Predicting Peri-Operative Cardiorespiratory Adverse Events in Children with Idiopathic Pulmonary Arterial Hypertension Undergoing Cardiac Catheterization Using Echocardiography: A Cohort Study.

General anesthesia in children with idiopathic pulmonary arterial hypertension (PAH) carries an increased risk of peri-operative cardiorespiratory complications though risk stratifying individual children pre-operatively remains difficult. We report the incidence and echocardiographic risk factors for adverse events in children with PAH undergoing general anesthesia for cardiac catheterization. Echocardiographic, hemodynamic, and adverse event data from consecutive PAH patients are reported. A multivariable predictive model was developed from echocardiographic variables identified by Bayesian univariable logistic regression. Model performance was reported by area under the curve for receiver operating characteristics (AUCroc) and precision/recall (AUCpr) and a pre-operative scoring system derived (0-100). Ninety-three children underwent 158 cardiac catheterizations with mean age 8.8 ± 4.6 years. Adverse events (n = 42) occurred in 15 patients (16%) during 16 catheterizations (10%) including cardiopulmonary resuscitation (n = 5, 3%), electrocardiographic changes (n = 3, 2%), significant hypotension (n = 2, 1%), stridor (n = 1, 1%), and death (n = 2, 1%). A multivariable model (age, right ventricular dysfunction, and dilatation, pulmonary and tricuspid regurgitation severity, and maximal velocity) was highly predictive of adverse events (AUCroc 0.86, 95% CI 0.75 to 1.00; AUCpr 0.68, 95% CI 0.50 to 0.91; baseline AUCpr 0.10). Pre-operative risk scores were higher in those who had a subsequent adverse event (median 47, IQR 43 to 53) than in those who did not (median 23, IQR 15 to 33). Pre-operative echocardiography informs the risk of peri-operative adverse events and may therefore be useful both for consent and multi-disciplinary care planning.

Phosphodiesterase 5 inhibitor treatment and survival in interstitial lung disease pulmonary hypertension: A Bayesian retrospective observational cohort study.

BACKGROUND AND OBJECTIVE: Pulmonary hypertension is a life-limiting complication of interstitial lung disease (ILD-PH). We investigated whether treatment with phosphodiesterase 5 inhibitors (PDE5i) in patients with ILD-PH was associated with improved survival. METHODS: Consecutive incident patients with ILD-PH and right heart catheterisation, echocardiography and spirometry data were followed from diagnosis to death, transplantation or censoring with all follow-up and survival data modelled by Bayesian methods. RESULTS: The diagnoses in 128 patients were idiopathic pulmonary fibrosis (n = 74, 58%), hypersensitivity pneumonitis (n = 17, 13%), non-specific interstitial pneumonia (n = 12, 9%), undifferentiated ILD (n = 8, 6%) and other lung diseases (n = 17, 13%). Final outcomes were death (n = 106, 83%), transplantation (n = 9, 7%) and censoring (n = 13, 10%). Patients treated with PDE5i (n = 50, 39%) had higher mean pulmonary artery pressure (median 38 mm Hg [interquartile range, IQR: 34, 43] vs. 35 mm Hg [IQR: 31, 38], p = 0.07) and percentage predicted forced vital capacity (FVC; median 57% [IQR: 51, 73] vs. 52% [IQR: 45, 66], p=0.08) though differences did not reach significance. Patients treated with PDE5i survived longer than untreated patients (median 2.18 years [95% CI: 1.43, 3.04] vs. 0.94 years [0.69, 1.51], p = 0.003) independent of all other prognostic markers by Bayesian joint-modelling (HR 0.39, 95% CI: 0.23, 0.59, p < 0.001) and propensity-matched analyses (HR 0.38, 95% CI: 0.22, 0.58, p < 0.001). Survival difference with treatment was significantly larger if right ventricular function was normal, rather than abnormal, at presentation (+2.55 years, 95% CI: -0.03, +3.97 vs. +0.98 years, 95% CI: +0.47, +2.00, p = 0.04). CONCLUSION: PDE5i treatment in ILD-PH should be investigated by a prospective randomized trial.

Phenotypic Expression and Outcomes in Individuals With Rare Genetic Variants of Hypertrophic Cardiomyopathy.

BACKGROUND: 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. OBJECTIVES: The goal of this study was to compare lifetime outcomes and cardiovascular phenotypes according to the presence of rare variants in sarcomere-encoding genes among middle-aged adults. METHODS: This study analyzed whole exome sequencing and cardiac magnetic resonance imaging in UK Biobank participants stratified according to 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 an increased risk of death or major adverse cardiac events compared with controls (hazard ratio: 1.69; 95% confidence interval [CI]: 1.38-2.07; P < 0.001), mainly due to heart failure endpoints (hazard ratio: 4.23; 95% CI: 3.07-5.83; P < 0.001). In 21,322 participants with both cardiac magnetic resonance imaging and whole exome sequencing, SARC-HCM-P/LP variants were associated with an asymmetric increase in left ventricular maximum wall thickness (10.9 ± 2.7 mm vs 9.4 ± 1.6 mm; P < 0.001), but hypertrophy (≥13 mm) was only present in 18.4% (n = 9 of 49; 95% CI: 9%-32%). SARC-HCM-P/LP variants were still associated with heart failure after adjustment for wall thickness (hazard ratio: 6.74; 95% CI: 2.43-18.7; 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 an 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.

Genetic and functional insights into the fractal structure of the heart.

The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a remnant of embryonic development1,2. The function of these trabeculae in adults and their genetic architecture are unknown. Here we performed a genome-wide association study to investigate image-derived phenotypes of trabeculae using the fractal analysis of trabecular morphology in 18,096 participants of the UK Biobank. We identified 16 significant loci that contain genes associated with haemodynamic phenotypes and regulation of cytoskeletal arborization3,4. Using biomechanical simulations and observational data from human participants, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Through genetic association studies with cardiac disease phenotypes and Mendelian randomization, we find a causal relationship between trabecular morphology and risk of cardiovascular disease. These findings suggest a previously unknown role for myocardial trabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity and reveal the influence of the myocardial trabeculae on susceptibility to cardiovascular disease.

Reevaluating the Genetic Contribution of Monogenic Dilated Cardiomyopathy.

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 patients with DCM 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 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 patients with DCM and 912 healthy volunteers processed with identical sequencing and bioinformatics pipelines. We further aggregated data from 1498 patients with DCM sequenced in diagnostic laboratories and the Exome Aggregation Consortium 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 patients with DCM and 26% of cases in the diagnostic referral cohort enriched in familial and early-onset DCM. Although the absence of a significant excess in other genes cannot preclude a limited role in disease, such genes have limited diagnostic value because novel variants will be uninterpretable and their diagnostic yield is minimal. CONCLUSIONS: 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 other genes analyzed here will need to be rigorously evaluated in ongoing curation efforts to determine their validity as Mendelian DCM genes but have limited value in diagnostic testing in DCM at present. This data will contribute to community gene curation efforts and will reduce erroneous and inconclusive findings in diagnostic testing.

Sex and regional differences in myocardial plasticity in aortic stenosis are revealed by 3D model machine learning.

AIMS: Left ventricular hypertrophy (LVH) in aortic stenosis (AS) varies widely before and after aortic valve replacement (AVR), and deeper phenotyping beyond traditional global measures may improve risk stratification. We hypothesized that machine learning derived 3D LV models may provide a more sensitive assessment of remodelling and sex-related differences in AS than conventional measurements. METHODS AND RESULTS: One hundred and sixteen patients with severe, symptomatic AS (54% male, 70 ± 10 years) underwent cardiovascular magnetic resonance pre-AVR and 1 year post-AVR. Computational analysis produced co-registered 3D models of wall thickness, which were compared with 40 propensity-matched healthy controls. Preoperative regional wall thickness and post-operative percentage wall thickness regression were analysed, stratified by sex. AS hypertrophy and regression post-AVR was non-uniform-greatest in the septum with more pronounced changes in males than females (wall thickness regression: -13 ± 3.6 vs. -6 ± 1.9%, respectively, P < 0.05). Even patients without LVH (16% with normal indexed LV mass, 79% female) had greater septal and inferior wall thickness compared with controls (8.8 ± 1.6 vs. 6.6 ± 1.2 mm, P < 0.05), which regressed post-AVR. These differences were not detectable by global measures of remodelling. Changes to clinical parameters post-AVR were also greater in males: N-terminal pro-brain natriuretic peptide (NT-proBNP) [-37 (interquartile range -88 to -2) vs. -1 (-24 to 11) ng/L, P = 0.008], and systolic blood pressure (12.9 ± 23 vs. 2.1 ± 17 mmHg, P = 0.009), with changes in NT-proBNP correlating with percentage LV mass regression in males only (ß 0.32, P = 0.02). CONCLUSION: In patients with severe AS, including those without overt LVH, LV remodelling is most plastic in the septum, and greater in males, both pre-AVR and post-AVR. Three-dimensional machine learning is more sensitive than conventional analysis to these changes, potentially enhancing risk stratification. CLINICAL TRIAL REGISTRATION: Regression of myocardial fibrosis after aortic valve replacement (RELIEF-AS); NCT02174471. https://clinicaltrials.gov/ct2/show/NCT02174471.

