Abstract 17617: Toward Improving Diagnosis and Interventional Predictability in Aortic Valve Stenosis Using Computational Modeling
Vijay Govindarajan, Nils Johnson, Krishnan B Chandran, David D McPherson- Physiology (medical)
- Cardiology and Cardiovascular Medicine
Introduction: Watchful waiting for symptoms or critical hemodynamics to intervene on aortic stenosis (AS) may delay too long. While stress tests can detect symptoms in allegedly asymptomatic AS patients, image-based computations based on advanced modeling techniques can provide critical biomechanical insights.
Hypothesis: Advanced computational modeling techniques utilizing patient data backed by high-performance computing can inform a personalized understanding of disease progression, diagnosis, and anticipated post-op hemodynamics to predict interventional efficacy.
Methods: A validated in-house Fluid-structure Interaction (FSI) algorithm that allows easy translation of patient-specific anatomies was utilized to perform "virtual stress test" on a moderate AS with increasing LV contraction to predict systolic ejection dynamics under stress. TAVR was virtually implanted and post-op aortic hemodynamics were quantitatively compared with pre-op (diseased) data.
Results: Predictions demonstrated that that aortic flow turbulence in moderate AS (Re = 11,700) during exercise (115 bpm) can be similar to severe AS (Re=10,000) at rest (70 bpm) resulting in a greater dissipation losses rising linearly with heart rates. As LV contraction rose, each valve cusp underwent a distinct non-linear rise (>100%) in mechanical stresses that can accelerate calcification. Interestingly, our evaluation revealed that the heterogenous ΔP vs. Q relationship in AS observed clinically can be attributed to a non-linear rise in aortic valve area occurring during mid-systole. Aortic hemodynamics improved post-TAVR by reducing dissipation losses by ~11 fold and restoring near-normal peak-systolic velocity (1.7 m/s) in the ascending aorta.
Conclusions: High-performance based multi-physics algorithms incorporating patient data might offer comprehensive biomechanical insights into diagnosis and interventional suitability for aortic valve stenosis.