98 results found
Jia X, Thorley A, Chen W, et al., 2021, Learning a Model-Driven Variational Network for Deformable Image Registration., IEEE Trans Med Imaging, Vol: PP
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 issue and meanwhile retain the fast inference speed of deep learning, we propose VR-Net, a novel cascaded variational network for unsupervised deformable image registration. Using a 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 and the other one being 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 (termed generalized denoising layer) to formulate the denoising problem. Finally, we cascade the three neural layers multiple times to form our 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, whilst maintaining the fast inference speed of deep learning and the data-efficiency of variational models.
Halliday BP, de Marvao A, Thilaganathan B, 2021, Peripartum cardiomyopathy and pre-eclampsia: two tips of the same iceberg, EUROPEAN JOURNAL OF HEART FAILURE, ISSN: 1388-9842
Simoes Monteiro de Marvao A, McGurk K, Zheng S, et al., 2021, Phenotypic expression and outcomes in individuals with rare genetic variants of hypertrophic cardiomyopathy, Journal of the American College of Cardiology, ISSN: 0735-1097
Background: Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomereencoding genes, but little is known about the clinical significance of these variants in thegeneral population.Objectives: To compare lifetime outcomes and cardiovascular phenotypes according to thepresence of rare variants in sarcomere-encoding genes amongst middle-aged adults.Methods: We analysed whole exome sequencing and cardiac magnetic resonance (CMR)imaging in UK Biobank participants stratified by sarcomere-encoding variant status.Results: The prevalence of rare variants (allele frequency <0.00004) in HCM-associatedsarcomere-encoding genes in 200,584 participants was 2.9% (n=5,712; 1 in 35), and theprevalence of variants pathogenic or likely pathogenic for HCM (SARC-HCM-P/LP) was0.25% (n=493, 1 in 407). SARC-HCM-P/LP variants were associated with increased risk ofdeath or major adverse cardiac events compared to controls (HR 1.69, 95% CI 1.38 to 2.07,p<0.001), mainly due to heart failure endpoints (HR 4.23, 95% CI 3.07 to 5.83, p<0.001). In21,322 participants with CMR, SARC-HCM-P/LP were associated with asymmetric increasein left ventricular maximum wall thickness (10.9±2.7 vs 9.4±1.6 mm, p<0.001) buthypertrophy (≥13mm) was only present in 18.4% (n=9/49, 95% CI 9 to 32%). SARC-HCMP/LP were still associated with heart failure after adjustment for wall thickness (HR 6.74,95% CI 2.43 to 18.7, p<0.001).Conclusions: In this population of middle-aged adults, SARC-HCM-P/LP variants have lowaggregate penetrance for overt HCM but are associated with increased risk of adversecardiovascular outcomes and an attenuated cardiomyopathic phenotype. Although absoluteevent rates are low, identification of these variants may enhance risk stratification beyondfamilial disease.
Bleakley C, de Marvao A, Morosin M, et al., 2021, Utility of echocardiographic right ventricular subcostal strain in critical care., Eur Heart J Cardiovasc Imaging
AIMS: Right ventricular (RV) strain is a known predictor of outcomes in various heart and lung pathologies but has been considered too technically challenging for routine use in critical care. We examined whether RV strain acquired from the subcostal view, frequently more accessible in the critically ill, is an alternative to conventionally derived RV strain in intensive care. METHODS AND RESULTS: RV strain data were acquired from apical and subcostal views on transthoracic echocardiography (TTE) in 94 patients (35% female), mean age 50.5 ± 15.2 years, venovenous extracorporeal membrane oxygenation (VVECMO) (44%). RV strain values from the apical (mean ± standard deviation; -20.4 ± 6.7) and subcostal views (-21.1 ± 7) were highly correlated (Pearson's r -0.89, P < 0.001). RV subcostal strain correlated moderately well with other echocardiography parameters including tricuspid annular plane systolic excursion (r -0.44, P < 0.001), RV systolic velocity (rho = -0.51, P < 0.001), fractional area change (r -0.66, P < 0.01), and RV outflow tract velocity time integral (r -0.49, P < 0.001). VVECMO was associated with higher RV subcostal strain (non-VVECMO -19.6 ± 6.7 vs. VVECMO -23.2 ± 7, P = 0.01) but not apical RV strain. On univariate analysis, RV subcostal strain was weakly associated with survival at 30 days (R2 = 0.04, P = 0.05, odds ratio =1.08) while apical RV was not (P = 0.16). CONCLUSION : RV subcostal deformation imaging is a reliable surrogate for conventionally derived strain in critical care and may in time prove to be a useful diagnostic marker in this cohort.