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. Optimising 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 optimised 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 = .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.

Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach.

Deep learning approaches have achieved state-of-the-art performance in cardiac 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-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localization 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, a refinement step is designed to explicitly impose shape prior knowledge and improve segmentation quality. This step is effective for overcoming image artifacts (e.g., due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialize 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-ventricular 3D models, despite the presence of artifacts in input CMR volumes.

Artificial Intelligence for Cardiac Imaging-Genetics Research.

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.

Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: 3D 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 (ß = 0.29) and reduced relative wall thickness (ß = -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 (ß = 0.28-0.34) including increased levels of tRNA-specific modified nucleosides and fatty acid acylcarnitines, and decreased levels (ß = -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.

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 to assess its relationship with hemodynamic and functional parameters and future cardiovascular events. Materials and Methods This retrospective study used data acquired from May 2004 to October 2013 in 256 patients with newly diagnosed PH who underwent cardiac MRI, right-sided heart catheterization, and 6-minute walk distance testing, with median follow-up of 4.0 years. A total of 256 healthy control subjects underwent cardiac MRI only. Biventricular FD, volumes, and function were assessed on short-axis cine images. Reproducibility was assessed with the intraclass correlation coefficient, correlation between variables was assessed with the Pearson correlation test, and mortality prediction was compared by using uni- and multivariable Cox regression analyses. 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 patients with PH (median, 1.310; interquartile range [IQR], 1.281-1.341) than in healthy subjects (median, 1.264; IQR, 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). At univariable Cox regression analysis, RV FD was a significant predictor of death (hazard ratio [HR], 1.256; 95% CI: 1.011, 1.560; P = .04); however, at multivariable analysis, RV FD did not enable prediction of survival independently of conventional parameters of RV remodeling (HR, 1.179; 95% CI: 0.871, 1.596; P = .29). Conclusion Fractal analysis of RV trabecular complexity is a highly reproducible measure of remodeling in patients with PH that is associated with afterload, although the gain in survival prediction over traditional markers is not significant. Published under a CC BY 4.0 license. Online supplemental material is available for this article.

Three-dimensional cardiovascular imaging-genetics: a mass univariate framework.

Motivation: Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for high-throughput mapping of genotype-phenotype associations in three dimensions (3D). Results: High-resolution cardiac magnetic resonance images were automatically segmented in 1124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts. Availability and implementation: The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work. Contact: declan.oregan@imperial.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study.

Purpose To determine if patient survival and mechanisms of right ventricular failure in pulmonary hypertension could be predicted by using supervised machine learning of three-dimensional patterns of systolic cardiac motion. Materials and Methods The study was approved by a research ethics committee, and participants gave written informed consent. Two hundred fifty-six patients (143 women; mean age ± standard deviation, 63 years ± 17) with newly diagnosed pulmonary hypertension underwent cardiac magnetic resonance (MR) imaging, right-sided heart catheterization, and 6-minute walk testing with a median follow-up of 4.0 years. Semiautomated segmentation of short-axis cine images was used to create a three-dimensional model of right ventricular motion. Supervised principal components analysis was used to identify patterns of systolic motion that were most strongly predictive of survival. Survival prediction was assessed by using difference in median survival time and area under the curve with time-dependent receiver operating characteristic analysis for 1-year survival. Results At the end of follow-up, 36% of patients (93 of 256) died, and one underwent lung transplantation. Poor outcome was predicted by a loss of effective contraction in the septum and free wall, coupled with reduced basal longitudinal motion. When added to conventional imaging and hemodynamic, functional, and clinical markers, three-dimensional cardiac motion improved survival prediction (area under the receiver operating characteristic curve, 0.73 vs 0.60, respectively; P < .001) and provided greater differentiation according to difference in median survival time between high- and low-risk groups (13.8 vs 10.7 years, respectively; P < .001). Conclusion A machine-learning survival model that uses three-dimensional cardiac motion predicts outcome independent of conventional risk factors in patients with newly diagnosed pulmonary hypertension. Online supplemental material is available for this article.

