Automated spatially targeted optical micro proteomics identifies fibroblast- and macrophage-specific regulation of myocardial infarction scar maturation in rats.

Myocardial infarction (MI) results from occlusion of blood supply to the heart muscle causing death of cardiac muscle cells. Following myocardial infarction (MI), extracellular matrix deposition and scar formation mechanically stabilize the injured heart as damaged myocytes undergo necrosis and removal. Fibroblasts and macrophages are key drivers of post-MI scar formation, maturation, and ongoing long-term remodelling; however, their individual contributions are difficult to assess from bulk analyses of infarct scar. Here, we employ state-of-the-art automated spatially targeted optical micro proteomics (autoSTOMP) to photochemically tag and isolate proteomes associated with subpopulations of fibroblasts (SMA+) and macrophages (CD68+) in the context of the native, MI tissue environment. Over a time course of 6-weeks post-MI, we captured dynamic changes in the whole-infarct proteome and determined that some of these protein composition signatures were differentially localized near SMA+ fibroblasts or CD68+ macrophages within the scar region. These results link specific cell populations to within-infarct protein remodelling and illustrate the distinct metabolic and structural processes underlying the observed physiology of each cell type.

Machine learning for multidimensional response and survival after cardiac resynchronization therapy using features from cardiac magnetic resonance.

Background: Cardiac resynchronization therapy (CRT) response is complex, and better approaches are required to predict survival and need for advanced therapies. Objective: The objective was to use machine learning to characterize multidimensional CRT response and its relationship with long-term survival. Methods: Associations of 39 baseline features (including cardiac magnetic resonance [CMR] findings and clinical parameters such as glomerular filtration rate [GFR]) with a multidimensional CRT response vector (consisting of post-CRT left ventricular end-systolic volume index [LVESVI] fractional change, post-CRT B-type natriuretic peptide, and change in peak VO2) were evaluated. Machine learning generated response clusters, and cross-validation assessed associations of clusters with 4-year survival. Results: Among 200 patients (median age 67.4 years, 27.0% women) with CRT and CMR, associations with more than 1 response parameter were noted for the CMR CURE-SVD dyssynchrony parameter (associated with post-CRT brain natriuretic peptide [BNP] and LVESVI fractional change) and GFR (associated with peak VO2 and post-CRT BNP). Machine learning defined 3 response clusters: cluster 1 (n = 123, 90.2% survival [best]), cluster 2 (n = 45, 60.0% survival [intermediate]), and cluster 3 (n = 32, 34.4% survival [worst]). Adding the 6-month response cluster to baseline features improved the area under the receiver operating characteristic curve for 4-year survival from 0.78 to 0.86 (P = .02). A web-based application was developed for cluster determination in future patients. Conclusion: Machine learning characterizes distinct CRT response clusters influenced by CMR features, kidney function, and other factors. These clusters have a strong and additive influence on long-term survival relative to baseline features.

Cardiac magnetic resonance defines mechanisms of sex-based differences in outcomes following cardiac resynchronization therapy.

