Machine learning model to predict hypotension after starting continuous renal replacement therapy

Abstract Hypotension after starting continuous renal replacement therapy (CRRT) is associated with worse outcomes compared with normotension, but it is difficult to predict because several factors have interactive and complex effects on the risk. The present study applied machine learning algorithms...

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Autores principales:	Min Woo Kang, Seonmi Kim, Yong Chul Kim, Dong Ki Kim, Kook-Hwan Oh, Kwon Wook Joo, Yon Su Kim, Seung Seok Han
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fe531680bfcd416e8bfb465f14c63a68
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spelling	oai:doaj.org-article:fe531680bfcd416e8bfb465f14c63a682021-12-02T18:53:18ZMachine learning model to predict hypotension after starting continuous renal replacement therapy10.1038/s41598-021-96727-42045-2322https://doaj.org/article/fe531680bfcd416e8bfb465f14c63a682021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96727-4https://doaj.org/toc/2045-2322Abstract Hypotension after starting continuous renal replacement therapy (CRRT) is associated with worse outcomes compared with normotension, but it is difficult to predict because several factors have interactive and complex effects on the risk. The present study applied machine learning algorithms to develop models to predict hypotension after initiating CRRT. Among 2349 adult patients who started CRRT due to acute kidney injury, 70% and 30% were randomly assigned into the training and testing sets, respectively. Hypotension was defined as a reduction in mean arterial pressure (MAP) ≥ 20 mmHg from the initial value within 6 h. The area under the receiver operating characteristic curves (AUROCs) in machine learning models, such as support vector machine (SVM), deep neural network (DNN), light gradient boosting machine (LGBM), and extreme gradient boosting machine (XGB) were compared with those in disease-severity scores such as the Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation II. The XGB model showed the highest AUROC (0.828 [0.796–0.861]), and the DNN and LGBM models followed with AUROCs of 0.822 (0.789–0.856) and 0.813 (0.780–0.847), respectively; all machine learning AUROC values were higher than those obtained from disease-severity scores (AUROCs < 0.6). Although other definitions of hypotension were used such as a reduction of MAP ≥ 30 mmHg or a reduction occurring within 1 h, the AUROCs of machine learning models were higher than those of disease-severity scores. Machine learning models successfully predict hypotension after starting CRRT and can serve as the basis of systems to predict hypotension before starting CRRT.Min Woo KangSeonmi KimYong Chul KimDong Ki KimKook-Hwan OhKwon Wook JooYon Su KimSeung Seok HanNature 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 Min Woo Kang Seonmi Kim Yong Chul Kim Dong Ki Kim Kook-Hwan Oh Kwon Wook Joo Yon Su Kim Seung Seok Han Machine learning model to predict hypotension after starting continuous renal replacement therapy
description	Abstract Hypotension after starting continuous renal replacement therapy (CRRT) is associated with worse outcomes compared with normotension, but it is difficult to predict because several factors have interactive and complex effects on the risk. The present study applied machine learning algorithms to develop models to predict hypotension after initiating CRRT. Among 2349 adult patients who started CRRT due to acute kidney injury, 70% and 30% were randomly assigned into the training and testing sets, respectively. Hypotension was defined as a reduction in mean arterial pressure (MAP) ≥ 20 mmHg from the initial value within 6 h. The area under the receiver operating characteristic curves (AUROCs) in machine learning models, such as support vector machine (SVM), deep neural network (DNN), light gradient boosting machine (LGBM), and extreme gradient boosting machine (XGB) were compared with those in disease-severity scores such as the Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation II. The XGB model showed the highest AUROC (0.828 [0.796–0.861]), and the DNN and LGBM models followed with AUROCs of 0.822 (0.789–0.856) and 0.813 (0.780–0.847), respectively; all machine learning AUROC values were higher than those obtained from disease-severity scores (AUROCs < 0.6). Although other definitions of hypotension were used such as a reduction of MAP ≥ 30 mmHg or a reduction occurring within 1 h, the AUROCs of machine learning models were higher than those of disease-severity scores. Machine learning models successfully predict hypotension after starting CRRT and can serve as the basis of systems to predict hypotension before starting CRRT.
format	article
author	Min Woo Kang Seonmi Kim Yong Chul Kim Dong Ki Kim Kook-Hwan Oh Kwon Wook Joo Yon Su Kim Seung Seok Han
author_facet	Min Woo Kang Seonmi Kim Yong Chul Kim Dong Ki Kim Kook-Hwan Oh Kwon Wook Joo Yon Su Kim Seung Seok Han
author_sort	Min Woo Kang
title	Machine learning model to predict hypotension after starting continuous renal replacement therapy
title_short	Machine learning model to predict hypotension after starting continuous renal replacement therapy
title_full	Machine learning model to predict hypotension after starting continuous renal replacement therapy
title_fullStr	Machine learning model to predict hypotension after starting continuous renal replacement therapy
title_full_unstemmed	Machine learning model to predict hypotension after starting continuous renal replacement therapy
title_sort	machine learning model to predict hypotension after starting continuous renal replacement therapy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fe531680bfcd416e8bfb465f14c63a68
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Machine learning model to predict hypotension after starting continuous renal replacement therapy

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