Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacementCentral MessagePerspective
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | JTCVS Techniques |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666250723004625 |
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| author | Anoushka Dasi, BS Beom Lee, MS Venkateshwar Polsani, MD, FACC, FASE Pradeep Yadav, MD, FACC Lakshmi Prasad Dasi, PhD, FACC, FAIMBE Vinod H. Thourani, MD, FACS, FACC |
| author_facet | Anoushka Dasi, BS Beom Lee, MS Venkateshwar Polsani, MD, FACC, FASE Pradeep Yadav, MD, FACC Lakshmi Prasad Dasi, PhD, FACC, FAIMBE Vinod H. Thourani, MD, FACS, FACC |
| author_sort | Anoushka Dasi, BS |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0744ee7223ba4d70897a0bd2cb4db982 |
| institution | Kabale University |
| issn | 2666-2507 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | JTCVS Techniques |
| spelling | doaj-art-0744ee7223ba4d70897a0bd2cb4db9822025-08-20T03:38:27ZengElsevierJTCVS Techniques2666-25072024-02-012351710.1016/j.xjtc.2023.11.011Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacementCentral MessagePerspectiveAnoushka Dasi, BS0Beom Lee, MS1Venkateshwar Polsani, MD, FACC, FASE2Pradeep Yadav, MD, FACC3Lakshmi Prasad Dasi, PhD, FACC, FAIMBE4Vinod H. Thourani, MD, FACS, FACC5Department of Biomedical Engineering, Ohio State University, Columbus, OhioDepartment of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GaDepartment of Cardiac Surgery, Piedmont Heart Institute, Atlanta, GaDepartment of Cardiac Surgery, Piedmont Heart Institute, Atlanta, GaDepartment of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GaDepartment of Cardiac Surgery, Piedmont Heart Institute, Atlanta, Ga; Address for reprints: Vinod H. Thourani, MD, FACS, FACC, Department of Cardiovascular Surgery, Piedmont Heart Institute, 95 Collier Rd Suite 5015, Atlanta, GA 30309.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.http://www.sciencedirect.com/science/article/pii/S2666250723004625AIaortic stenosisaortic valveTAVR |
| spellingShingle | Anoushka Dasi, BS Beom Lee, MS Venkateshwar Polsani, MD, FACC, FASE Pradeep Yadav, MD, FACC Lakshmi Prasad Dasi, PhD, FACC, FAIMBE Vinod H. Thourani, MD, FACS, FACC Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacementCentral MessagePerspective JTCVS Techniques AI aortic stenosis aortic valve TAVR |
| title | Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacementCentral MessagePerspective |
| title_full | Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacementCentral MessagePerspective |
| title_fullStr | Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacementCentral MessagePerspective |
| title_full_unstemmed | Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacementCentral MessagePerspective |
| title_short | Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacementCentral MessagePerspective |
| title_sort | predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacementcentral messageperspective |
| topic | AI aortic stenosis aortic valve TAVR |
| url | http://www.sciencedirect.com/science/article/pii/S2666250723004625 |
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