Background: Mechanisms of sex-based differences in outcomes following cardiac resynchronization therapy (CRT) are poorly understood. Objective: To use cardiac magnetic resonance (CMR) to define mechanisms of sex-based differences in outcomes after CRT and describe distinct CMR-based phenotypes of CRT candidates based on sex and non-ischemic/ischemic cardiomyopathy type. Materials and methods: In a prospective study, sex-based differences in three short-term CRT response measures [fractional change in left ventricular end-systolic volume index 6 months after CRT (LVESVI-FC), B-type natriuretic peptide (BNP) 6 months after CRT, change in peak VO2 6 months after CRT], and long-term survival were evaluated with respect to 39 baseline parameters from CMR, exercise testing, laboratory testing, electrocardiograms, comorbid conditions, and other sources. CMR was also used to quantify the degree of left-ventricular mechanical dyssynchrony by deriving the circumferential uniformity ratio estimate (CURE-SVD) parameter from displacement encoding with stimulated echoes (DENSE) strain imaging. Statistical methods included multivariable linear regression with evaluation of interaction effects associated with sex and cardiomyopathy type (ischemic and non-ischemic cardiomyopathy) and survival analysis. Results: Among 200 patients, the 54 female patients (27%) pre-CRT had a smaller CMR-based LVEDVI (p = 0.04), more mechanical dyssynchrony based on the validated CMR CURE-SVD parameter (p = 0.04), a lower frequency of both late gadolinium enhancement (LGE) and ischemic cardiomyopathy (p < 0.0001), a greater RVEF (p = 0.02), and a greater frequency of LBBB (p = 0.01). After categorization of patients into four groups based on cardiomyopathy type (ischemic/non-ischemic cardiomyopathy) and sex, female patients with non-ischemic cardiomyopathy had the lowest CURE-SVD (p = 0.003), the lowest pre-CRT BNP levels (p = 0.01), the lowest post-CRT BNP levels (p = 0.05), and the most favorable LVESVI-FC (p = 0.001). Overall, female patients had better 3-year survival before adjustment for cardiomyopathy type (p = 0.007, HR = 0.45) and after adjustment for cardiomyopathy type (p = 0.009, HR = 0.67). Conclusion: CMR identifies distinct phenotypes of female CRT patients with non-ischemic and ischemic cardiomyopathy relative to male patients stratified by cardiomyopathy type. The more favorable short-term response and long-term survival outcomes in female heart failure patients with CRT were associated with lower indexed CMR-based LV volumes, decreased presence of scar associated with prior myocardial infarction and ICM, and greater CMR-based dyssynchrony with the CURE-SVD.

Multiscale model of heart growth during pregnancy: integrating mechanical and hormonal signaling.

Pregnancy stands at the interface of mechanics and biology. The growing fetus continuously loads the maternal organs as circulating hormone levels surge, leading to significant changes in mechanical and hormonal cues during pregnancy. In response, maternal soft tissues undergo remarkable growth and remodeling to support the mother and baby for a healthy pregnancy. We focus on the maternal left ventricle, which increases its cardiac output and mass during pregnancy. This study develops a multiscale cardiac growth model for pregnancy to understand how mechanical and hormonal cues interact to drive this growth process. We coupled a cell signaling network model that predicts cell-level hypertrophy in response to hormones and stretch to a compartmental model of the rat heart and circulation that predicts organ-level growth in response to hemodynamic changes. We calibrated this multiscale model to data from experimental volume overload and hormonal infusions of angiotensin 2 (AngII), estrogen (E2), and progesterone (P4). We then validated the model's ability to capture interactions between inputs by comparing model predictions against published observations for the combinations of VO + E2 and AngII + E2. Finally, we simulated pregnancy-induced changes in hormones and hemodynamics to predict heart growth during pregnancy. Our model produced growth consistent with experimental data. Overall, our analysis suggests that the rise in P4 during the first half of gestation is an important contributor to heart growth during pregnancy. We conclude with suggestions for future experimental studies that will provide a better understanding of how hormonal and mechanical cues interact to drive pregnancy-induced heart growth.

Individual variability in animal-specific hemodynamic compensation following myocardial infarction.

Ventricular enlargement and heart failure are common in patients who survive a myocardial infarction (MI). There is striking variability in the degree of post-infarction ventricular remodeling, however, and no one factor or set of factors have been identified that predicts heart failure risk well. Sympathetic activation directly and indirectly modulates hypertrophic stimuli by altering both neurohormonal milieu and ventricular loading. In a recent study, we developed a method to identify the balance of reflex compensatory mechanisms employed by individual animals following MI based on measured hemodynamics. Here, we conducted prospective studies of acute myocardial infarction in rats to test the degree of variability in reflex compensation as well as whether responses to pharmacologic agents targeted at those reflex mechanisms could be anticipated in individual animals. We found that individual animals use very different mixtures of reflex compensation in response to experimental coronary ligation. Some of these mechanisms were related - animals that compensated strongly with venoconstriction tended to exhibit a decrease in the contractility of the surviving myocardium and those that increased contractility tended to exhibit venodilation. Furthermore, some compensatory mechanisms - such as venoconstriction - increased the extent of predicted ventricular enlargement. Unfortunately, initial reflex responses to infarction were a poor predictor of subsequent responses to pharmacologic agents, suggesting that customizing pharmacologic therapy to individuals based on an initial response will be challenging.

