDR=0·015). Models examining the transforming growth factor-β (TGF-β)-specific and acute inflammation-specific polygenic risk scores for schizophrenia found significant associations with the first five principal components (F=2·62, p=0·022;F=2·54, p=0·026). Using linkage disequilibrium score regression, we observed genetic overlap with schizophrenia for right ventricular end-systolic volume and right ventricular ejection fraction (p=0·0090, p=0·0077). Interpretation:High polygenic risk scores for schizophrenia are associated with decreased cardiac volumes, increased ejection fractions, and decreased absolute peak diastolic strain rates. TGF-β and inflammatory pathways might be implicated, and there is evidence of genetic overlap for some cardiac phenotypes. Reduced absolute peak diastolic strain rates indicate increased myocardial stiffness and diastolic dysfunction, which increases risk of cardiac disease. Thus, genetic risk for schizophrenia is associated with cardiac structural changes that can worsen cardiac outcomes. Further work is required to determine whether these associations are specific to schizophrenia or are also seen in other psychiatric conditions. FundingNational Institute for Health Research, Maudsley Charity, Wellcome Trust, Medical Research Council, Academy of Medical Sciences, Edmond J Safra Foundation, British Heart Foundation.

BackgroundPre-eclampsia is a multiorgan disease of pregnancy that has short- and long-term implications for the woman and fetus, whose immediate impact is poorly understood. We present a novel multi-system approach to MRI investigation of pre-eclampsia, with acquisition of maternal cardiac, placental, and fetal brain anatomical and functional imaging.MethodsA prospective study was carried out recruiting pregnant women with pre-eclampsia, chronic hypertension, or no medical complications, and a non-pregnant female cohort. All women underwent a cardiac MRI, and pregnant women underwent a fetal-placental MRI. Cardiac analysis for structural, morphological and flow data was undertaken; placenta and fetal brain volumetric and T2* data were obtained. All results were corrected for gestational age.ResultsSeventy-eight MRIs were obtained during pregnancy. Pregnancies affected by pre-eclampsia demonstrated lower placental and fetal brain T2*. Within the pre-eclampsia group, three placental T2* results were within the normal range, these were the only cases with normal placental histopathology. Similarly, three fetal brain T2* results were within the normal range; these cases had no evidence of cerebral redistribution on fetal Dopplers. Cardiac MRI analysis demonstrated higher left ventricular mass in pre-eclampsia with 3D modelling revealing additional specific characteristics of eccentricity and outflow track remodelling.ConclusionsWe present the first holistic assessment of the immediate implications of pre-eclampsia on the placenta, maternal heart, and fetal brain. As well as having potential clinical implications for the risk-stratification and management of women with pre-eclampsia, this gives an insight into disease mechanism.What is new?We propose a comprehensive MRI protocol for maternal cardiac and fetal-placental imaging that is safe in pregnancy and acceptable to women, and report on the largest set of functional placental MRI data in pre-eclamptic pregnanciesThe findings suggest altered oxygenation and microstructure in the placenta, which is associated with similar changes in the fetal brainThe study identifies a specific pattern of remodelling in the maternal left ventricle that is associated with pre-eclampsiaWhat are the clinical implications?We demonstrate that MRI provides maternal and fetal insights into pre-eclamptic pregnancies that cannot be obtained by other means during pregnancyFunctional placental MRI may be able to differentiate between preeclampsia disease severities, while fetal brain imaging may help refine neurodevelopmental prognostic assessment of children exposed to pre-eclampsia in uteroA specific 3D pattern of left ventricular remodelling and thickening was found to be discriminant of the presence of pre-eclampsia and may provide a basis for improved clinical risk stratification

Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality, with rare pathogenic variants found in about a third of cases (sarcomere-positive). Large-scale genome-wide association studies (GWAS) demonstrate that common genetic variation contributes substantially to HCM risk. Here, we derive polygenic scores (PGS) from HCM GWAS, and multi-trait analysis of GWAS incorporating genetically-correlated traits, and test their performance in the UK Biobank, 100,000 Genomes Project, and across clinical cohorts. Higher PGS substantially increases population risk of HCM, particularly amongst sarcomere-positive carriers where HCM penetrance differs 10-fold between those in the highest and lowest PGS quintiles. In relatives of HCM patients, PGS stratifies risks of developing HCM and adverse outcomes. Finally, PGS strongly predicts risk of adverse outcomes in HCM, with a 4 to 6-fold increase in death between cases in the highest and lowest PGS quintiles. These findings promise broad clinical utility of PGS in the general population, in cases, and in families with HCM, enabling tailored screening and surveillance, and stratification of risk of adverse outcomes.

BackgroundHypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous phenotypes but there is no systematic framework for classifying morphology or assessing associated risks. Here we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression.MethodsWe enrolled 436 HCM patients (median age 60 years; 28.8% women) with clinical, genetic and imaging data. An independent cohort of 60 HCM patients from Singapore (median age 59 years; 11% women) and a reference population from UK Biobank (n = 16,691, mean age 55 years; 52.5% women) were also recruited. We used machine learning to analyse the three-dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree.ResultsCarriers of pathogenic or likely pathogenic variants for HCM (P/LP) variants had lower left ventricular mass, but greater basal septal hypertrophy, with reduced lifespan (mean follow-up 9.9 years) compared to genotype negative individuals (hazard ratio: 2.66; 95% confidence interval [CI]: 1.42-4.96;P< 0.002). Four main phenotypic branches were identified using unsupervised learning of three-dimensional shape: 1) non-sarcomeric hypertrophy with co-existing hypertension; 2) diffuse and basal asymmetric hypertrophy associated with outflow tract obstruction; 3) isolated basal hypertrophy; 4) milder non-obstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for P/LP variants: 2.18 [95% CI: 1.93-2.28,P= 0.0001]). Polygenic risk for HCM was also associated with different patterns and degrees of disease expression. The model was generalisable to an independent cohort (trustworthinessM1: 0.86-0.88).ConclusionsWe report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severity, genetic risk and outcomes. This approach will be of value in understanding the causes and consequences of disease diversity.

Data-driven deep learning approaches to image registration can be less accurate than conventional iterative approaches, especially when training data is limited. To address this whilst retaining the fast inference speed of deep learning, we propose VR-Net, a novel cascaded variational network for unsupervised deformable image registration. Using the variable splitting optimization scheme, we first convert the image registration problem, established in a generic variational framework, into two sub-problems, one with a point-wise, closed-form solution while the other one is a denoising problem. We then propose two neural layers (i.e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i.e. generalized denoising layer). Finally, we cascade the warping layer, intensity consistency layer, and generalized denoising layer to form the VR-Net. Extensive experiments on three (two 2D and one 3D) cardiac magnetic resonance imaging datasets show that VR-Net outperforms state-of-the-art deep learning methods on registration accuracy, while maintains the fast inference speed of deep learning and the data-efficiency of variational model., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible