Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy

Objective: The study aims to develop a mechanical learning model as a predictive model for predicting the appearance of sepsis-associated encephalopathy (SAE).Materials and Methods: The prediction model was developed in a primary cohort of 2,028 sepsis patients from June 2001 to October 2012, retrie...

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Auteurs principaux:	Lina Zhao, Yunying Wang, Zengzheng Ge, Huadong Zhu, Yi Li
Format:	article
Langue:	EN
Publié:	Frontiers Media S.A. 2021
Sujets:	sepsis sepsis-associated encephalopathy encephalopathy delirium nomogram mechanical learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Accès en ligne:	https://doaj.org/article/e7111c7ce5fc4ff788a3892c1ce480ad
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spelling	oai:doaj.org-article:e7111c7ce5fc4ff788a3892c1ce480ad2021-11-16T07:29:49ZMechanical Learning for Prediction of Sepsis-Associated Encephalopathy1662-518810.3389/fncom.2021.739265https://doaj.org/article/e7111c7ce5fc4ff788a3892c1ce480ad2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fncom.2021.739265/fullhttps://doaj.org/toc/1662-5188Objective: The study aims to develop a mechanical learning model as a predictive model for predicting the appearance of sepsis-associated encephalopathy (SAE).Materials and Methods: The prediction model was developed in a primary cohort of 2,028 sepsis patients from June 2001 to October 2012, retrieved from the Medical Information Mart for Intensive Care (MIMIC III) database. Least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction and feature selection. The model was developed using multivariable logistic regression analysis. The performance of the nomogram has been evaluated in terms of calibration, discrimination, and clinical utility.Results: There were nine particular features in septic patients that were significantly associated with SAE. Predictors of individualized prediction nomograms included age, rapid sequential evaluation of organ failure (qSOFA), and drugs including carbapenem antibiotics, quinolone antibiotics, steroids, midazolam, H2-antagonist, diphenhydramine hydrochloride, and heparin sodium injection. The area under the curve (AUC) was 0.743, indicating good discrimination. The prediction model showed calibration curves with minor deviations from the ideal predictions. Decision curve analysis (DCA) suggested that the nomogram was clinically useful.Conclusion: We propose a nomogram for the individualized prediction of SAE with satisfactory performance and clinical utility, which could aid the clinician in the early detection and management of SAE.Lina ZhaoYunying WangZengzheng GeHuadong ZhuYi LiFrontiers Media S.A.articlesepsissepsis-associated encephalopathyencephalopathydeliriumnomogrammechanical learningNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Computational Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sepsis sepsis-associated encephalopathy encephalopathy delirium nomogram mechanical learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	sepsis sepsis-associated encephalopathy encephalopathy delirium nomogram mechanical learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Lina Zhao Yunying Wang Zengzheng Ge Huadong Zhu Yi Li Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy
description	Objective: The study aims to develop a mechanical learning model as a predictive model for predicting the appearance of sepsis-associated encephalopathy (SAE).Materials and Methods: The prediction model was developed in a primary cohort of 2,028 sepsis patients from June 2001 to October 2012, retrieved from the Medical Information Mart for Intensive Care (MIMIC III) database. Least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction and feature selection. The model was developed using multivariable logistic regression analysis. The performance of the nomogram has been evaluated in terms of calibration, discrimination, and clinical utility.Results: There were nine particular features in septic patients that were significantly associated with SAE. Predictors of individualized prediction nomograms included age, rapid sequential evaluation of organ failure (qSOFA), and drugs including carbapenem antibiotics, quinolone antibiotics, steroids, midazolam, H2-antagonist, diphenhydramine hydrochloride, and heparin sodium injection. The area under the curve (AUC) was 0.743, indicating good discrimination. The prediction model showed calibration curves with minor deviations from the ideal predictions. Decision curve analysis (DCA) suggested that the nomogram was clinically useful.Conclusion: We propose a nomogram for the individualized prediction of SAE with satisfactory performance and clinical utility, which could aid the clinician in the early detection and management of SAE.
format	article
author	Lina Zhao Yunying Wang Zengzheng Ge Huadong Zhu Yi Li
author_facet	Lina Zhao Yunying Wang Zengzheng Ge Huadong Zhu Yi Li
author_sort	Lina Zhao
title	Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy
title_short	Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy
title_full	Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy
title_fullStr	Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy
title_full_unstemmed	Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy
title_sort	mechanical learning for prediction of sepsis-associated encephalopathy
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/e7111c7ce5fc4ff788a3892c1ce480ad
work_keys_str_mv	AT linazhao mechanicallearningforpredictionofsepsisassociatedencephalopathy AT yunyingwang mechanicallearningforpredictionofsepsisassociatedencephalopathy AT zengzhengge mechanicallearningforpredictionofsepsisassociatedencephalopathy AT huadongzhu mechanicallearningforpredictionofsepsisassociatedencephalopathy AT yili mechanicallearningforpredictionofsepsisassociatedencephalopathy
_version_	1718426639382085632

Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy

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