The influence of boundary conditions and protein availability on the remodeling of cardiomyocytes.

The heart muscle is capable of growing and remodeling in response to changes in its mechanical and hormonal environment. While this capability is essential to the healthy function of the heart, under extreme conditions it may also lead to heart failure. In this work, we derive a thermodynamically based and microscopically motivated model that highlights the influence of mechanical boundary conditions and hormonal changes on the remodeling process in cardiomyocytes. We begin with a description of the kinematics associated with the remodeling process. Specifically, we derive relations between the macroscopic deformation, the number of sarcomeres, the sarcomere stretch, and the number of myofibrils in the cell. We follow with the derivation of evolution equations that describe the production and the degradation of protein in the cytosol. Next, we postulate a dissipation-based formulation that characterizes the remodeling process. We show that this process stems from a competition between the internal energy, the entropy, the energy supplied to the system by ATP and other sources, and dissipation mechanisms. To illustrate the merit of this framework, we study four initial and boundary conditions: (1) a myocyte undergoing isometric contractions in the presence of either an infinite or a limited supply of proteins and (2) a myocyte that is free to dilate along the radial direction with an infinite and a limited supply of proteins. This work underscores the importance of boundary conditions on the overall remodeling response of cardiomyocytes, suggesting a plausible mechanism that might play a role in distinguishing eccentric vs. concentric hypertrophy.

A rapid electromechanical model to predict reverse remodeling following cardiac resynchronization therapy.

Cardiac resynchronization therapy (CRT) is an effective therapy for patients who suffer from heart failure and ventricular dyssynchrony such as left bundle branch block (LBBB). When it works, it reverses adverse left ventricular (LV) remodeling and the progression of heart failure. However, CRT response rate is currently as low as 50-65%. In theory, CRT outcome could be improved by allowing clinicians to tailor the therapy through patient-specific lead locations, timing, and/or pacing protocol. However, this also presents a dilemma: there are far too many possible strategies to test during the implantation surgery. Computational models could address this dilemma by predicting remodeling outcomes for each patient before the surgery takes place. Therefore, the goal of this study was to develop a rapid computational model to predict reverse LV remodeling following CRT. We adapted our recently developed computational model of LV remodeling to simulate the mechanics of ventricular dyssynchrony and added a rapid electrical model to predict electrical activation timing. The model was calibrated to quantitatively match changes in hemodynamics and global and local LV wall mass from a canine study of LBBB and CRT. The calibrated model was used to investigate the influence of LV lead location and ischemia on CRT remodeling outcome. Our model results suggest that remodeling outcome varies with both lead location and ischemia location, and does not always correlate with short-term improvement in QRS duration. The results and time frame required to customize and run this model suggest promise for this approach in a clinical setting.

Automated Spatially Targeted Optical Microproteomics Investigates Inflammatory Lesions In Situ.

Tissue microenvironment properties like blood flow, extracellular matrix, or proximity to immune-infiltrate are important regulators of cell biology. However, methods to study regional protein expression in the native tissue environment are limited. To address this need, we developed a novel approach to visualize, purify, and measure proteins in situ using automated spatially targeted optical microproteomics (AutoSTOMP). Here, we report custom codes to specify regions of heterogeneity in a tissue section and UV-biotinylate proteins within those regions. We have developed liquid chromatography-mass spectrometry (LC-MS)/MS-compatible biochemistry to purify those proteins and label-free quantification methodology to determine protein enrichment in target cell types or structures relative to nontarget regions in the same sample. These tools were applied to (a) identify inflammatory proteins expressed by CD68+ macrophages in rat cardiac infarcts and (b) characterize inflammatory proteins enriched in IgG4+ lesions in human esophageal tissues. These data indicate that AutoSTOMP is a flexible approach to determine regional protein expression in situ on a range of primary tissues and clinical biopsies where current tools and sample availability are limited.

