Proper Selection Does Make the Difference: A Propensity-Matched Analysis of Percutaneous and Surgical Cut-Down Transfemoral TAVR

See It Best: A Propensity-Matched Analysis of Ultrasound-Guided versus Blind Femoral Artery Puncture in Balloon-Expandable TAVI

Background: Currently, transcatheter aortic valve implantation (TAVI) is the standard procedure recommended for patients over 75 years of age with symptomatic aortic valve stenosis. Percutaneous transfemoral (TF) access is the main route used to perform the procedure. Among periprocedural complications, access-related ones are the most frequent, potentially leading to prolonged in-hospital stays and transfusions. Methods: We performed a retrospective analysis of prospectively collected data on consecutive patients undergoing TF-TAVI with the latest generation balloon-expandable transcatheter valve between 2013 and 2022. Results: A total of 600 patients were analyzed, differentiating the population between ultrasound-guided and blind common femoral artery puncture. Valve Academic Research Consortium 3 (VARC-3)criteria were used to report at 30 days and follow-up. In our propensity-matched comparison of the two groups, we found a strong reduction in access-related complications in the echo-guided group, particularly in terms of reduction of major and minor bleedings. We also found a significant trend in reduction of local complications, such as pseudoaneurysms, hematomas, arterio-venous fistulas, dissection of the femoral or iliac arteries, and stenosis. Conclusions: Although there is a lack of consensus on the role of ultrasound-guided puncture, we found better outcomes for patients having an echo-guided puncture of the main access, particularly with regard to access-related complications, early mobilization, and early discharge home.

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.