Background Patients with repaired tetralogy of Fallot (TOF) account for a substantial proportion of cases with late-onset right ventricular (RV) failure. The current surgical approach, which includes pulmonary valve replacement/insertion (PVR), has yielded mixed results. Therefore, it may be clinically useful to identify parameters that can potentially be used to predict RV function response to PVR. Methods and Results Cardiac magnetic resonance (CMR) data before and 6-month after PVR were obtained from 16 patients with repaired TOF (8 m, 8 f, median age 42.75). RV ejection fraction (EF) change from pre- to post-PVR was used as the outcome. The patients were divided into Group 1 (n=8, better outcome) and Group 2 (n=8, worst outcome). CMR-based patient-specific computational RV/LV models were constructed and RV mechanical stress and strain, wall thickness (WT), curvature, and volumes were obtained for analysis. Our results indicated that RV wall stress was the best single predictor for post-PRV outcome with an area under the receiver operating characteristic curve of 0.819. Mean values of stress, strain, WT, and longitudinal curvature differed significantly between the two groups with RV wall stress showing the largest difference. Mean RV stress from Group 2 was 103% higher than that from Group 1. Conclusion Computational modeling and RV stress may be used as a potential tool to identify RV function response to PVR. Large-scale clinical studies are needed to validate these preliminary findings.

BackgroundAccurate calculation of ventricular stress and strain is critical for cardiovascular investigations. Sarcomere shortening in active contraction leads to change of ventricular zero-stress configurations during the cardiac cycle. A new model using different zero-load diastole and systole geometries was introduced to provide more accurate cardiac stress/strain calculations with potential to predict post pulmonary valve replacement (PVR) surgical outcome.MethodsCardiac magnetic resonance (CMR) data were obtained from 16 patients with repaired tetralogy of Fallot prior to and 6 months after pulmonary valve replacement (8 male, 8 female, mean age 34.5 years). Patients were divided into Group 1 (n = 8) with better post PVR outcome and Group 2 (n = 8) with worse post PVR outcome based on their change in RV ejection fraction (EF). CMR-based patient-specific computational RV/LV models using one zero-load geometry (1G model) and two zero-load geometries (diastole and systole, 2G model) were constructed and RV wall thickness, volume, circumferential and longitudinal curvatures, mechanical stress and strain were obtained for analysis. Pairwise T-test and Linear Mixed Effect (LME) model were used to determine if the differences from the 1G and 2G models were statistically significant, with the dependence of the pair-wise observations and the patient-slice clustering effects being taken into consideration. For group comparisons, continuous variables (RV volumes, WT, C- and L- curvatures, and stress and strain values) were summarized as mean ± SD and compared between the outcome groups by using an unpaired Student t-test. Logistic regression analysis was used to identify potential morphological and mechanical predictors for post PVR surgical outcome.ResultsBased on results from the 16 patients, mean begin-ejection stress and strain from the 2G model were 28% and 40% higher than that from the 1G model, respectively. Using the 2G model results, RV EF changes correlated negatively with stress (r = -0.609, P = 0.012) and with pre-PVR RV end-diastole volume (r = -0.60, P = 0.015), but did not correlate with WT, C-curvature, L-curvature, or strain. At begin-ejection, mean RV stress of Group 2 was 57.4% higher than that of Group 1 (130.1±60.7 vs. 82.7±38.8 kPa, P = 0.0042). Stress was the only parameter that showed significant differences between the two groups. The combination of circumferential curvature, RV volume and the difference between begin-ejection stress and end-ejection stress was the best predictor for post PVR outcome with an area under the ROC curve of 0.855. The begin-ejection stress was the best single predictor among the 8 individual parameters with an area under the ROC curve of 0.782.ConclusionThe new 2G model may be able to provide more accurate ventricular stress and strain calculations for potential clinical applications. Combining morphological and mechanical parameters may provide better predictions for post PVR outcome.

Apical aneurysm was observed to be associated with midventricular obstruction (MVO) in hypertrophic cardiomyopathy (HCM). To investigate the genesis of the apical aneurysm, the idealized numerical left ventricular models (finite-element left ventricle models) of the healthy left ventricle, subaortic obstruction, and midventricular obstruction in HCM of left ventricle were created. The mechanical effects in the formation of apical aneurysm were determined by comparing the myofiber stress on the apical wall between these three models (healthy, subaortic obstruction, and midventricular obstruction models). In comparing the subaortic obstruction model and MVO model with HCM, it was found that, at the time of maximum pressure, the maximum value of myofiber stress in MVO model was 75.0% higher than that in the subaortic obstruction model (654.5 kPa vs. 373.9 kPa). The maximum stress on the apex of LV increased 79.9, 69.3, 117.8% than that on the myocardium around the apex in healthy model, subaortic obstruction model, and MVO model, respectively. Our results indicated that high myofiber stress on the apical wall might initiate the formation process of the apical aneurysm.

Background. Patient-specific active Fluid-Structure Interactions (FSI) model is a useful approach to non-invasively investigate the hemodynamics in the heart. However, it takes a lot of effort to obtain the proper external force boundary conditions for active models, which heavily restrained the time-sensitive clinical applications of active computational models. Methods. The simulation results of 12 passive FSI models based on 6 patients’ pre-operative and post-operative CT images were compared with corresponding active models to investigate the differences in hemodynamics and cardiac mechanics between these models. Results. In comparing the passive and active models, it was found that there was no significant difference in pressure difference and shear stress on mitral valve leaflet (MVL) at the pre-SAM time point, but a significant difference was found in wall stress on the inner boundary of left ventricle (Endocardium). It was also found that pressure difference on the coapted MVL and the shear stress on MVL were significantly decreased after successful surgery in both active and passive models. Conclusion. Our results suggested that the passive models may provide good approximated hemodynamic results at 5% RR interval, which is crucial for analyzing the initiation of systolic anterior motion (SAM). Comparing to active models, the passive models decrease the complexity of the modeling construction and the difficulty of convergence significantly. These findings suggest that, with proper boundary conditions and sufficient clinical data, the passive computational model may be a good substitution model for the active model to perform hemodynamic analysis of the initiation of SAM.

Background Patient-specific active fluid–structure interactions (FSI) model is a useful approach to non-invasively investigate the hemodynamics in the heart. However, it takes a lot of effort to obtain the proper external force boundary conditions for active models, which heavily restrained the time-sensitive clinical applications of active computational models. Methods The simulation results of 12 passive FSI models based on 6 patients’ pre-operative and post-operative CT images were compared with corresponding active models to investigate the differences in hemodynamics and cardiac mechanics between these models. Results In comparing the passive and active models, it was found that there was no significant difference in pressure difference and shear stress on mitral valve leaflet (MVL) at the pre-SAM time point, but a significant difference was found in wall stress on the inner boundary of left ventricle (endocardium). It was also found that pressure difference on the coapted MVL and the shear stress on MVL were significantly decreased after successful surgery in both active and passive models. Conclusion Our results suggested that the passive models may provide good approximated hemodynamic results at 5% RR interval, which is crucial for analyzing the initiation of systolic anterior motion (SAM). Comparing to active models, the passive models decrease the complexity of the modeling construction and the difficulty of convergence significantly. These findings suggest that, with proper boundary conditions and sufficient clinical data, the passive computational model may be a good substitution model for the active model to perform hemodynamic analysis of the initiation of SAM.