Cardiac Magnetic Resonance Assessment of Response to Cardiac Resynchronization Therapy and Programming Strategies.

OBJECTIVES: The objective was to determine the feasibility and effectiveness of cardiac magnetic resonance (CMR) cine and strain imaging before and after cardiac resynchronization therapy (CRT) for assessment of response and the optimal resynchronization pacing strategy. BACKGROUND: CMR with cardiac implantable electronic devices can safely provide high-quality right ventricular/left ventricular (LV) ejection fraction (RVEF/LVEF) assessments and strain. METHODS: CMR with cine imaging, displacement encoding with stimulated echoes for the circumferential uniformity ratio estimate with singular value decomposition (CURE-SVD) dyssynchrony parameter, and scar assessment was performed before and after CRT. Whereas the pre-CRT scan constituted a single "imaging set" with complete volumetric, strain, and scar imaging, multiple imaging sets with complete strain and volumetric data were obtained during the post-CRT scan for biventricular pacing (BIVP), LV pacing (LVP), and asynchronous atrial pacing modes by reprogramming the device outside the scanner between imaging sets. RESULTS: 100 CMRs with a total of 162 imaging sets were performed in 50 patients (median age 70 years [IQR: 50-86 years]; 48% female). Reduction in LV end-diastolic volumes (P = 0.002) independent of CRT pacing were more prominent than corresponding reductions in right ventricular end-diastolic volumes (P = 0.16). A clear dependence of the optimal CRT pacing mode (BIVP vs LVP) on the PR interval (P = 0.0006) was demonstrated. The LVEF and RVEF improved more with BIVP than LVP with PR intervals ≥240 milliseconds (P = 0.025 and P = 0.002, respectively); the optimal mode (BIVP vs LVP) was variable with PR intervals <240 milliseconds. A lower pre-CRT displacement encoding with stimulated echoes (DENSE) CURE-SVD was associated with greater improvements in the post-CRT CURE-SVD (r = -0.69; P < 0.001), LV end-systolic volume (r = -0.58; P < 0.001), and LVEF (r = -0.52; P < 0.001). CONCLUSIONS: CMR evaluation with assessment of multiple pacing modes during a single scan after CRT is feasible and provides useful information for patient care with respect to response and the optimal pacing strategy.

Mechano-chemo signaling interactions modulate matrix production by cardiac fibroblasts.

Extracellular matrix remodeling after myocardial infarction occurs in a dynamic environment in which local mechanical stresses and biochemical signaling species stimulate the accumulation of collagen-rich scar tissue. It is well-known that cardiac fibroblasts regulate post-infarction matrix turnover by secreting matrix proteins, proteases, and protease inhibitors in response to both biochemical stimuli and mechanical stretch, but how these stimuli act together to dictate cellular responses is still unclear. We developed a screen of cardiac fibroblast-secreted proteins in response to combinations of biochemical agonists and cyclic uniaxial stretch in order to elucidate the relationships between stretch, biochemical signaling, and cardiac matrix turnover. We found that stretch significantly synergized with biochemical agonists to inhibit the secretion of matrix metalloproteinases, with stretch either amplifying protease suppression by individual agonists or antagonizing agonist-driven upregulation of protease expression. Stretch also modulated fibroblast sensitivity towards biochemical agonists by either sensitizing cells towards agonists that suppress protease secretion or de-sensitizing cells towards agonists that upregulate protease secretion. These findings suggest that the mechanical environment can significantly alter fibrosis-related signaling in cardiac fibroblasts, suggesting caution when extrapolating in vitro data to predict effects of fibrosis-related cytokines in situations like myocardial infarction where mechanical stretch occurs.

The connexin 43 carboxyl terminal mimetic peptide αCT1 prompts differentiation of a collagen scar matrix in humans resembling unwounded skin.