Bleakley C, Singh S, de Marvao A, et al., 2021, Reply to: RV dysfunction in Covid-19 ARDS: Is there a difference in the impact of mechanical ventilation and ECMO?, Int J Cardiol, Vol: 332
Bleakley C, de Marvao A, Athayde A, et al., 2021, The Impact of Norepinephrine on Myocardial Perfusion in Critical Illness., J Am Soc Echocardiogr
Howard LSGE, He J, Watson GMJ, et al., 2021, Supplementation with Iron in Pulmonary Arterial Hypertension: Two Randomized Crossover Trials., Ann Am Thorac Soc
RATIONALE: Iron deficiency, in the absence of anaemia, is common in patients with idiopathic and heritable pulmonary arterial hypertension (PAH) and is associated with a worse clinical outcome. Oral iron absorption may be impeded by elevated circulating hepcidin levels. The safety and benefit of parenteral iron replacement in this patient population is unclear. OBJECTIVES: To evaluate the safety and efficacy of parenteral iron replacement in pulmonary arterial hypertension. METHODS: In two randomised, double blind, placebo-controlled 12 week crossover studies, 39 patients in Europe received a single infusion of ferric carboxymaltose (Ferinject®) 1000 mg (or 15 mg/kg if weight < 66.7Kg) or saline as placebo and 17 patients in China received iron dextran (Cosmofer®) 20 mg iron/kg body weight or saline placebo. All patients had idiopathic or heritable PAH and iron deficiency at entry as defined by: a serum ferritin < 37 µg/l or iron < 10.3 µmol/l or transferrin saturations < 16.4%. RESULTS: Both iron treatments were well tolerated and improved iron status. Analysed separately and combined, there was no effect on any measure of exercise capacity (using cardiopulmonary exercise testing or 6 minute walk test) or cardio-pulmonary haemodynamics, as assessed by right heart catheterisation, cardiac magnetic resonance or plasma NT-proBNP, at 12 weeks. CONCLUSION: Iron repletion by administration of a slow release iron preparation as a single infusion to PAH patients with iron deficiency without overt anaemia was well tolerated but provided no significant clinical benefit at 12 weeks. Clinical trial registered with ClinicalTrials.gov (NCT01447628).
de Marvao A, McGurk KA, Zheng SL, et al., 2021, Outcomes and phenotypic expression of rare variants in hypertrophic cardiomyopathy genes amongst UK Biobank participants, Publisher: Cold Spring Harbor Laboratory
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomere-encoding genes, but little is known about the clinical significance of these variants in the general population.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We compared outcomes and cardiovascular phenotypes in UK Biobank participants with whole exome sequencing stratified by sarcomere-encoding variant status.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The prevalence of rare variants (allele frequency <0.00004) in HCM-associated sarcomere-encoding genes in 200,584 participants was 2.9% (n=5,727; 1 in 35), of which 0.24% (n=474, 1 in 423) were pathogenic or likely pathogenic variants (SARC-P/LP). SARC-P/LP variants were associated with increased risk of death or major adverse cardiac events compared to controls (HR 1.68, 95% CI 1.37-2.06, p<0.001), mainly due to heart failure (HR 4.40, 95% CI 3.22-6.02, p<0.001) and arrhythmia (HR 1.55, 95% CI 1.18-2.03, p=0.002). In 21,322 participants with cardiac magnetic resonance imaging, SARC-P/LP were associated with increased left ventricular maximum wall thickness (10.9±2.7 vs 9.4±1.6 mm, p<0.001) and concentric remodelling (mass/volume ratio: 0.63±0.12 vs 0.58±0.09 g/mL, p<0.001), but hypertrophy (≥13mm) was only present in 16% (n=7/43, 95% CI 7-31%). Other rare sarcomere-encoding variants had a weak effect on wall thickness (9.5±1.7 vs 9.4±1.6 mm, p=0.002) with no combined excess cardiovascular risk (HR 1.00 95% CI 0.92-1.08, p=0.9).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>In the general population, SARC-P/LP variants have low aggregate penetrance for overt HCM bu
Mazzarotto F, Hawley MH, Beltrami M, et al., 2021, Systematic large-scale assessment of the genetic architecture of left ventricular non-compaction reveals diverse aetiologies, Genetics in Medicine, Vol: 23, Pages: 856-864, ISSN: 1098-3600
