Machine learning prediction models for prognosis of critically ill patients after open-heart surgery

Abstract We aimed to build up multiple machine learning models to predict 30-days mortality, and 3 complications including septic shock, thrombocytopenia, and liver dysfunction after open-heart surgery. Patients who underwent coronary artery bypass surgery, aortic valve replacement, or other heart-r...

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Autores principales:	Zhihua Zhong, Xin Yuan, Shizhen Liu, Yuer Yang, Fanna Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/69eb579772ef469d946cc6b2735f8712
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spelling	oai:doaj.org-article:69eb579772ef469d946cc6b2735f87122021-12-02T14:27:02ZMachine learning prediction models for prognosis of critically ill patients after open-heart surgery10.1038/s41598-021-83020-72045-2322https://doaj.org/article/69eb579772ef469d946cc6b2735f87122021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83020-7https://doaj.org/toc/2045-2322Abstract We aimed to build up multiple machine learning models to predict 30-days mortality, and 3 complications including septic shock, thrombocytopenia, and liver dysfunction after open-heart surgery. Patients who underwent coronary artery bypass surgery, aortic valve replacement, or other heart-related surgeries between 2001 and 2012 were extracted from MIMIC-III databases. Extreme gradient boosting, random forest, artificial neural network, and logistic regression were employed to build models by utilizing fivefold cross-validation and grid search. Receiver operating characteristic curve, area under curve (AUC), decision curve analysis, test accuracy, F1 score, precision, and recall were applied to access the performance. Among 6844 patients enrolled in this study, 215 patients (3.1%) died within 30 days after surgery, part of patients appeared liver dysfunction (248; 3.6%), septic shock (32; 0.5%), and thrombocytopenia (202; 2.9%). XGBoost, selected to be our final model, achieved the best performance with highest AUC and F1 score. AUC and F1 score of XGBoost for 4 outcomes: 0.88 and 0.58 for 30-days mortality, 0.98 and 0.70 for septic shock, 0.88 and 0.55 for thrombocytopenia, 0.89 and 0.40 for liver dysfunction. We developed a promising model, presented as software, to realize monitoring for patients in ICU and to improve prognosis.Zhihua ZhongXin YuanShizhen LiuYuer YangFanna LiuNature 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 Zhihua Zhong Xin Yuan Shizhen Liu Yuer Yang Fanna Liu Machine learning prediction models for prognosis of critically ill patients after open-heart surgery
description	Abstract We aimed to build up multiple machine learning models to predict 30-days mortality, and 3 complications including septic shock, thrombocytopenia, and liver dysfunction after open-heart surgery. Patients who underwent coronary artery bypass surgery, aortic valve replacement, or other heart-related surgeries between 2001 and 2012 were extracted from MIMIC-III databases. Extreme gradient boosting, random forest, artificial neural network, and logistic regression were employed to build models by utilizing fivefold cross-validation and grid search. Receiver operating characteristic curve, area under curve (AUC), decision curve analysis, test accuracy, F1 score, precision, and recall were applied to access the performance. Among 6844 patients enrolled in this study, 215 patients (3.1%) died within 30 days after surgery, part of patients appeared liver dysfunction (248; 3.6%), septic shock (32; 0.5%), and thrombocytopenia (202; 2.9%). XGBoost, selected to be our final model, achieved the best performance with highest AUC and F1 score. AUC and F1 score of XGBoost for 4 outcomes: 0.88 and 0.58 for 30-days mortality, 0.98 and 0.70 for septic shock, 0.88 and 0.55 for thrombocytopenia, 0.89 and 0.40 for liver dysfunction. We developed a promising model, presented as software, to realize monitoring for patients in ICU and to improve prognosis.
format	article
author	Zhihua Zhong Xin Yuan Shizhen Liu Yuer Yang Fanna Liu
author_facet	Zhihua Zhong Xin Yuan Shizhen Liu Yuer Yang Fanna Liu
author_sort	Zhihua Zhong
title	Machine learning prediction models for prognosis of critically ill patients after open-heart surgery
title_short	Machine learning prediction models for prognosis of critically ill patients after open-heart surgery
title_full	Machine learning prediction models for prognosis of critically ill patients after open-heart surgery
title_fullStr	Machine learning prediction models for prognosis of critically ill patients after open-heart surgery
title_full_unstemmed	Machine learning prediction models for prognosis of critically ill patients after open-heart surgery
title_sort	machine learning prediction models for prognosis of critically ill patients after open-heart surgery
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/69eb579772ef469d946cc6b2735f8712
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Machine learning prediction models for prognosis of critically ill patients after open-heart surgery

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