Personalization of electro-mechanical models of the pressure-overloaded left ventricle: fitting of Windkessel-type afterload models.

Computer models of left ventricular (LV) electro-mechanics (EM) show promise as a tool for assessing the impact of increased afterload upon LV performance. However, the identification of unique afterload model parameters and the personalization of EM LV models remains challenging due to significant clinical input uncertainties. Here, we personalized a virtual cohort of N = 17 EM LV models under pressure overload conditions. A global-local optimizer was developed to uniquely identify parameters of a three-element Windkessel (Wk3) afterload model. The sensitivity of Wk3 parameters to input uncertainty and of the EM LV model to Wk3 parameter uncertainty was analysed. The optimizer uniquely identified Wk3 parameters, and outputs of the personalized EM LV models showed close agreement with clinical data in all cases. Sensitivity analysis revealed a strong dependence of Wk3 parameters on input uncertainty. However, this had limited impact on outputs of EM LV models. A unique identification of Wk3 parameters from clinical data appears feasible, but it is sensitive to input uncertainty, thus depending on accurate invasive measurements. By contrast, the EM LV model outputs were less sensitive, with errors of less than 8.14% for input data errors of 10%, which is within the bounds of clinical data uncertainty. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

The CFTR variant profile of Hispanic patients with cystic fibrosis: Impact on access to effective screening, diagnosis, and personalized medicine.

Hispanic patients comprise an appreciable and increasing proportion of patients with cystic fibrosis (CF) in the United States (US). Hispanic patients with CF are known to have increased morbidity and mortality compared to non-Hispanic white patients with CF, and ongoing investigations are underway to identify contributing factors amenable to intervention in order to address the disparate health outcomes. One contributing factor is the different CF transmembrane conductance regulator (CFTR) variant profile observed in Hispanic patients with CF. The most common CFTR variant, p.Phe508del (legacy name F508del), is proportionally underrepresented in Hispanic patients with CF. This difference has implications for prenatal screening, newborn screening (NBS), and CFTR variant-specific therapeutic options. In particular, the recent approval of a highly effective CFTR modulator for patients carrying at least one copy of F508del, elexacaftor/tezacaftor/ivacaftor triple combination therapy, underscores the potential for unequal access to personalized treatment for Hispanic patients with CF. We report the CFTR variant profiles of Hispanic patients with CF and non-CF Hispanic infants with a false-positive New York State CF NBS at a single center in New York City over a 5-year study period, as an opportunity to address the racial and ethnic disparities that currently exist in CF screening, diagnosis, and treatment. In addition to the previously documented disparate prevalence of the CFTR variant F508del in Hispanic patients, we observed two CFTR variants, p.His609Arg (legacy name H609R) and p.Thr1036Asn (legacy name T1036N), frequently identified in our Hispanic patients of Ecuadorian and Mexican ancestry, respectively, that are not well-described in the US population. The presence of population-specific and individually rare CFTR variants in Hispanic patients with CF further accentuates the disparity in health outcomes, as these CFTR variants are often absent from prenatal and NBS CFTR variant panels, potentially delaying diagnosis, and without an approved CFTR variant-specific therapy.

Robust and efficient fixed-point algorithm for the inverse elastostatic problem to identify myocardial passive material parameters and the unloaded reference configuration.

Image-based computational models of the heart represent a powerful tool to shed new light on the mechanisms underlying physiological and pathological conditions in cardiac function and to improve diagnosis and therapy planning. However, in order to enable the clinical translation of such models, it is crucial to develop personalized models that are able to reproduce the physiological reality of a given patient. There have been numerous contributions in experimental and computational biomechanics to characterize the passive behavior of the myocardium. However, most of these studies suffer from severe limitations and are not applicable to high-resolution geometries. In this work, we present a novel methodology to perform an automated identification of in vivo properties of passive cardiac biomechanics. The highly-efficient algorithm fits material parameters against the shape of a patient-specific approximation of the end-diastolic pressure-volume relation (EDPVR). Simultaneously, an unloaded reference configuration is generated, where a novel line search strategy to improve convergence and robustness is implemented. Only clinical image data or previously generated meshes at one time point during diastole and one measured data point of the EDPVR are required as an input. The proposed method can be straightforwardly coupled to existing finite element (FE) software packages and is applicable to different constitutive laws and FE formulations. Sensitivity analysis demonstrates that the algorithm is robust with respect to initial input parameters.

Towards a Computational Framework for Modeling the Impact of Aortic Coarctations Upon Left Ventricular Load.

Computational fluid dynamics (CFD) models of blood flow in the left ventricle (LV) and aorta are important tools for analyzing the mechanistic links between myocardial deformation and flow patterns. Typically, the use of image-based kinematic CFD models prevails in applications such as predicting the acute response to interventions which alter LV afterload conditions. However, such models are limited in their ability to analyze any impacts upon LV load or key biomarkers known to be implicated in driving remodeling processes as LV function is not accounted for in a mechanistic sense. This study addresses these limitations by reporting on progress made toward a novel electro-mechano-fluidic (EMF) model that represents the entire physics of LV electromechanics (EM) based on first principles. A biophysically detailed finite element (FE) model of LV EM was coupled with a FE-based CFD solver for moving domains using an arbitrary Eulerian-Lagrangian (ALE) formulation. Two clinical cases of patients suffering from aortic coarctations (CoA) were built and parameterized based on clinical data under pre-treatment conditions. For one patient case simulations under post-treatment conditions after geometric repair of CoA by a virtual stenting procedure were compared against pre-treatment results. Numerical stability of the approach was demonstrated by analyzing mesh quality and solver performance under the significantly large deformations of the LV blood pool. Further, computational tractability and compatibility with clinical time scales were investigated by performing strong scaling benchmarks up to 1536 compute cores. The overall cost of the entire workflow for building, fitting and executing EMF simulations was comparable to those reported for image-based kinematic models, suggesting that EMF models show potential of evolving into a viable clinical research tool.