Purpose: To characterise the genetic architecture of left ventricular non-compaction (LVNC) and investigate the extent to which it may represent a distinct pathology or a secondary phenotype associated with other cardiac diseases.Methods: We performed rare variant association analysis with 840 LVNC cases and 125,748 gnomAD population controls, and compared results to similar analyses on dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM). Results: We observed substantial genetic overlap indicating that LVNC often represents a phenotypic variation of DCM or HCM. In contrast, truncating variants (TV) in MYH7, ACTN2 and PRDM16 were uniquely associated with LVNC and may reflect a distinct LVNC aetiology. In particular, MYH7 TV, generally considered non-pathogenic for cardiomyopathies, were 20-fold enriched in LVNC cases over controls. MYH7 TV heterozygotes identified in the UK Biobank and healthy volunteer cohorts also displayed significantly greater non-compaction compared to matched controls. RYR2 exon deletions and HCN4 transmembrane variants were also enriched in LVNC, supporting prior reports of association with arrhythmogenic LVNC phenotypes.Conclusions: LVNC is characterised by substantial genetic overlap with DCM/HCM but is also associated with distinct non-compaction and arrhythmia aetiologies. These results will enable enhanced application of LVNC genetic testing and help to distinguish pathological from physiological non-compaction.
Ware J, Tadros R, Francis C, et al., 2021, Shared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect, Nature Genetics, Vol: 53, Pages: 128-134, ISSN: 1061-4036
The heart muscle diseases hypertrophic (HCM) and dilated (DCM) cardiomyopathies are leading causes of sudden death and heart failure in young otherwise healthy individuals. We conducted genome-wide association studies (GWAS) and multi-trait analyses in HCM (1,733 cases), DCM (5,521 cases), and nine left ventricular (LV) traits in 19,260 UK Biobank participants with structurally-normal hearts. We identified 16 loci associated with HCM, 13 with DCM, and 23 with LV traits. We show strong genetic correlations between LV traits and cardiomyopathies, with opposing effects in HCM and DCM. Two-sample Mendelian randomization supports a causal association linking increased contractility with HCM risk. A polygenic risk score (PRS) explains a significant portion of phenotypic variability in carriers of HCM-causing rare variants. Our findings thus provide evidence that PRS may account for variability in Mendelian diseases. More broadly, we provide insights into how genetic pathways may lead to distinct disorders through opposing genetic effects.
Lu W, Jia X, Chen W, et al., 2021, One-stage Multi-task Detector for 3D Cardiac MR Imaging, 25th International Conference on Pattern Recognition (ICPR), Publisher: IEEE COMPUTER SOC, Pages: 1949-1955, ISSN: 1051-4651
Zhang X, Walsh R, Whiffin N, et al., 2021, Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions, Genetics in Medicine, Vol: 23, Pages: 69-79, ISSN: 1098-3600
Background: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning tools are useful for genome-wide variant prioritisation but remain imprecise. Since the relationship between molecular consequence and likelihood of pathogenicity varies between genes with distinct molecular mechanisms, we hypothesised that a disease-specific classifier may outperform existing genome-wide tools. Methods: We present a novel disease-specific variant classification tool, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias, trained with variants of known clinical effect. To benchmark against state-of-the-art genome-wide pathogenicity classification tools, we assessed classification of hold-out test variants using both overall performance metrics, and metrics of high-confidence (>90%) classifications relevant to variant interpretation. We further evaluated the prioritisation of variants associated with disease and patient clinical outcomes, providing validations that are robust to potential mis-classification in gold-standard reference datasets.Results: CardioBoost has higher discriminating power than published genome-wide variant classification tools in distinguishing between pathogenic and benign variants based on overall classification performance measures with the highest area under the Precision-Recall Curve as 91% for cardiomyopathies and as 96% for inherited arrhythmias. When assessed at high-confidence (>90%) classification thresholds, prediction accuracy is improved by at least 120% over existing tools for both cardiomyopathies and arrhythmias, with significantly improved sensitivity and specificity. Finally, CardioBoost improves prioritisation of variants significantly associated with disease, and stratifies survival of patients with cardiomyopathies, confirming biologically relevant vari
