Intracardiac Hemodynamics before and after Surgical Ventricular Restoration: Computational Analysis Based on CCT Data

Background: Surgical ventricular restoration (SVR) of the left ventricle (LV) is an established option to restore ventricular function and shape after remodeling due to myocardial infarction. While the STICH trial reported limited improvement in life expectancy, other studies report excellent outcomes for SVR. This study aims to investigate the change of intracardiac hemodynamics due to SVR.

Methods: Cardiac computed tomography (CCT) data of 10 patients (age: 59 ± 12.3 years, 3 females, BSA: 1.88 ± 0.23 m²) before and after successful SVR (NYHA 3 ± 0.45 to 1.3 ± 0.47) were used to reconstruct 3D end-diastolic and end-systolic LV anatomy. The image-based data were analyzed regarding anatomical post treatment changes and used to compute intracardiac hemodynamics via computational fluid dynamics (CFD). Therewith, washout behavior, intracardiac flow kinetic energy, and large flow structures were investigated.

Results: After treatment, the mitral valve regurgitation degree reduced from 1.1 ± 0.87 to 0.5 ± 0.52. Geometric analysis of CCT data found, that the end-diastolic volume reduced significantly from 228 ± 76.4 mL to 136 ± 37.0 mL. The stroke volume reduced nonsignificantly from 60 ± 21.9 mL to 56 ± 16.5 mL, thus meaning significant increase in ejection fraction. The flow rate curves show a reinstated E-wave postoperatively, while sphericity and undulation index, describing the LV shape, show more spherical and less undulated postoperative shapes. From a hemodynamic perspective, a significantly improved washout and thus lower risk of thrombus formation and embolic events is visible after surgery. The computations furthermore suggest a strong correlation between washout and ejection fraction, linking the washout to a purely geometric measure.

Conclusion: CCT-based CFD analysis of intracardiac blood flow allows analyzing SVR from a hemodynamic point of view. Perspectively, the presented methodology offers the potential to virtually perform SVR and predict interventional outcomes pre-operatively.

Publikationsverlauf

Artikel online veröffentlicht:
13. Februar 2024