Gurev, V. | Search bioRxiv

bioRxiv2019

Model order reduction for left ventricular mechanics via congruency training

Computational models of the cardiovascular system and heart function are currently being investigated as analytic tools to assist medical practice and clinical trials. Recent technological advances allow for finite element models of heart ventricles and atria to be customized to medical images and to assimilate electrical and hemodynamic measurements. Optimizing model parameters to physiological data is, however, challenging due to the computational complexity of finite element models. Metaheuristic algorithms and other optimization strategies typically require sampling hundreds of points in the model parameter space before converging to optimal solutions. Similarly, resolving uncertainty of model outputs to input assumptions is difficult for finite element models due to their computational cost. In this paper, we present a novel, multifidelity strategy for model order reduction of 3-D finite element models of ventricular mechanics. Our approach is centered around well established findings on the similarity between contraction of an isolated muscle and the whole ventricle. Specifically, we demonstrate that simple linear transformations between sarcomere strain (tension) and ventricular volume (pressure) are sufficient to reproduce global pressure-volume outputs of 3-D finite element models even by a reduced model with just a single myocyte unit. We further develop a procedure for congruency training of a surrogate low-order model from multi-scale finite elements, and we construct an example of parameter optimization based on medical images. We discuss how the presented approach might be employed to process large datasets of medical images as well as databases of echocardiographic reports, paving the way towards application of heart mechanics models in the clinical practice.

biophysics↗

bioRxiv2019

Intrinsic structure of model-derived metrics for in silico proarrhytmic riskassessment identified by global sensitivity analysis

Multiscale computational models of heart are being extensively investigated for improved assessment of drug-induced Torsades de Pointes (TdP) risk, a fatal side effect of many drugs. Model-derived metrics (features) such as action potential duration, net charge carried by ionic currents (qNet) and others have been proposed in the past as potential candidates for classifying TdP risk. However, the criteria for selection of new risk metrics are still poorly justified, and they are often trained/tested only on small datasets. Moreover, classifiers built on derived features have thus far not consistently provided increased prediction accuracies compared to classifiers based on in vitro measurements of drug effects on ion channels (direct features). In this paper, we analyze a large population of virtual drugs to examine systematically the sensitivity of several model-derived features. The influence of different ion channels in regulation of the model-derived features is identified using global sensitivity analysis (GSA). Specifically, the analysis points to key differences in the input parameters that affect several model-derived features and the generation of early afterdepolarizations (EAD), thus opposing the idea that these features and sensitivity to EAD might be strongly correlated. We also demonstrate that previously proposed model-derived features could be well fitted by a linear combination of direct features. This well explains the observed comparable performances of classifiers built on direct features and model-derived features. Combining GSA and simple probability analysis, we also show that the odds of any linear metric constructed from direct features to perform as well as qNet is very low. Nevertheless, despite high predictive power of qNet to separate drugs into correct categories of TdP risk, the GSA results suggest that the actual mechanistic interpretation for qNets improved performance deserves further investigation. In conclusion, analyses like ours can provide more robust feature selection/construction. Improved experimental designs with increased focus on the critical model parameters indicated by GSA can potentially reduce the uncertainties of key model components and result in increased confidence of TdP risk predicted by in silico models. Author SummaryBiophysical models often have extremely involved intrinsic structure. In the majority of research, either complex methods of non-linear dynamics and empirical analysis are employed to explore the underlying structure of cell processes such as transmembrane ionic currents. Global sensitivity analysis (GSA) could be considered as a brute force alternative to study the model relationships between physical processes, discovering the mechanisms responsible for phenomena of interest. As we demonstrated here, GSA application could be extended to explore the structure of features derived from outputs of biophysical models and used in statistical models to build regressions or classifiers. In particular, GSA seems to be valuable to formalize the methods of feature selection/construction that are used for classification of drugs with respect to their cardiotoxicity.

pharmacology and toxicology↗