Predicting mortality after transcatheter aortic valve replacement using preprocedural CT
Abstract Transcatheter aortic valve replacement (TAVR) is a widely used intervention for patients with severe aortic stenosis. Identifying high-risk patients is crucial due to potential postprocedural complications. Currently, this involves manual clinical assessment and time-consuming radiological...
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Nature Portfolio
2024-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-63022-x |
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| author | David Brüggemann Denis Cener Nazar Kuzo Shehab Anwer Julia Kebernik Matthias Eberhard Hatem Alkadhi Felix C. Tanner Ender Konukoglu |
| author_facet | David Brüggemann Denis Cener Nazar Kuzo Shehab Anwer Julia Kebernik Matthias Eberhard Hatem Alkadhi Felix C. Tanner Ender Konukoglu |
| author_sort | David Brüggemann |
| collection | DOAJ |
| description | Abstract Transcatheter aortic valve replacement (TAVR) is a widely used intervention for patients with severe aortic stenosis. Identifying high-risk patients is crucial due to potential postprocedural complications. Currently, this involves manual clinical assessment and time-consuming radiological assessment of preprocedural computed tomography (CT) images by an expert radiologist. In this study, we introduce a probabilistic model that predicts post-TAVR mortality automatically using unprocessed, preprocedural CT and 25 baseline patient characteristics. The model utilizes CT volumes by automatically localizing and extracting a region of interest around the aortic root and ascending aorta. It then extracts task-specific features with a 3D deep neural network and integrates them with patient characteristics to perform outcome prediction. As missing measurements or even missing CT images are common in TAVR planning, the proposed model is designed with a probabilistic structure to allow for marginalization over such missing information. Our model demonstrates an AUROC of 0.725 for predicting all-cause mortality during postprocedure follow-up on a cohort of 1449 TAVR patients. This performance is on par with what can be achieved with lengthy radiological assessments performed by experts. Thus, these findings underscore the potential of the proposed model in automatically analyzing CT volumes and integrating them with patient characteristics for predicting mortality after TAVR. |
| format | Article |
| id | doaj-art-eb877de8ad714cb1af1c4967d8b8aeac |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-eb877de8ad714cb1af1c4967d8b8aeac2025-08-20T02:10:12ZengNature PortfolioScientific Reports2045-23222024-05-0114111110.1038/s41598-024-63022-xPredicting mortality after transcatheter aortic valve replacement using preprocedural CTDavid Brüggemann0Denis Cener1Nazar Kuzo2Shehab Anwer3Julia Kebernik4Matthias Eberhard5Hatem Alkadhi6Felix C. Tanner7Ender Konukoglu8Computer Vision Laboratory, ETH ZurichComputer Vision Laboratory, ETH ZurichDepartment of Cardiology, University Heart Center, University Hospital ZurichDepartment of Cardiology, University Heart Center, University Hospital ZurichInstitute for Diagnostic and Interventional Radiology, University Hospital ZurichInstitute for Diagnostic and Interventional Radiology, University Hospital ZurichInstitute for Diagnostic and Interventional Radiology, University Hospital ZurichDepartment of Cardiology, University Heart Center, University Hospital ZurichComputer Vision Laboratory, ETH ZurichAbstract Transcatheter aortic valve replacement (TAVR) is a widely used intervention for patients with severe aortic stenosis. Identifying high-risk patients is crucial due to potential postprocedural complications. Currently, this involves manual clinical assessment and time-consuming radiological assessment of preprocedural computed tomography (CT) images by an expert radiologist. In this study, we introduce a probabilistic model that predicts post-TAVR mortality automatically using unprocessed, preprocedural CT and 25 baseline patient characteristics. The model utilizes CT volumes by automatically localizing and extracting a region of interest around the aortic root and ascending aorta. It then extracts task-specific features with a 3D deep neural network and integrates them with patient characteristics to perform outcome prediction. As missing measurements or even missing CT images are common in TAVR planning, the proposed model is designed with a probabilistic structure to allow for marginalization over such missing information. Our model demonstrates an AUROC of 0.725 for predicting all-cause mortality during postprocedure follow-up on a cohort of 1449 TAVR patients. This performance is on par with what can be achieved with lengthy radiological assessments performed by experts. Thus, these findings underscore the potential of the proposed model in automatically analyzing CT volumes and integrating them with patient characteristics for predicting mortality after TAVR.https://doi.org/10.1038/s41598-024-63022-x |
| spellingShingle | David Brüggemann Denis Cener Nazar Kuzo Shehab Anwer Julia Kebernik Matthias Eberhard Hatem Alkadhi Felix C. Tanner Ender Konukoglu Predicting mortality after transcatheter aortic valve replacement using preprocedural CT Scientific Reports |
| title | Predicting mortality after transcatheter aortic valve replacement using preprocedural CT |
| title_full | Predicting mortality after transcatheter aortic valve replacement using preprocedural CT |
| title_fullStr | Predicting mortality after transcatheter aortic valve replacement using preprocedural CT |
| title_full_unstemmed | Predicting mortality after transcatheter aortic valve replacement using preprocedural CT |
| title_short | Predicting mortality after transcatheter aortic valve replacement using preprocedural CT |
| title_sort | predicting mortality after transcatheter aortic valve replacement using preprocedural ct |
| url | https://doi.org/10.1038/s41598-024-63022-x |
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