Phase II clinical trials have reported that acute treatment of surgical skin wounds with the therapeutic peptide alpha Connexin Carboxy-Terminus 1 (αCT1) improves cutaneous scar appearance by 47% 9-month postsurgery. While Cx43 and ZO-1 have been identified as molecular targets of αCT1, the mode-of-action of the peptide in scar mitigation at cellular and tissue levels remains to be further characterized. Scar histoarchitecture in αCT1 and vehicle-control treated skin wounds within the same patient were compared using biopsies from a Phase I clinical trial at 29-day postwounding. The sole effect on scar structure of a range of epidermal and dermal variables examined was that αCT1-treated scars had less alignment of collagen fibers relative to control wounds-a characteristic that resembles unwounded skin. The with-in subject effect of αCT1 on scar collagen order observed in Phase I testing in humans was recapitulated in Sprague-Dawley rats and the IAF hairless guinea pig. Transient increase in histologic collagen density in response to αCT1 was also observed in both animal models. Mouse NIH 3T3 fibroblasts and primary human dermal fibroblasts treated with αCT1 in vitro showed more rapid closure in scratch wound assays, with individual cells showing decreased directionality in movement. An agent-based computational model parameterized with fibroblast motility data predicted collagen alignments in simulated scars consistent with that observed experimentally in human and the animal models. In conclusion, αCT1 prompts decreased directionality of fibroblast movement and the generation of a 3D collagen matrix postwounding that is similar to unwounded skin-changes that correlate with long-term improvement in scar appearance.

Network model-based screen for FDA-approved drugs affecting cardiac fibrosis.

Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and α-SMA expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFß expression and MMP2-driven TGFß activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drug-target mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.

A multiscale model of cardiac concentric hypertrophy incorporating both mechanical and hormonal drivers of growth.

Growth and remodeling in the heart is driven by a combination of mechanical and hormonal signals that produce different patterns of growth in response to exercise, pregnancy, and various pathologies. In particular, increases in afterload lead to concentric hypertrophy, a thickening of the walls that increases the contractile ability of the heart while reducing wall stress. In the current study, we constructed a multiscale model of cardiac hypertrophy that connects a finite-element model representing the mechanics of the growing left ventricle to a cell-level network model of hypertrophic signaling pathways that accounts for changes in both mechanics and hormones. We first tuned our model to capture published in vivo growth trends for isoproterenol infusion, which stimulates ß-adrenergic signaling pathways without altering mechanics, and for transverse aortic constriction (TAC), which involves both elevated mechanics and altered hormone levels. We then predicted the attenuation of TAC-induced hypertrophy by two distinct genetic interventions (transgenic Gq-coupled receptor inhibitor overexpression and norepinephrine knock-out) and by two pharmacologic interventions (angiotensin receptor blocker losartan and ß-blocker propranolol) and compared our predictions to published in vivo data for each intervention. Our multiscale model captured the experimental data trends reasonably well for all conditions simulated. We also found that when prescribing realistic changes in mechanics and hormones associated with TAC, the hormonal inputs were responsible for the majority of the growth predicted by the multiscale model and were necessary in order to capture the effect of the interventions for TAC.

A Comparison of Fiber Based Material Laws for Myocardial Scar.

The mechanics of most soft tissues in the human body are determined by the organization of their collagen fibers. Predicting how mechanics will change during growth and remodeling of those tissues requires constitutive laws that account for the density and dispersion of collagen fibers. Post-infarction scar in the heart, a mechanically and structurally complex material, does not yet have a validated fiber-based constitutive model. In this study, we tested four different constitutive laws employing exponential or polynomial strain-energy functions and accounting for either mean fiber orientation alone or the details of the fiber distribution about that mean. We quantified the goodness of fit of each law to mechanical testing data from 6-week-old myocardial scar in the rat using both sum of squared error (SSE) and the Akaike Information Criterion (AIC) to account for differences in the number of material parameters within the constitutive laws. We then compared their ability to prospectively predict the mechanics of independent myocardial scar samples from other time points during healing. Our analysis suggests that a constitutive law with a polynomial form that incorporates detailed information about collagen fiber distribution using a structure tensor provides excellent fits with just two parameters and reasonable predictions of myocardial scar mechanics from measured structure alone in scars containing sufficiently high collagen content.

