Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement

Abstract Cerebrovascular events (CVE) are among the most feared complications of transcatheter aortic valve replacement (TAVR). CVE appear difficult to predict due to their multifactorial origin incompletely explained by clinical predictors. We aimed to build a deep learning-based predictive tool fo...

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Autores principales:	Taishi Okuno, Pavel Overtchouk, Masahiko Asami, Daijiro Tomii, Stefan Stortecky, Fabien Praz, Jonas Lanz, George C. M. Siontis, Christoph Gräni, Stephan Windecker, Thomas Pilgrim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/58810c58ee17474f9308606b1ebbbfdf
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spelling	oai:doaj.org-article:58810c58ee17474f9308606b1ebbbfdf2021-12-02T18:48:09ZDeep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement10.1038/s41598-021-98265-52045-2322https://doaj.org/article/58810c58ee17474f9308606b1ebbbfdf2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98265-5https://doaj.org/toc/2045-2322Abstract Cerebrovascular events (CVE) are among the most feared complications of transcatheter aortic valve replacement (TAVR). CVE appear difficult to predict due to their multifactorial origin incompletely explained by clinical predictors. We aimed to build a deep learning-based predictive tool for TAVR-related CVE. Integrated clinical and imaging characteristics from consecutive patients enrolled into a prospective TAVR registry were analysed. CVE comprised any strokes and transient ischemic attacks. Predictive variables were selected by recursive feature reduction to train an autoencoder predictive model. Area under the curve (AUC) represented the model’s performance to predict 30-day CVE. Among 2279 patients included between 2007 and 2019, both clinical and imaging data were available in 1492 patients. Median age was 83 years and STS score was 4.6%. Acute (< 24 h) and subacute (day 2–30) CVE occurred in 19 (1.3%) and 36 (2.4%) patients, respectively. The occurrence of CVE was associated with an increased risk of death (HR [95% CI] 2.62 [1.82–3.78]). The constructed predictive model uses less than 107 clinical and imaging variables and has an AUC of 0.79 (0.65–0.93). TAVR-related CVE can be predicted using a deep learning-based predictive algorithm. The model is implemented online for broad usage.Taishi OkunoPavel OvertchoukMasahiko AsamiDaijiro TomiiStefan StorteckyFabien PrazJonas LanzGeorge C. M. SiontisChristoph GräniStephan WindeckerThomas PilgrimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Taishi Okuno Pavel Overtchouk Masahiko Asami Daijiro Tomii Stefan Stortecky Fabien Praz Jonas Lanz George C. M. Siontis Christoph Gräni Stephan Windecker Thomas Pilgrim Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement
description	Abstract Cerebrovascular events (CVE) are among the most feared complications of transcatheter aortic valve replacement (TAVR). CVE appear difficult to predict due to their multifactorial origin incompletely explained by clinical predictors. We aimed to build a deep learning-based predictive tool for TAVR-related CVE. Integrated clinical and imaging characteristics from consecutive patients enrolled into a prospective TAVR registry were analysed. CVE comprised any strokes and transient ischemic attacks. Predictive variables were selected by recursive feature reduction to train an autoencoder predictive model. Area under the curve (AUC) represented the model’s performance to predict 30-day CVE. Among 2279 patients included between 2007 and 2019, both clinical and imaging data were available in 1492 patients. Median age was 83 years and STS score was 4.6%. Acute (< 24 h) and subacute (day 2–30) CVE occurred in 19 (1.3%) and 36 (2.4%) patients, respectively. The occurrence of CVE was associated with an increased risk of death (HR [95% CI] 2.62 [1.82–3.78]). The constructed predictive model uses less than 107 clinical and imaging variables and has an AUC of 0.79 (0.65–0.93). TAVR-related CVE can be predicted using a deep learning-based predictive algorithm. The model is implemented online for broad usage.
format	article
author	Taishi Okuno Pavel Overtchouk Masahiko Asami Daijiro Tomii Stefan Stortecky Fabien Praz Jonas Lanz George C. M. Siontis Christoph Gräni Stephan Windecker Thomas Pilgrim
author_facet	Taishi Okuno Pavel Overtchouk Masahiko Asami Daijiro Tomii Stefan Stortecky Fabien Praz Jonas Lanz George C. M. Siontis Christoph Gräni Stephan Windecker Thomas Pilgrim
author_sort	Taishi Okuno
title	Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement
title_short	Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement
title_full	Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement
title_fullStr	Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement
title_full_unstemmed	Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement
title_sort	deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/58810c58ee17474f9308606b1ebbbfdf
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Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement

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