Cardiac contraction is the result of integrated cellular, tissue and organ function. Biophysical in silico cardiac models offer a systematic approach for studying these multi-scale interactions. The computational cost of such models is high, due to their multi-parametric and nonlinear nature. This has so far made it difficult to perform model fitting and prevented global sensitivity analysis (GSA) studies. We propose a machine learning approach based on Gaussian process emulation of model simulations using probabilistic surrogate models, which enables model parameter inference via a Bayesian history matching (HM) technique and GSA on whole-organ mechanics. This framework is applied to model healthy and aortic-banded hypertensive rats, a commonly used animal model of heart failure disease. The obtained probabilistic surrogate models accurately predicted the left ventricular pump function ( R 2 = 0.92 for ejection fraction). The HM technique allowed us to fit both the control and diseased virtual bi-ventricular rat heart models to magnetic resonance imaging and literature data, with model outputs from the constrained parameter space falling within 2 SD of the respective experimental values. The GSA identified Troponin C and cross-bridge kinetics as key parameters in determining both systolic and diastolic ventricular function. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.
Cardiac anatomy plays a crucial role in determining cardiac function. However, there is a poor understanding of how specific and localised anatomical changes affect different cardiac functional outputs. In this work, we test the hypothesis that in a statistical shape model (SSM), the modes that are most relevant for describing anatomy are also most important for determining the output of cardiac electromechanics simulations. We made patient-specific four-chamber heart meshes (n = 20) from cardiac CT images in asymptomatic subjects and created a SSM from 19 cases. Nine modes captured 90% of the anatomical variation in the SSM. Functional simulation outputs correlated best with modes 2, 3 and 9 on average (R = 0.49 ± 0.17, 0.37 ± 0.23 and 0.34 ± 0.17 respectively). We performed a global sensitivity analysis to identify the different modes responsible for different simulated electrical and mechanical measures of cardiac function. Modes 2 and 9 were the most important for determining simulated left ventricular mechanics and pressure-derived phenotypes. Mode 2 explained 28.56 ± 16.48% and 25.5 ± 20.85, and mode 9 explained 12.1 ± 8.74% and 13.54 ± 16.91% of the variances of mechanics and pressure-derived phenotypes, respectively. Electrophysiological biomarkers were explained by the interaction of 3 ± 1 modes. In the healthy adult human heart, shape modes that explain large portions of anatomical variance do not explain equivalent levels of electromechanical functional variation. As a result, in cardiac models, representing patient anatomy using a limited number of modes of anatomical variation can cause a loss in accuracy of simulated electromechanical function.
We have described impairment of the respiratory function in adult patients with childhood-onset growth hormone (GH) deficiency. The aim of the present study was to evaluate lung volumes and respiratory muscle strength in patients diagnosed as GH deficient before and after 6 and 12 months of recombinant GH treatment. Ten adults diagnosed as GH deficient in childhood, ten adults diagnosed as GH deficient in adulthood and ten healthy subjects entered the study. For each subject, evaluation of respiratory function followed the same standard approach, consisting of respiratory muscle strength assessment, record of flow-volume curves, measurement of static lung volumes and lung diffusing capacity. Childhood-onset GH-deficient patients had a significant reduction of maximal inspiratory (p < 0.01) and maximal expiratory (p < 0.05) mouth pressures. Total lung capacity, vital capacity and functional residual capacity were significantly reduced compared to healthy subjects (p < 0.05). Conversely, residual volume and diffusing lung capacity did not show any significant change. No significant change of the ratio between the percentage forced expiratory volume in 1 s and the forced vital capacity was observed. The decrease of respiratory mouth pressures was not correlated to the decrease of lung volumes. Adult-onset GH-deficient patients had only a significant reduction of maximal expiratory pressure compared to healthy subjects (p < 0.05). After 6 months of treatment no significant differences in any of the evaluated parameters were found. After 12 months of treatment patients with childhood-onset GH deficiency show a significant improvement of lung volumes (p < 0.01) and maximal respiratory mouth pressures (p < 0.005), whereas adult-onset GH-deficient patients show a significant improvement of maximal expiratory pressure (p < 0.05). In conclusion, the results of this study showed that adult patients affected with childhood-onset GH deficiency suffer from an impairment of the ventilatory function due to a reduction of lung volumes and a decrease of respiratory pressures probably due to a reduction of respiratory muscle strength. This impairment was reversed after 12 months of treatment with recombinant GH. Conversely, adult-onset GH-deficient patients had only an impairment of the maximal expiratory pressure, probably due to respiratory muscle weakness re-established after 12 months of GH therapy.
AimsRegional heterogeneities in contraction contribute to heart failure with reduced ejection fraction (HFrEF). We aimed to determine whether regional changes in myocardial relaxation similarly contribute to diastolic dysfunction in post-infarction HFrEF, and to elucidate the underlying mechanisms.Methods and resultsUsing the magnetic resonance imaging phase-contrast technique, we examined local diastolic function in a rat model of post-infarction HFrEF. In comparison with sham-operated animals, post-infarction HFrEF rats exhibited reduced diastolic strain rate adjacent to the scar, but not in remote regions of the myocardium. Removal of Ca2+ within cardiomyocytes governs relaxation, and we indeed found that Ca2+ transients declined more slowly in cells isolated from the adjacent region. Resting Ca2+ levels in adjacent zone myocytes were also markedly elevated at high pacing rates. Impaired Ca2+ removal was attributed to a reduced rate of Ca2+ sequestration into the sarcoplasmic reticulum (SR), due to decreased local expression of the SR Ca2+ ATPase (SERCA). Wall stress was elevated in the adjacent region. Using ex vivo experiments with loaded papillary muscles, we demonstrated that high mechanical stress is directly linked to SERCA down-regulation and slowing of relaxation. Finally, we confirmed that regional diastolic dysfunction is also present in human HFrEF patients. Using echocardiographic speckle-tracking of patients enrolled in the LEAF trial, we found that in comparison with controls, post-infarction HFrEF subjects exhibited reduced diastolic train rate adjacent to the scar, but not in remote regions of the myocardium.ConclusionOur data indicate that relaxation varies across the heart in post-infarction HFrEF. Regional diastolic dysfunction in this condition is linked to elevated wall stress adjacent to the infarction, resulting in down-regulation of SERCA, disrupted diastolic Ca2+ handling, and local slowing of relaxation.
Computational Fluid Dynamics (CFD) is used to assist in designing artificial valves and planning procedures, focusing on local flow features. However, assessing the impact on overall cardiovascular function or predicting longer-term outcomes may requires more comprehensive whole heart CFD models. Fitting such models to patient data requires numerous computationally expensive simulations, and depends on specific clinical measurements to constrain model parameters, hampering clinical adoption. Surrogate models can help to accelerate the fitting process while accounting for the added uncertainty. We create a validated patient-specific four-chamber heart CFD model based on the Navier-Stokes-Brinkman (NSB) equations and test Gaussian Process Emulators (GPEs) as a surrogate model for performing a variance-based global sensitivity analysis (GSA). GSA identified preload as the dominant driver of flow in both the right and left side of the heart, respectively. Left-right differences were seen in terms of vascular outflow resistances, with pulmonary artery resistance having a much larger impact on flow than aortic resistance. Our results suggest that GPEs can be used to identify parameters in personalized whole heart CFD models, and highlight the importance of accurate preload measurements.
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