Meyer H, Dawes T, Serrani M, et al., 2020, Genetic and functional insights into the fractal structure of the heart, Nature, Vol: 584, Pages: 589-594, ISSN: 0028-0836
The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a vestigeof embryonic development.1,2 The function of these trabeculae in adults and their genetic architecture are unknown. Toinvestigate this we performed a genome-wide association study using fractal analysis of trabecular morphology as animage-derived phenotype in 18,096 UK Biobank participants. We identified 16 significant loci containing genes associatedwith haemodynamic phenotypes and regulation of cytoskeletal arborisation.3,4 Using biomechanical simulations and humanobservational data, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Throughgenetic association studies with cardiac disease phenotypes and Mendelian randomisation, we find a causal relationshipbetween trabecular morphology and cardiovascular disease risk. These findings suggest an unexpected role for myocardialtrabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity, and reveal theirinfluence on susceptibility to disease
Pua CJ, Tham N, Chin CW, et al., 2020, Genetic studies of hypertrophic cardiomyopathy in Singaporeans identify variants in TNNI3 and TNNT2 that are common in Chinese patients, Circulation: Genomic and Precision Medicine, Vol: 13, Pages: 424-434, ISSN: 2574-8300
Background - To assess the genetic architecture of hypertrophic cardiomyopathy (HCM) in patients of predominantly Chinese ancestry.Methods - We sequenced HCM disease genes in Singaporean patients (n=224) and Singaporean controls (n=3,634), compared findings with additional populations and Caucasian HCM cohorts (n=6,179) and performed in vitro functional studies.Results - Singaporean HCM patients had significantly fewer confidently interpreted HCM disease variants (Pathogenic (P)/Likely Pathogenic (LP):18%, p<0.0001) but an excess of variants of unknown significance (exVUS: 24%, p<0.0001), as compared to Caucasians (P/LP: 31%, exVUS: 7%). Two missense variants in thin filament encoding genes were commonly seen in Singaporean HCM (TNNI3:p.R79C, disease allele frequency (AF)=0.018; TNNT2:p.R286H, disease AF=0.022) and are enriched in Singaporean HCM when compared with Asian controls (TNNI3:p.R79C, Singaporean controls AF=0.0055, p=0.0057, gnomAD-East Asian (gnomAD-EA) AF=0.0062, p=0.0086; TNNT2:p.R286H, Singaporean controls AF=0.0017, p<0.0001, gnomAD-EA AF=0.0009, p<0.0001). Both these variants have conflicting annotations in ClinVar and are of low penetrance (TNNI3:p.R79C, 0.7%; TNNT2:p.R286H, 2.7%) but are predicted to be deleterious by computational tools. In population controls, TNNI3:p.R79C carriers had significantly thicker left ventricular walls compared to non-carriers while its etiological fraction is limited (0.70, 95% CI: 0.35-0.86) and thus TNNI3:p.R79C is considered a VUS. Mutant TNNT2:p.R286H iPSC-CMs show hypercontractility, increased metabolic requirements and cellular hypertrophy and the etiological fraction (0.93, 95% CI: 0.83-0.97) support the likely pathogenicity of TNNT2:p.R286H.Conclusions - As compared to Caucasians, Chinese HCM patients commonly have low penetrance risk alleles in TNNT2 or TNNI3 but exhibit few clinically actionable HCM variants overall. This highlights the need for greater study of HCM genetics in non-Caucasian pop
Osimo EF, Brugger SP, de Marvao A, et al., 2020, Cardiac structure and function in schizophrenia: cardiac magnetic resonance imaging study, British Journal of Psychiatry, Vol: 217, Pages: 450-457, ISSN: 0007-1250
