Modelling in congenital heart disease. Art or science?

Patient-specific simulations for planning treatment in congenital heart disease

Patient-specific computational models have been extensively developed over the last decades and applied to investigate a wide range of cardiovascular problems. However, translation of these technologies into clinical applications, such as planning of medical procedures, has been limited to a few single case reports. Hence, the use of patient-specific models is still far from becoming a standard of care in clinical practice. The aim of this study is to describe our experience with a modelling framework that allows patient-specific simulations to be used for prediction of clinical outcomes. A cohort of 12 patients with congenital heart disease who were referred for percutaneous pulmonary valve implantation, stenting of aortic coarctation and surgical repair of double-outlet right ventricle was included in this study. Image data routinely acquired for clinical assessment were post-processed to set up patient-specific models and test device implantation and surgery. Finite-element and computational fluid dynamics analyses were run to assess feasibility of each intervention and provide some guidance. Results showed good agreement between simulations and clinical decision including feasibility, device choice and fluid-dynamic parameters. The promising results of this pilot study support translation of computer simulations as tools for personalization of cardiovascular treatments.

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

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.

In-vivo assessment of the morphology and hemodynamic functions of the BioValsalva™ composite valve-conduit graft using cardiac magnetic resonance imaging and computational modelling technology

BACKGROUND

The evaluation of any new cardiac valvular prosthesis should go beyond the classical morbidity and mortality rates and involve hemodynamic assessment. As a proof of concept, the objective of this study was to characterise for the first time the hemodynamics and the blood flow profiles at the aortic root in patients implanted with BioValsalva™ composite valve-conduit using comprehensive MRI and computer technologies.

METHODS

Four male patients implanted with BioValsalva™ and 2 age-matched normal controls (NC) underwent cardiac magnetic resonance imaging (MRI). Phase-contrast imaging with velocity-mapping in 3 orthogonal directions was performed at the level of the aortic root and descending thoracic aorta. Computational fluid dynamic (CFD) simulations were performed for all the subjects with patient-specific flow information derived from phase-contrast MR data.

RESULTS

The maximum and mean flow rates throughout the cardiac cycle at the aortic root level were very comparable between NC and BioValsalva™ patients (541 ± 199 vs. 567 ± 75 ml/s) and (95 ± 46 vs. 96 ± 10 ml/s), respectively. The maximum velocity (cm/s) was higher in patients (314 ± 49 vs. 223 ± 20; P = 0.06) due to relatively smaller effective orifice area (EOA), 2.99 ± 0.47 vs. 4.40 ± 0.24 cm2 (P = 0.06), however, the BioValsalva™ EOA was comparable to other reported prosthesis. The cross-sectional area and maximum diameter at the root were comparable between the two groups. BioValsalva™ conduit was stiffer than the native aortic wall, compliance (mm2 • mmHg(-1) • 10(-3)) values were (12.6 ± 4.2 vs 25.3 ± 0.4.; P = 0.06). The maximum time-averaged wall shear stress (Pa), at the ascending aorta was equivalent between the two groups, 17.17 ± 2.7 (NC) vs. 17.33 ± 4.7 (BioValsalva™ ). Flow streamlines at the root and ascending aorta were also similar between the two groups apart from a degree of helical flow that occurs at the outer curvature at the angle developed near the suture line.

CONCLUSIONS

BioValsalva™ composite valve-conduit prosthesis is potentially comparable to native aortic root in structural design and in many hemodynamic parameters, although it is stiffer. Surgeons should pay more attention to the surgical technique to maximise the reestablishment of normal smooth aortic curvature geometry to prevent unfavourable flow characteristics.