Fractal analysis of right ventricular trabeculae in pulmonary hypertension

Purpose: To measure right ventricular (RV) trabecular complexity by its fractal dimension (FD) in healthy subjects and patients with pulmonary hypertension (PH) and assess its relationship to hemodynamic and functional parameters, and future cardiovascular events. Materials and methods: This retrospective study used data acquired from May 2004 to October 2013 for 256 patients with newly-diagnosed PH that underwent cardiac magnetic resonance (CMR) imaging, right heart catheterization and six-minute walk distance testing with a median follow-up of 4.0 years. 256 healthy controls underwent CMR only. Biventricular FD, volumes and function were assessed on short-axis cine images. Reproducibility was assessed by intraclass correlation coefficient, correlation between variables was assessed by Pearson’s correlation test, and mortality prediction compared by univariable and multivariable Cox regression analysis. Results: RV-FD reproducibility had an intraclass correlation coefficient of 0.97 (95% confidence interval [CI]: 0.96, 0.98). RV-FD was higher in PH patients than healthy subjects (median 1.310, inter-quartile range [IQR] 1.281-1.341 vs 1.264, 1.242-1.295, P <.001) with the greatest difference near the apex. RV-FD was associated with pulmonary vascular resistance (r=0.30, P <.001). In univariable Cox regression analysis, RV-FD was a significant predictor of death (hazards ratio [HR]: 1.256, CI: 1.011, 1.560, P =.04), but in a multivariable analysis did not predict survival independently of conventional parameters of RV remodeling (HR: 1.179, CI: 0.871, 1.596, P =0.29). Conclusion: Fractal analysis of RV trabecular complexity is a highly reproducible measure of remodeling in PH associated with afterload, although the gain in survival prediction over traditional markers is not significant

Multiview Motion Estimation for 3D cardiac motion tracking

Code for paper ''MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI''Code for paper ''MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI''

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimizing the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimized for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients, the predictive accuracy (quantified by Harrell’s C-index) was significantly higher (P = 0.0012) for our model C = 0.75 (95% CI: 0.70–0.79) than the human benchmark of C = 0.59 (95% CI: 0.53–0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: Three dimensional analysis of cardiac magnetic resonance imaging

Aims We sought to identify metabolic pathways associated with right ventricular (RV) adaptation to pulmonary hypertension (PH). We evaluated candidate metabolites, previously associated with survival in pulmonary arterial hypertension, and used automated image segmentation and parametric mapping to model their relationship to adverse patterns of remodelling and wall stress. Methods and results In 312 PH subjects (47.1% female, mean age 60.8 ± 15.9 years), of which 182 (50.5% female, mean age 58.6 ± 16.8 years) had metabolomics, we modelled the relationship between the RV phenotype, haemodynamic state, and metabolite levels. Atlas-based segmentation and co-registration of cardiac magnetic resonance imaging was used to create a quantitative 3D model of RV geometry and function—including maps of regional wall stress. Increasing mean pulmonary artery pressure was associated with hypertrophy of the basal free wall (β = 0.29) and reduced relative wall thickness (β = −0.38), indicative of eccentric remodelling. Wall stress was an independent predictor of all-cause mortality (hazard ratio = 1.27, P = 0.04). Six metabolites were significantly associated with elevated wall stress (β = 0.28–0.34) including increased levels of tRNA-specific modified nucleosides and fatty acid acylcarnitines, and decreased levels (β = −0.40) of sulfated androgen. Conclusion Using computational image phenotyping, we identify metabolic profiles, reporting on energy metabolism and cellular stress-response, which are associated with adaptive RV mechanisms to PH

Environmental and genetic predictors of human cardiovascular ageing

Cardiovascular ageing is a process that begins early in life and leads to a progressive change in structure and decline in function due to accumulated damage across diverse cell types, tissues and organs contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence and end-organ damage, however the genetic architecture of cardiovascular ageing is not known. Here we use machine learning approaches to quantify cardiovascular age from image-derived traits of vascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing is found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated by cardiometabolic risk factors and we also identify prescribed medications that are potential modifiers of ageing. Through large-scale modelling of ageing across multiple traits our results reveal insights into the mechanisms driving premature cardiovascular ageing and reveal potential molecular targets to attenuate age-related processes

Phenotypic expression and outcomes in individuals with rare genetic variants of hypertrophic cardiomyopathy

Background: Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomereencoding genes, but little is known about the clinical significance of these variants in the general population. Objectives: To compare lifetime outcomes and cardiovascular phenotypes according to the presence 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-associated sarcomere-encoding genes in 200,584 participants was 2.9% (n=5,712; 1 in 35), and the prevalence of variants pathogenic or likely pathogenic for HCM (SARC-HCM-P/LP) was 0.25% (n=493, 1 in 407). SARC-HCM-P/LP variants were associated with increased risk of death 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). In 21,322 participants with CMR, SARC-HCM-P/LP were associated with asymmetric increase in left ventricular maximum wall thickness (10.9±2.7 vs 9.4±1.6 mm, p<0.001) but hypertrophy (≥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 low aggregate penetrance for overt HCM but are associated with increased risk of adverse cardiovascular outcomes and an attenuated cardiomyopathic phenotype. Although absolute event rates are low, identification of these variants may enhance risk stratification beyond familial disease

A genotype-phenotype taxonomy of hypertrophic cardiomyopathy

Background: Hypertrophic 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. Methods: We 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. Results: Carriers of pathogenic or likely pathogenic variants (P/LP) for HCM 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 (trustworthiness M1: 0.86-0.88). Conclusions: We 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