BACKGROUND: Heart disease is the leading cause of death in schizophrenia. However, there has been little research directly examining cardiac function in schizophrenia. AIMS: To investigate cardiac structure and function in individuals with schizophrenia using cardiac magnetic resonance imaging (CMR) after excluding medical and metabolic comorbidity. METHOD: In total, 80 participants underwent CMR to determine biventricular volumes and function and measures of blood pressure, physical activity and glycated haemoglobin levels. Individuals with schizophrenia ('patients') and controls were matched for age, gender, ethnicity and body surface area. RESULTS: Patients had significantly smaller indexed left ventricular (LV) end-diastolic volume (effect size d = -0.82, P = 0.001), LV end-systolic volume (d = -0.58, P = 0.02), LV stroke volume (d = -0.85, P = 0.001), right ventricular (RV) end-diastolic volume (d = -0.79, P = 0.002), RV end-systolic volume (d = -0.58, P = 0.02), and RV stroke volume (d = -0.87, P = 0.001) but unaltered ejection fractions relative to controls. LV concentricity (d = 0.73, P = 0.003) and septal thickness (d = 1.13, P < 0.001) were significantly larger in the patients. Mean concentricity in patients was above the reference range. The findings were largely unchanged after adjusting for smoking and/or exercise levels and were independent of medication dose and duration. CONCLUSIONS: Individuals with schizophrenia show evidence of concentric cardiac remodelling compared with healthy controls of a similar age, gender, ethnicity, body surface area and blood pressure, and independent of smoking and activity levels. This could be contributing to the excess cardiovascular mortality observed in schizophrenia. Future studies should investigate the contribution of antipsychotic medication to these changes.
Osimo E, Brugger S, De Marvao A, et al., 2020, Cardiac structure and function in schizophrenia: a cardiac MR imaging study, British Journal of Psychiatry, Vol: 217, Pages: 450-457, ISSN: 0007-1250
Background: Heart disease is the leading cause of death in schizophrenia. However, there has been little research directly examining cardiac function in schizophrenia.Aims:We investigated cardiac structure and function in patients with schizophrenia using cardiac magnetic resonance imaging (CMR) after excluding medical and metabolic comorbidity. Methods:80 participants underwent CMR to determine biventricular volumes and function and measures of blood pressure, physical activity, and glycated haemoglobin levels. Patients and controls were matched for age, sex, ethnicity, and body surface area. Results:Patients with schizophrenia had significantly smaller indexed left ventricular (LV) end-diastolic volume (effect size, d=-0.82, p=0.001), LV end-systolic volume (d=-0.58, p=0.02), LV stroke volume (d=-0.85, p=0.001), right ventricular (RV) end-diastolic volume (d=-0.79, p=0.002), RV end-systolic volume (d=-0.58, p=0.02), and RV stroke volume (d=-0.87, p=0.001) but unaltered ejection fractions relative to controls. LV concentricity (d=0.73, p=0.003) and septal thickness (d=1.13, p<0.001) were significantly larger in schizophrenia. Mean concentricity in patients was above the reference range. The findings were largely unchanged after adjusting for smoking and/or exercise levels and were independent of medication dose and duration. Conclusions:Patients with schizophrenia show evidence of concentric cardiac remodelling compared to healthy controls of a similar age, sex, ethnicity, body surface area and blood pressure, and independent of smoking and activity levels. This could be contributing to the excess cardiovascular mortality observed in patients. Future studies should investigate the contribution of antipsychotic medication to these changes.
Biffi C, Cerrolaza Martinez JJ, Tarroni G, et al., 2020, Explainable anatomical shape analysis through deep hierarchical generative models, IEEE Transactions on Medical Imaging, Vol: 39, Pages: 2088-2099, ISSN: 0278-0062
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer’s disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating highthroughput analysis of normal anatomy and pathology in largescale studies of volumetric imaging.