Titin-truncating variants affect heart function in disease cohorts and the general population.

Titin-truncating variants (TTNtv) commonly cause dilated cardiomyopathy (DCM). TTNtv are also encountered in ∼1% of the general population, where they may be silent, perhaps reflecting allelic factors. To better understand TTNtv, we integrated TTN allelic series, cardiac imaging and genomic data in humans and studied rat models with disparate TTNtv. In patients with DCM, TTNtv throughout titin were significantly associated with DCM. Ribosomal profiling in rat showed the translational footprint of premature stop codons in Ttn, TTNtv-position-independent nonsense-mediated degradation of the mutant allele and a signature of perturbed cardiac metabolism. Heart physiology in rats with TTNtv was unremarkable at baseline but became impaired during cardiac stress. In healthy humans, machine-learning-based analysis of high-resolution cardiac imaging showed TTNtv to be associated with eccentric cardiac remodeling. These data show that TTNtv have molecular and physiological effects on the heart across species, with a continuum of expressivity in health and disease.

Moderate Physical Activity in Healthy Adults Is Associated With Cardiac Remodeling.

BACKGROUND: Cardiac mass and volumes are often elevated in athletes, but it is not known whether moderate physical activity is also associated with cardiac dilatation and hypertrophy in a healthy adult population. METHODS AND RESULTS: In total, 1096 adults (54% female, median age 39 years) without cardiovascular disease or cardiomyopathy-associated genetic variants underwent cardiac magnetic resonance imaging to determine biventricular volumes and function. Physical activity was assessed using a validated activity questionnaire. The relationship between cardiac parameters and activity was assessed using multiple linear regression adjusting for age, sex, race, and systolic blood pressure. Logistic regression was performed to determine the effect of activity on the likelihood of subjects having cardiac dilatation or hypertrophy according to standard cardiac magnetic resonance normal ranges. Increasing physical activity was associated with greater left ventricular (LV) mass (ß=0.23; P<0.0001) and elevated LV and right ventricular volumes (LV: ß=0.26, P<0.0001; right ventricular: ß=0.26, P<0.0001). Physical activity had a larger effect on cardiac parameters than systolic blood pressure (0.06≤ß≤0.21) and a similar effect to age (-0.20≤ß≤-0.31). Increasing physical activity was a risk factor for meeting imaging criteria for LV hypertrophy (adjusted odds ratio 2.1; P<0.0001), LV dilatation (adjusted odds ratio 2.2; P<0.0001), and right ventricular dilatation (adjusted odds ratio 2.2; P<0.0001). CONCLUSIONS: Exercise-related cardiac remodeling is not confined to athletes, and there is a risk of overdiagnosing cardiac dilatation or hypertrophy in a proportion of active, healthy adults.

Pulmonary Artery Stiffness Is Independently Associated with Right Ventricular Mass and Function: A Cardiac MR Imaging Study.

Purpose To determine the relationship between pulmonary artery (PA) stiffness and both right ventricular (RV) mass and function with cardiac magnetic resonance (MR) imaging. Materials and Methods The study was approved by the local research ethics committee, and all participants gave written informed consent. Cardiac MR imaging was performed at 1.5 T in 156 healthy volunteers (63% women; age range, 19-61 years; mean age, 36.1 years). High-temporal-resolution phase-contrast imaging was performed in the main and right PAs. Pulmonary pulse wave velocity (PWV) was determined by the interval between arterial systolic upslopes. RV function was assessed with feature tracking to derive peak systolic strain and strain rate, as well as peak early-diastolic strain rate. RV volumes, ejection fraction (RVEF), and mass were measured from the cine images. The association of pulmonary PWV with RV function and mass was quantified with univariate linear regression. Interstudy repeatability was assessed with intraclass correlation. Results The repeatability coefficient for pulmonary PWV was 0.96. Increases in pulmonary PWV and RVEF were associated with increases in age (r = 0.32, P < .001 and r = 0.18, P = .025, respectively). After adjusting for age (P = .090), body surface area (P = .073), and sex (P = .005), pulmonary PWV demonstrated an independent positive association with RVEF (r = 0.34, P = .026). Significant associations were also seen with RV mass (r = 0.41, P = .004), RV radial strain (r = 0.38, P = .022), and strain rate (r = 0.35, P = .002), and independent negative associations were seen with radial (r = 0.27, P = .003), longitudinal (r = 0.40, P = .007), and circumferential (r = 0.31, P = .005) peak early-diastolic strain rate with the same covariates. Conclusion Pulmonary PWV is reliably assessed with cardiac MR imaging. In subjects with no known cardiovascular disease, increasing PA stiffness is associated with increasing age and is also moderately associated with both RV mass and function after controlling for age, body surface area, and sex. (©) RSNA, 2016 Online supplemental material is available for this article.

Precursors of Hypertensive Heart Phenotype Develop in Healthy Adults: A High-Resolution 3D MRI Study.

OBJECTIVES: This study used high-resolution 3-dimensional cardiac magnetic resonance to define the anatomical and functional left ventricular (LV) properties associated with increasing systolic blood pressure (SBP) in a drug-naïve cohort. BACKGROUND: LV hypertrophy and remodeling occur in response to hemodynamic stress but little is known about how these phenotypic changes are initiated in the general population. METHODS: In this study, 1,258 volunteers (54% women, mean age 40.6 ± 12.8 years) without self-reported cardiovascular disease underwent 3-dimensional cardiac magnetic resonance combined with computational modeling. The relationship between SBP and wall thickness (WT), relative WT, end-systolic wall stress (WS), and fractional wall thickening were analyzed using 3-dimensional regression models adjusted for body surface area, sex, race, age, and multiple testing. Significantly associated points in the LV model (p < 0.05) were identified and the relationship with SBP reported as mean ß coefficients. RESULTS: There was a continuous relationship between SBP and asymmetric concentric hypertrophic adaptation of the septum and anterior wall that was associated with normalization of wall stress. In the lateral wall an increase in wall stress with rising SBP was not balanced by a commensurate hypertrophic relationship. In normotensives, SBP was positively associated with WT (ß = 0.09) and relative WT (ß = 0.07) in the septal and anterior walls, and this regional hypertrophic relationship was progressively stronger among pre-hypertensives (ß = 0.10) and hypertensives (ß = 0.30). CONCLUSIONS: These findings show that the precursors of the hypertensive heart phenotype can be traced to healthy normotensive adults and that an independent and continuous relationship exists between adverse LV remodeling and SBP in a low-risk population. These adaptations show distinct regional variations with concentric hypertrophy of the septum and eccentric hypertrophy of the lateral wall, which challenge conventional classifications of LV remodeling.

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion.

Atlases encode valuable anatomical and functional information from a population. In this work, a bi-ventricular cardiac atlas was built from a unique data set, which consists of high resolution cardiac MR images of 1000+ normal subjects. Based on the atlas, statistical methods were used to study the variation of cardiac shapes and the distribution of cardiac motion across the spatio-temporal domain. We have shown how statistical parametric mapping (SPM) can be combined with a general linear model to study the impact of gender and age on regional myocardial wall thickness. Finally, we have also investigated the influence of the population size on atlas construction and atlas-based analysis. The high resolution atlas, the statistical models and the SPM method will benefit more studies on cardiac anatomy and function analysis in the future.

Integrated allelic, transcriptional, and phenomic dissection of the cardiac effects of titin truncations in health and disease.

The recent discovery of heterozygous human mutations that truncate full-length titin (TTN, an abundant structural, sensory, and signaling filament in muscle) as a common cause of end-stage dilated cardiomyopathy (DCM) promises new prospects for improving heart failure management. However, realization of this opportunity has been hindered by the burden of TTN-truncating variants (TTNtv) in the general population and uncertainty about their consequences in health or disease. To elucidate the effects of TTNtv, we coupled TTN gene sequencing with cardiac phenotyping in 5267 individuals across the spectrum of cardiac physiology and integrated these data with RNA and protein analyses of human heart tissues. We report diversity of TTN isoform expression in the heart, define the relative inclusion of TTN exons in different isoforms (using the TTN transcript annotations available at http://cardiodb.org/titin), and demonstrate that these data, coupled with the position of the TTNtv, provide a robust strategy to discriminate pathogenic from benign TTNtv. We show that TTNtv is the most common genetic cause of DCM in ambulant patients in the community, identify clinically important manifestations of TTNtv-positive DCM, and define the penetrance and outcomes of TTNtv in the general population. By integrating genetic, transcriptome, and protein analyses, we provide evidence for a length-dependent mechanism of disease. These data inform diagnostic criteria and management strategies for TTNtv-positive DCM patients and for TTNtv that are identified as incidental findings.