A Preliminary Study on the Usage of a Data-Driven Probabilistic Approach to Predict Valve Performance Under Different Physiological Conditions

Effect of Blood Pressure Levels on Sinus Hemodynamics in Relation to Calcification After Bioprosthetic Aortic Valve Replacement

Coexisting hypertension and aortic stenosis are common. Some studies showed that elevated blood pressures may be associated with progression of calcific aortic valve disease (CAVD) while others showed no correlation. Flow dynamics in the sinuses of Valsalva are considered key factors in the progression of CAVD. While the relationship between hemodynamics and CAVD is not yet fully understood, it has been demonstrated that they are tightly correlated. This study aims to investigate the effect of changing systolic and diastolic blood pressures (SBP and DBP, respectively) on sinus hemodynamics in relation to potential initiation or progression of CAVD after aortic valve replacement (AVR). Evolut R, SAPIEN 3 and Magna valves were deployed in an aortic root under pulsatile conditions. Using particle image velocimetry, the hemodynamics in the sinus were assessed. The velocity, vorticity, circulation ( Γ ) and shear stress were calculated. This study shows that under elevated SBP and DBP, velocity, vorticity, and shear stress nearby the leaflets increased. Additionally, larger fluctuations of Γ and area under the curve throughout the cardiac cycle were observed. Elevated blood pressures are associated with higher velocity, vorticity, and shear stress near the leaflets which may initiate or accelerate pro-calcific changes in the prosthetic leaflets leading to bioprosthetic valve degeneration.

Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacement

Objective

After transcatheter aortic valve replacement, the mean transvalvular pressure gradient indicates the effectiveness of the therapy. The objective is to develop artificial intelligence to predict the post-transcatheter aortic valve replacement aortic valve pressure gradient and aortic valve area from preprocedural echocardiography and computed tomography data.

Methods

A retrospective study was conducted on patients who underwent transcatheter aortic valve replacement due to aortic valve stenosis. A total of 1091 patients were analyzed for pressure gradient predictions (mean age 76.8 ± 9.2 years, 57.8% male), and 1063 patients were analyzed for aortic valve area predictions (mean age 76.7 ± 9.3 years, 57.2% male). An artificial intelligence learning model was trained (training: n = 663 patients, validation: n = 206 patients) and tested (testing: n = 222 patients) to predict pressure gradient, and a separate artificial intelligence learning model was trained (training: n = 640 patients, validation: n = 218 patients) and tested (testing: n = 205 patients) for predicting aortic valve area.

Results

The mean absolute error for pressure gradient and aortic valve area predictions was 3.0 mm Hg and 0.45 cm2, respectively. Valve sheath size, body surface area, and age were determined to be the top 3 predictors for pressure gradient, and valve sheath size, left ventricular ejection fraction, and aortic annulus mean diameter were identified to be the top 3 predictors of post-transcatheter aortic valve replacement aortic valve area. A training dataset size of more than 500 patients demonstrated good robustness of the artificial intelligence models for pressure gradient and aortic valve area.

Conclusions

The artificial intelligence-based algorithm has demonstrated potential in predicting post-transcatheter aortic valve replacement transvalvular pressure gradient predictions for patients with aortic valve stenosis. Further studies are necessary to differentiate pressure gradient between valve types.