Using machine learning to characterize heart failure across the scales

Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the progression of heart failure and guide personalized treatment planning. Yet, the predictive potential of cardiac growth models remains poorly understood. Here, we quantify predictive power of a stretch-driven growth model using a chronic porcine heart failure model, subject-specific multiscale simulation, and machine learning techniques. We combine hierarchical modeling, Bayesian inference, and Gaussian process regression to quantify the uncertainty of our experimental measurements during an 8-week long study of volume overload in six pigs. We then propagate the experimental uncertainties from the organ scale through our computational growth model and quantify the agreement between experimentally measured and computationally predicted alterations on the cellular scale. Our study suggests that stretch is the major stimulus for myocyte lengthening and demonstrates that a stretch-driven growth model alone can explain 52.7% of the observed changes in myocyte morphology. We anticipate that our approach will allow us to design, calibrate, and validate a new generation of multiscale cardiac growth models to explore the interplay of various subcellular-, cellular-, and organ-level contributors to heart failure. Using machine learning in heart failure research has the potential to combine information from different sources, subjects, and scales to provide a more holistic picture of the failing heart and point toward new treatment strategies

PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps

Electroanatomical mapping, a keystone diagnostic tool in cardiac electrophysiology studies, can provide high-density maps of the local electric properties of the tissue. It is therefore tempting to use such data to better individualize current patient-specific models of the heart through a data assimilation procedure and to extract potentially insightful information such as conduction properties. Parameter identification for state-of-the-art cardiac models is however a challenging task. In this work, we introduce a novel inverse problem for inferring the anisotropic structure of the conductivity tensor, that is fiber orientation and conduction velocity along and across fibers, of an eikonal model for cardiac activation. The proposed method, named PIEMAP, performed robustly with synthetic data and showed promising results with clinical data. These results suggest that PIEMAP could be a useful supplement in future clinical workflows of personalized therapies.Comment: 12 pages, 4 figures, 1 tabl

How drugs modulate the performance of the human heart

Many drugs interact with ion channels in the cells of our heart and trigger heart rhythm disorders with potentially fatal consequences. Computational modeling can provide mechanistic insight into the onset and propagation of drug-induced arrhythmias, but the effect of drugs on the mechanical performance of the heart remains poorly understood. Here we establish a multiphysics framework that integrates the biochemical, electrical, and mechanical effects of drugs, from cellular excitation to cardiac contraction. For the example of the drug dofetilide, we show that drug concentrations of 5x and 8x increase the heart rate to 122 and 114 beats per minute, increase myofiber stretches by 5%, and decrease overall tissue relaxation by 6%. This results in inter-ventricular and atrial-ventricular dyssynchronies and changes in cardiac output by - 2.5 % and +7%. Our results emphasize the need for multiphysics modeling to better understand the mechanical implications of drug-induced arrhythmias. Knowing how different drug concentrations affect the performance of the heart has important clinical implications in drug safety evaluation and personalized medicine.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Medical Instruments & Bio-Inspired Technolog

Sensitivity Analysis of Ventricular Activation and Electrocardiogram in Tailored Models of Heart-Failure Patients

Cardiac resynchronization therapy is not effective in a variable proportion of heart failure patients. An accurate knowledge of each patient's electroanatomical features could be helpful to determine the most appropriate treatment. The goal of this study was to analyze and quantify the sensitivity of left ventricular (LV) activation and the electrocardiogram (ECG) to changes in 39 parameters used to tune realistic anatomical-electrophysiological models of the heart. Electrical activity in the ventricles was simulated using a reaction-diffusion equation. To simulate cellular electrophysiology, the Ten Tusscher-Panfilov 2006 model was used. Intracardiac electrograms and 12-lead ECGs were computed by solving the bidomain equation. Parameters showing the highest sensitivity values were similar in the six patients studied. QRS complex and LV activation times were modulated by the sodium current, the cell surface-to-volume ratio in the LV, and tissue conductivities. The T-wavewas modulated by the calcium and rectifier-potassium currents, and the cell surface-to-volume ratio in both ventricles. We conclude that homogeneous changes in ionic currents entail similar effects in all ECG leads, whereas the effects of changes in tissue properties show larger inter-lead variability. The effects of parameter variations are highly consistent between patients and most of the model tuning could be performed with only ~10 parameters

Electro-mechanical modeling and simulation of reentry phenomena in the presence of myocardial infarction

In this work we present a parallel solver for the numerical simulation of the cardiac electro-mechanical activity. We first review the most complete mathematical model of cardiac electro-mechanics, the so-called electro-mechanical coupling (EMC) model, which consists of the following four sub-models, strongly coupled together: the Bidomain model for the electrical activity at tissue scale, constituted by a parabolic system of two reaction-diffusion partial differential equations (PDEs); the finite elasticity system for the mechanical behavior at tissue scale; the membrane model for the bioelectrical activity at cellular scale, consisting of a stiff system of ordinary differential equations (ODEs); the active tension model for the mechanical activity at cellular scale, consisting of a system of ODEs. The discretization of the EMC model is performed by finite elements in space and an operator splitting strategy in time, based on semi-implicit finite differences. As a result of the discretization techniques adopted, the most computational demanding part at each time step is the solution of the non-linear algebraic system, deriving from the discretization of the finite elasticity equations, and of the linear system deriving from the discretization of the Bidomain equations. The former is solved by a Newton-GMRES-BDDC solver, i.e. the Jacobian system at each Newton iteration is solved by GMRES accelerated by the Balancing Domain Decomposition by Constraints (BDDC) preconditioner. The latter is solved by the Conjugate Gradient method, preconditioned by the Multilevel Additive Schwarz preconditioner. The performance of the resulting parallel solver is studied on the simulation of the induction of ventricular tachycardia in an idealized left ventricle affected by an infarct scar. The simulations are run on the Marconi-KNL cluster of the Cineca laboratory