Computational models of cardiac hypertrophy.

Cardiac hypertrophy, defined as an increase in mass of the heart, is a complex process driven by simultaneous changes in hemodynamics, mechanical stimuli, and hormonal inputs. It occurs not only during pre- and post-natal development but also in adults in response to exercise, pregnancy, and a range of cardiovascular diseases. One of the most exciting recent developments in the field of cardiac biomechanics is the advent of computational models that are able to accurately predict patterns of heart growth in many of these settings, particularly in cases where changes in mechanical loading of the heart play an import role. These emerging models may soon be capable of making patient-specific growth predictions that can be used to guide clinical interventions. Here, we review the history and current state of cardiac growth models and highlight three main limitations of current approaches with regard to future clinical application: their inability to predict the regression of heart growth after removal of a mechanical overload, inability to account for evolving hemodynamics, and inability to incorporate known growth effects of drugs and hormones on heart growth. Next, we outline growth mechanics approaches used in other fields of biomechanics and highlight some potential lessons for cardiac growth modeling. Finally, we propose a multiscale modeling approach for future studies that blends tissue-level growth models with cell-level signaling models to incorporate the effects of hormones in the context of pregnancy-induced heart growth.

Computational model predicts paracrine and intracellular drivers of fibroblast phenotype after myocardial infarction.

The fibroblast is a key mediator of wound healing in the heart and other organs, yet how it integrates multiple time-dependent paracrine signals to control extracellular matrix synthesis has been difficult to study in vivo. Here, we extended a computational model to simulate the dynamics of fibroblast signaling and fibrosis after myocardial infarction (MI) in response to time-dependent data for nine paracrine stimuli. This computational model was validated against dynamic collagen expression and collagen area fraction data from post-infarction rat hearts. The model predicted that while many features of the fibroblast phenotype at inflammatory or maturation phases of healing could be recapitulated by single static paracrine stimuli (interleukin-1 and angiotensin-II, respectively), mimicking the reparative phase required paired stimuli (e.g. TGFß and endothelin-1). Virtual overexpression screens simulated with either static cytokine pairs or post-MI paracrine dynamic predicted phase-specific regulators of collagen expression. Several regulators increased (Smad3) or decreased (Smad7, protein kinase G) collagen expression specifically in the reparative phase. NADPH oxidase (NOX) overexpression sustained collagen expression from reparative to maturation phases, driven by TGFß and endothelin positive feedback loops. Interleukin-1 overexpression had mixed effects, both enhancing collagen via the TGFß positive feedback loop and suppressing collagen via NFκB and BAMBI (BMP and activin membrane-bound inhibitor) incoherent feed-forward loops. These model-based predictions reveal network mechanisms by which the dynamics of paracrine stimuli and interacting signaling pathways drive the progression of fibroblast phenotypes and fibrosis after myocardial infarction.

Longitudinal Reinforcement of Acute Myocardial Infarcts Improves Function by Transmurally Redistributing Stretch and Stress.

A wide range of emerging therapies, from surgical restraint to biomaterial injection to tissue engineering, aim to improve heart function and limit adverse remodeling following myocardial infarction (MI). We previously showed that longitudinal surgical reinforcement of large anterior infarcts in dogs could significantly enhance systolic function without restricting diastolic function, but the underlying mechanisms for this improvement are poorly understood. The goal of this study was to construct a finite element model that could match our previously published data on changes in regional strains and left ventricular function following longitudinal surgical reinforcement, then use the model to explore potential mechanisms for the improvement in systolic function we observed. The model presented here, implemented in febio, matches all the key features of our experiments, including diastolic remodeling strains in the ischemic region, small shifts in the end-diastolic pressure-volume relationship (EDPVR), and large changes in the end-systolic pressure-volume relationship (ESPVR) in response to ischemia and to patch application. Detailed examination of model strains and stresses suggests that longitudinal reinforcement reduces peak diastolic fiber stretch and systolic fiber stress in the remote myocardium and shifts those peaks away from the endocardial surface by reshaping the left ventricle (LV). These findings could help to guide the development of novel therapies to improve post-MI function by providing specific design objectives.

