Patient‐specific parameter estimation in single‐ventricle lumped circulation models under uncertainty

Abstract

Summary Computational models of cardiovascular physiology can inform clinical decision-making, providing a physically consistent framework to assess vascular pressures and flow distributions, and aiding in treatment planning. In particular, lumped parameter network (LPN) models that make an analogy to electrical circuits offer a fast and surprisingly realistic method to reproduce the circulatory physiology. The complexity of LPN models can vary significantly to account, for example, for cardiac and valve function, respiration, autoregulation, and time-dependent hemodynamics. More complex models provide insight into detailed physiological mechanisms, but their utility is maximized if one can quickly identify patient specific parameters. The clinical utility of LPN models with many parameters will be greatly enhanced by automated parameter identification, particularly if parameter tuning can match non-invasively obtained clinical data. We present a framework for automated tuning of 0D lumped model parameters to match clinical data. We demonstrate the utility of this framework through application to single ventricle pediatric patients with Norwood physiology. Through a combination of local identifiability, Bayesian estimation and maximum a posteriori simplex optimization, we show the ability to automatically determine physiologically consistent point estimates of the parameters and to quantify uncertainty induced by errors and assumptions in the collected clinical data. We show that multi-level estimation, that is, updating the parameter prior information through sub-model analysis, can lead to a significant reduction in the parameter marginal posterior variance. We first consider virtual patient conditions, with clinical targets generated through model solutions, and second application to a cohort of four single-ventricle patients with Norwood physiology.

Computational modeling and engineering in pediatric and congenital heart disease

“…parameter estimation) and in assessing errors in simulation predictions. [28–30] [31,32] Parameter estimation has also been successfully applied in the setting of fluid structure interaction for selection of patient specific material properties to match time-resolved clinical imaging data. [33] These approaches have the potential to provide clinicians with accuracy on the level of trust that can be placed in simulation predictions, thereby fostering increased clinical acceptance and establishing standards for model fidelity.…”

The impact of personalized probabilistic wall thickness models on peak wall stress in abdominal aortic aneurysms

“…Consequently, the issue of missing or uncertain patient‐specific model input parameters is now generally recognized among researchers and an increasing amount of work regarding UQ in cardiovascular models has started to emerge in recent years. ()…”

“…Consequently, the issue of missing or uncertain patient-specific model input parameters is now generally recognized among researchers and an increasing amount of work regarding UQ in cardiovascular models has started to emerge in recent years. [1][2][3][4] In this work, we are concerned with the impact of an uncertain wall thickness in computational models of abdominal aortic aneurysms (AAAs). More specifically, we investigate and compare different modeling options for the uncertainty present in the wall thickness.…”

Identification of dynamic load for prosthetic structures

“…Marais et al evaluated the diameter and thickness‐related variations in mechanical properties of degraded arterial wall, in which an inverse FE method was used to identify the material parameters. More literature on the parameter identification can be referred to the references herein …”