Bhuva AN, Treibel TA, De Marvao A, et al., 2020, Sex and regional differences inmyocardial plasticity in aortic stenosis are revealed by 3D modelmachine learning, EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, Vol: 21, Pages: 417-427, ISSN: 2047-2404
Phua AIH, Le T-T, Tara SW, et al., 2020, Paradoxical higher myocardial wall stress and increased cardiac remodeling despite lower mass in females, Journal of the American Heart Association, Vol: 9, Pages: 1-7, ISSN: 2047-9980
BackgroundIncreased left ventricular (LV) mass is characterized by increased myocardial wall thickness and/or ventricular dilatation that is associated with worse outcomes. We aim to comprehensively compare sex‐stratified associations between measures of LV remodeling and increasing LV mass in the general population.Methods and ResultsParticipants were prospectively recruited in the National Heart Center Singapore Biobank to examine health and cardiovascular risk factors in the general population. Cardiovascular magnetic resonance was performed in all individuals. Participants with established cardiovascular diseases and abnormal cardiovascular magnetic resonance scan results were excluded. Global and regional measures of LV remodeling (geometry, function, interstitial volumes, and wall stress) were performed using conventional image analysis and novel 3‐dimensional machine learning phenotyping. Sex‐stratified analyses were performed in 1005 participants (57% males; 53±13 years). Age and prevalence of cardiovascular risk factors were well‐matched in both sexes (P>0.05 for all). Progressive increase in LV mass was associated with increased concentricity in either sex, but to a greater extent in females. Compared with males, females had higher wall stress (mean difference: 170 mm Hg, P<0.0001) despite smaller LV mass (42.4±8.2 versus 55.6±14.2 g/m2, P<0.0001), lower blood pressures (P<0.0001), and higher LV ejection fraction (61.9±5.9% versus 58.6±6.4%, P<0.0001). The regions of increased concentric remodeling corresponded to regions of increased wall stress. Compared with males, females had increased extracellular volume fraction (27.1±2.4% versus 25.1±2.9%, P<0.0001).ConclusionsCompared with males, females have lower LV mass, increased wall stress, and concentric remodeling. These findings provide mechanistic insights that females are susceptible to particular cardiovascular complications.
Mazzarotto F, Tayal U, Buchan RJ, et al., 2020, Re-evaluating the genetic contribution of monogenic dilated cardiomyopathy, Circulation, Vol: 141, Pages: 387-398, ISSN: 0009-7322
Background: Dilated cardiomyopathy (DCM) is genetically heterogeneous, with >100 purported disease genes tested in clinical laboratories. However, many genes were originally identified based on candidate-gene studies that did not adequately account for background population variation. Here we define the frequency of rare variation in 2538 DCM patients across protein-coding regions of 56 commonly tested genes and compare this to both 912 confirmed healthy controls and a reference population of 60,706 individuals in order to identify clinically interpretable genes robustly associated with dominant monogenic DCM.Methods: We used the TruSight Cardio sequencing panel to evaluate the burden of rare variants in 56 putative DCM genes in 1040 DCM patients and 912 healthy volunteers processed with identical sequencing and bioinformatics pipelines. We further aggregated data from 1498 DCM patients sequenced in diagnostic laboratories and the ExAC database for replication and meta-analysis.Results: Truncating variants in TTN and DSP were associated with DCM in all comparisons. Variants in MYH7, LMNA, BAG3, TNNT2, TNNC1, PLN, ACTC1, NEXN, TPM1 and VCL were significantly enriched in specific patient subsets, with the last 2 genes potentially contributing primarily to early-onset forms of DCM. Overall, rare variants in these 12 genes potentially explained 17% of cases in the outpatient clinic cohort representing a broad range of adult DCM patients and 26% of cases in the diagnostic referral cohort enriched in familial and early-onset DCM. Whilst the absence of a significant excess in other genes cannot preclude a limited role in disease, such genes have limited diagnostic value since novel variants will be uninterpretable and their diagnostic yield is minimal.Conclusion: In the largest sequenced DCM cohort yet described, we observe robust disease association with 12 genes, highlighting their importance in DCM and translating into high interpretability in diagnostic testing. The
de Marvao A, Dawes TJ, Howard JP, et al., 2020, Artificial intelligence and the cardiologist: what you need to know for 2020., Heart, Vol: 106, Pages: 399-400, ISSN: 1355-6037
de Marvao A, Dawes TJW, O'Regan DP, 2020, Artificial intelligence for cardiac imaging-genetics research, Frontiers in Cardiovascular Medicine, Vol: 6, Pages: 1-10, ISSN: 2297-055X
Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.
