Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacement

Anoushka Dasi; Beom Lee; Venkateshwar Polsani; Pradeep Yadav; Lakshmi Prasad Dasi; Vinod H Thourani

doi:10.1016/j.xjtc.2023.11.011

Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacement

JTCVS Tech. 2023 Nov 30:23:5-17. doi: 10.1016/j.xjtc.2023.11.011. eCollection 2024 Feb.

Authors

Anoushka Dasi¹, Beom Lee², Venkateshwar Polsani³, Pradeep Yadav³, Lakshmi Prasad Dasi², Vinod H Thourani³

Affiliations

¹ Department of Biomedical Engineering, Ohio State University, Columbus, Ohio.
² Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Ga.
³ Department of Cardiac Surgery, Piedmont Heart Institute, Atlanta, Ga.

Abstract

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.

Keywords: AI; TAVR; aortic stenosis; aortic valve.