Computational modelling for congenital heart disease: how far are we from clinical translation?

Abstract

Computational models of congenital heart disease (CHD) have become increasingly sophisticated over the last 20 years. They can provide an insight into complex flow phenomena, allow for testing devices into patient-specific anatomies (pre-CHD or post-CHD repair) and generate predictive data. This has been applied to different CHD scenarios, including patients with single ventricle, tetralogy of Fallot, aortic coarctation and transposition of the great arteries. Patient-specific simulations have been shown to be informative for preprocedural planning in complex cases, allowing for virtual stent deployment. Novel techniques such as statistical shape modelling can further aid in the morphological assessment of CHD, risk stratification of patients and possible identification of new ‘shape biomarkers’. Cardiovascular statistical shape models can provide valuable insights into phenomena such as ventricular growth in tetralogy of Fallot, or morphological aortic arch differences in repaired coarctation. In a constant move towards more realistic simulations, models can also account for multiscale phenomena (eg, thrombus formation) and importantly include measures of uncertainty (ie, CIs around simulation results). While their potential to aid understanding of CHD, surgical/procedural decision-making and personalisation of treatments is undeniable, important elements are still lacking prior to clinical translation of computational models in the field of CHD, that is, large validation studies, cost-effectiveness evaluation and establishing possible improvements in patient outcomes.

Computational Pre-surgical Planning of Arterial Patch Reconstruction: Parametric Limits and In Vitro Validation

“…Our recent investigations on the hemodynamic performance of right ventricular outflow conduits, suggested an improved performance when customized valve leakage area and orientation are considered 11. Following the success in patient-specific computational fluid dynamics (CFD) simulations,1,27,35 the pre-surgical planning concept, based on soft-tissue finite element models (FEM) has also been demonstrated 3,36,37,41,42. Particularly, the implementation of FEM in the pre-surgical planning of complex heart valve repair procedures is well-established 4,23,30 – 32,44.…”

Kinematic boundary conditions substantially impact in silico ventricular function

“…Patient‐specific biophysical models of cardiac behavior have proven competent in elucidating the fundamentals of cardiac (patho)physiology. Recently, computational models have been used to help clinicians diagnose, to evaluate drug effects, to gain better insight on risk‐benefit ratios, and to predict the outcome of different treatment strategies . Even though full four‐chamber heart models are the most comprehensive, the application of such models in the clinical environment is challenging because the available imaging data resolution is often insufficient to accurately segment the atrial wall, which is typically an order of magnitude thinner than the ventricular wall.…”

“…Recently, computational models have been used to help clinicians diagnose, to evaluate drug effects, to gain better insight on risk-benefit ratios, and to predict the outcome of different treatment strategies. [1][2][3][4][5][6][7][8][9][10][11][12] Even though full four-chamber heart models 13 are the most comprehensive, the application of such models in the clinical environment is challenging because the available imaging data resolution is often insufficient to accurately segment the atrial wall, which is typically an order of magnitude thinner than the ventricular wall. Moreover, the development and calibration of these models also demand more, typically unavailable, sources of clinical data (ie, pressure catheter measurements, strain imaging, MRI diffusion tensor imaging, etc.).…”

Epigenetics for the pediatric cardiologist

“…Additionally, there is considerable interest in computational fluid dynamics, which aims to model hemodynamic flow and structural properties of blood vessels in a clinically relevant manner. As described in a recent review, integration of molecular data into computational fluid dynamics can further complement the technology for predicting vascular phenomena . Data on gene expression and epigenetic modifiers could assist computational modeling and synergistically amplify the clinical benefit.…”

“…As described in a recent review, integration of molecular data into computational fluid dynamics can further complement the technology for predicting vascular phenomena. 57 Data on gene expression and epigenetic modifiers could assist computational modeling and synergistically amplify the clinical benefit.…”