Mazzarotto F, Hawley M, Beltrami M, et al., 2020, The genetic architecture of left ventricular non-compaction reveals both substantial overlap with other cardiomyopathies and a distinct aetiology in a subset of cases, Publisher: bioRxiv
Rationale: Left ventricular non-compaction (LVNC) is a condition characterised by trabeculations in the myocardial wall and is the subject of considerable conjecture as to whether it represents a distinct pathology or a secondary phenotype associated with other cardiac diseases, particularly cardiomyopathies. Objective: To investigate the genetic architecture of LVNC by identifying genes and variant classes robustly associated with disease and comparing these to other genetically characterised cardiomyopathies. Methods and Results: We performed rare variant association analysis using six different LVNC cohorts comprising 840 cases together with 125,748 gnomAD population controls and compared results to similar analyses with dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) cases. We observed substantial overlap in genes and variant classes enriched in LVNC and DCM/HCM, indicating that in many cases LVNC belongs to a spectrum of more established cardiomyopathies, with non-compaction representing a phenotypic variation in patients with DCM- or HCM-causing variants. In contrast, five variant classes were uniquely enriched in LVNC cases, of which truncating variants in MYH7, ACTN2 and PRDM16 may represent a distinct LVNC aetiology. MYH7 truncating variants are generally considered as non-pathogenic but were detected in 2% of LVNC cases compared to 0.1% of controls, including a cluster of variants around a single splice region. Additionally, structural variants (exon deletions) in RYR2 and missense variants in the transmembrane region of HCN4 were enriched in LVNC cases, confirming prior reports regarding the association of these variant classes with combined LVNC and arrhythmia phenotypes. Conclusions: We demonstrated that genetic association analysis can clarify the relationship between LVNC and established cardiomyopathies, highlighted substantial overlap with DCM/HCM but also identified variant classes associated with distinct LVNC and with joint LVN
Biffi C, Doumou G, Duan J, et al., 2020, Explainable anatomical shape analysis through deep hierarchical generative models., Publisher: arXiv
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating highthroughput analysis of normal anatomy and pathology in largescale studies of volumetric imaging.
Jin S, Savioli N, Marvao AD, et al., 2019, Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network, Publisher: arXiv
In this work, a novel approach is proposed for joint analysis of highdimensional time-resolved cardiac motion features obtained from segmentedcardiac MRI and low dimensional clinical risk factors to improve survivalprediction in heart failure. Different methods are evaluated to find theoptimal way to insert conventional covariates into deep prediction networks.Correlation analysis between autoencoder latent codes and covariate features isused to examine how these predictors interact. We believe that similarapproaches could also be used to introduce knowledge of genetic variants tosuch survival networks to improve outcome prediction by jointly analysingcardiac motion traits with inheritable risk factors.
Orini M, Graham AJ, MartinezNaharro A, et al., 2019, Noninvasive mapping of the electrophysiological substrate in cardiac amyloidosis and its relationship to structural abnormalities, Journal of the American Heart Association, Vol: 8, ISSN: 2047-9980
BackgroundThe relationship between structural pathology and electrophysiological substrate in cardiac amyloidosis is unclear. Differences between light‐chain (AL) and transthyretin (ATTR) cardiac amyloidosis may have prognostic implications.Methods and ResultsECG imaging and cardiac magnetic resonance studies were conducted in 21 cardiac amyloidosis patients (11 AL and 10 ATTR). Healthy volunteers were included as controls. With respect to ATTR, AL patients had lower amyloid volume (51.0/37.7 versus 73.7/16.4 mL, P=0.04), lower myocardial cell volume (42.6/19.1 versus 58.5/17.2 mL, P=0.021), and higher T1 (1172/64 versus 1109/80 ms, P=0.022) and T2 (53.4/2.9 versus 50.0/3.1 ms, P=0.003). ECG imaging revealed differences between cardiac amyloidosis and control patients in virtually all conduction‐repolarization parameters. With respect to ATTR, AL patients had lower epicardial signal amplitude (1.07/0.46 versus 1.83/1.26 mV, P=0.026), greater epicardial signal fractionation (P=0.019), and slightly higher dispersion of repolarization (187.6/65 versus 158.3/40 ms, P=0.062). No significant difference between AL and ATTR patients was found using the standard 12‐lead ECG. T1 correlated with epicardial signal amplitude (cc=−0.78), and extracellular volume with epicardial signal fractionation (cc=0.48) and repolarization time (cc=0.43). Univariate models based on single features from both cardiac magnetic resonance and ECG imaging classified AL and ATTR patients with an accuracy of 70% to 80%.ConclusionsIn this exploratory study cardiac amyloidosis was associated with ventricular conduction and repolarization abnormalities, which were more pronounced in AL than in ATTR. Combined ECG imaging–cardiac magnetic resonance analysis supports the hypothesis that additional mechanisms beyond infiltration may contribute to myocardial damage in AL amyloidosis. Further studies are needed to assess the clinical impact of this approach.
