Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacement.

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. 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. The mean absolute error for pressure gradient and aortic valve area predictions was 3.0 mm Hg and 0.45 cm 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.

Informations de copyright

© 2023 The Author(s).

Déclaration de conflit d'intérêts

A.D. reports having patent applications filed on novel polymeric valves, vortex generators, superhydrophobic/omniphobic surfaces, and predicting leaflet thrombosis modeling. V.H.T. is a consultant for Abbott Vascular, Boston Scientific, Edwards Lifesciences, CryoLife, Shockwave, and JenaValve. A.D. and V.H.T. have filed a patent application on computational predictive modeling of thrombosis in heart valves. All other authors reported no conflicts of interest. The Journal policy requires editors and reviewers to disclose conflicts of interest and to decline handling or reviewing manuscripts for which they may have a conflict of interest. The editors and reviewers of this article have no conflicts of interest.