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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9024528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lenivtcevaiuliia aorticriskspredictionmodelsaftercardiacsurgeriesusingintegrateddata AT panfilovdmitri aorticriskspredictionmodelsaftercardiacsurgeriesusingintegrateddata AT kopanitsageorgy aorticriskspredictionmodelsaftercardiacsurgeriesusingintegrateddata AT kozlovboris aorticriskspredictionmodelsaftercardiacsurgeriesusingintegrateddata |