Open-Source Routines for Building Personalized Left Ventricular Models From Cardiac Magnetic Resonance Imaging Data.

Creating patient-specific models of the heart is a promising approach for predicting outcomes in response to congenital malformations, injury, or disease, as well as an important tool for developing and customizing therapies. However, integrating multimodal imaging data to construct patient-specific models is a nontrivial task. Here, we propose an approach that employs a prolate spheroidal coordinate system to interpolate information from multiple imaging datasets and map those data onto a single geometric model of the left ventricle (LV). We demonstrate the mapping of the location and transmural extent of postinfarction scar segmented from late gadolinium enhancement (LGE) magnetic resonance imaging (MRI), as well as mechanical activation calculated from displacement encoding with stimulated echoes (DENSE) MRI. As a supplement to this paper, we provide MATLAB and Python versions of the routines employed here for download from SimTK.

Predictions of hypertrophy and its regression in response to pressure overload.

Mechanics-based cardiac growth models can now predict changes in mass, chamber size, and wall thickness in response to perturbations such as pressure overload (PO), volume overload, and myocardial infarction with a single set of growth parameters. As these models move toward clinical applications, many of the most interesting applications involve predictions of whether or how a patient's heart will reverse its growth after an intervention. In the case of PO, significant regression in wall thickness is observed both experimentally and clinically following relief of overload, for example following replacement of a stenotic aortic valve. Therefore, the objective of this work was to evaluate the ability of a published cardiac growth model that captures forward growth in multiple situations to predict growth reversal following relief of PO. Using a finite element model of a beating canine heart coupled to a circuit model of the circulation, we quantitatively matched hemodynamic data from a canine study of aortic banding followed by unbanding. Surprisingly, although the growth model correctly predicted the time course of PO-induced hypertrophy, it predicted only limited growth reversal given the measured unbanding hemodynamics, contradicting experimental and clinical observations. We were able to resolve this discrepancy only by incorporating an evolving homeostatic setpoint for the governing growth equations. Furthermore, our analysis suggests that many strain- and stress-based growth laws using the traditional volumetric growth framework will have similar difficulties capturing regression following the relief of PO unless growth setpoints are allowed to evolve.

Trajectory of right ventricular indices is an early predictor of outcomes in hypoplastic left heart syndrome.

BACKGROUND: Children with hypoplastic left heart syndrome (HLHS) have risk for mortality and/or transplantation. Previous studies have associated right ventricular (RV) indices in a single echocardiogram with survival, but none have related serial measurements to outcomes. This study sought to determine whether the trajectory of RV indices in the first year of life was associated with transplant-free survival to stage 3 palliation (S3P). METHODS: HLHS patients at a single center who underwent stage 1 palliation (S1P) between 2000 and 2015 were reviewed. Echocardiographic indices of RV size and function were obtained before and following S1P and stage 2 palliation (S2P). The association between these indices and transplant-free survival to S3P was examined. RESULTS: There were 61 patients enrolled in the study with 51 undergoing S2P, 20 S3P, and 18 awaiting S3P. In the stage 1 perioperative period, indexed RV end-systolic area increased in patients who died or needed transplant following S2P, and changed little in those surviving to S3P (3.37 vs -0.04 cm2 /m2 , P = .017). Increased indexed RV end-systolic area was associated with worse transplant-free survival. (OR = 0.815, P = .042). In the interstage period, indexed RV end-diastolic area increased less in those surviving to S3P (3.6 vs 9.2, P = .03). CONCLUSION: Change in indexed RV end-systolic area through the stage 1 perioperative period was associated with transplant-free survival to S3P. Neither the prestage nor poststage 1 indexed RV end-systolic area was associated with transplant-free survival to S3P. Patients with death or transplant before S3P had a greater increase in indexed RV end-diastolic area during the interstage period. This suggests earlier serial changes in RV size which may provide prognostic information beyond RV indices in a single study.