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Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data

The complications of thoracic aortic disease include aortic dissection and aneurysm. The risks are frequently compounded by many cardiovascular comorbidities, which makes the process of clinical decision making complicated. The purpose of this study is to develop risk predictive models for patients...

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Autores principales: Lenivtceva, Iuliia, Panfilov, Dmitri, Kopanitsa, Georgy, Kozlov, Boris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024528/
https://www.ncbi.nlm.nih.gov/pubmed/35455753
http://dx.doi.org/10.3390/jpm12040637
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author Lenivtceva, Iuliia
Panfilov, Dmitri
Kopanitsa, Georgy
Kozlov, Boris
author_facet Lenivtceva, Iuliia
Panfilov, Dmitri
Kopanitsa, Georgy
Kozlov, Boris
author_sort Lenivtceva, Iuliia
collection PubMed
description The complications of thoracic aortic disease include aortic dissection and aneurysm. The risks are frequently compounded by many cardiovascular comorbidities, which makes the process of clinical decision making complicated. The purpose of this study is to develop risk predictive models for patients after thoracic aneurysm surgeries, using integrated data from different medical institutions. Seven risk features were formulated for prediction. The CatBoost classifier performed best and provided an ROC AUC of 0.94–0.98 and an F-score of 0.95–0.98. The obtained results are widely in line with the current literature. The obtained findings provide additional support for clinical decision making, guiding a patient care team prior to surgical treatment, and promoting a safe postoperative period.
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spelling pubmed-90245282022-04-23 Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data Lenivtceva, Iuliia Panfilov, Dmitri Kopanitsa, Georgy Kozlov, Boris J Pers Med Article The complications of thoracic aortic disease include aortic dissection and aneurysm. The risks are frequently compounded by many cardiovascular comorbidities, which makes the process of clinical decision making complicated. The purpose of this study is to develop risk predictive models for patients after thoracic aneurysm surgeries, using integrated data from different medical institutions. Seven risk features were formulated for prediction. The CatBoost classifier performed best and provided an ROC AUC of 0.94–0.98 and an F-score of 0.95–0.98. The obtained results are widely in line with the current literature. The obtained findings provide additional support for clinical decision making, guiding a patient care team prior to surgical treatment, and promoting a safe postoperative period. MDPI 2022-04-15 /pmc/articles/PMC9024528/ /pubmed/35455753 http://dx.doi.org/10.3390/jpm12040637 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lenivtceva, Iuliia
Panfilov, Dmitri
Kopanitsa, Georgy
Kozlov, Boris
Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data
title Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data
title_full Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data
title_fullStr Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data
title_full_unstemmed Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data
title_short Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data
title_sort aortic risks prediction models after cardiac surgeries using integrated data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024528/
https://www.ncbi.nlm.nih.gov/pubmed/35455753
http://dx.doi.org/10.3390/jpm12040637
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