Duan J, Bello G, Schlemper J, et al., 2019, Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach, IEEE Transactions on Medical Imaging, Vol: 38, Pages: 2151-2164, ISSN: 0278-0062
Deep learning approaches have achieved state-of-the-art performance incardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular3D models, despite the artefacts in input CMR volumes.
Biffi C, Cerrolaza JJ, Tarroni G, et al., 2019, 3D high-resolution cardiac segmentation reconstruction from 2D views using conditional variational autoencoders, 16th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 1643-1646, ISSN: 1945-7928
Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imaging but are time-consuming, expensive to acquire and they often require long breath holds that are not suitable for patients. Consequently, multiplanar breath-hold 2D cines sequences are standard practice but are disadvantaged by lack of whole-heart coverage and low through-plane resolution. To address this, we propose a conditional variational autoencoder architecture able to learn a generative model of 3D high-resolution left ventricular (LV) segmentations which is conditioned on three 2D LV segmentations of one short-axis and two long-axis images. By only employing these three 2D segmentations, our model can efficiently reconstruct the 3D high-resolution LV segmentation of a subject. When evaluated on 400 unseen healthy volunteers, our model yielded an average Dice score of 87.92 ± 0.15 and outperformed competing architectures (TL-net, Dice score = 82.60 ± 0.23, p = 2.2 · 10 -16 ).
Garcia-Pavia P, Kim Y, Restrepo-Cordoba MA, et al., 2019, Genetic variants associated with cancer therapy-induced cardiomyopathy, Circulation, Vol: 140, Pages: 31-41, ISSN: 0009-7322
BackgroundCancer therapy-induced cardiomyopathy (CCM) is associated with cumulative drug exposures and pre-existing cardiovascular disorders. These parametersincompletely account for substantial inter-individual susceptibility to CCM. We hypothesized that rare variants in cardiomyopathy genes contribute to CCM.MethodsWe studied 213 CCM patients from three cohorts: retrospectively recruited adults with diverse cancers (n=99), prospectively phenotyped breast cancer adults (n=73) and prospectively phenotyped children with acute myeloid leukemia (n=41). Cardiomyopathy genes, including nine pre-specified genes were sequenced. The prevalence of rare variants was compared between CCM cohorts and The Cancer Genome Atlas (TCGA) participants(n=2053), healthy volunteers(n=445), and ancestry-matchedreference population. Clinical characteristics and outcomes were assessed, stratified by genotypes. A prevalent CCM genotype was modeled in anthracycline-treated mice.ResultsCCM was diagnosed 0.4-9 years after chemotherapy; 90% of these patients received anthracyclines. Adult CCM patients had cardiovascular risk factors similar to the U.S. population. Among nine prioritized genes CCM patients had more rare protein-altering variants than comparative cohorts (p≤1.98e-04). Titin-truncating variants (TTNtv) predominated, occurring in 7.5% CCM patients versus 1.1% TCGA participants (p=7.36e-08), 0.7% healthy volunteers (p=3.42e-06), and 0.6% reference population (p=5.87e-14). Adult CCM patients with TTNtv experienced more heart failure and atrial fibrillation (p=0.003)and impaired myocardial recovery (p=0.03) than those without.Consistent with human data, anthracycline-treated TTNtv mice and isolated TTNtv cardiomyocytes showed sustained contractile dysfunction unlike wildtype (p=0.0004 and p<0.002, respectively).ConclusionsUnrecognized rare variants in cardiomyopathy-associated genes, particularly TTNtv, increased the risk for CCM in children and adults, and adverse cardiac events
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