Establishment and validation of a logistic regression model for prediction of septic shock severity in children

Abstract Background Septic shock is the most severe complication of sepsis, and is a major cause of childhood mortality, constituting a heavy public health burden. Methods We analyzed the gene expression profiles of septic shock and control samples from the Gene Expression Omnibus (GEO). Four differ...

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Autores principales:	Yujie Han, Lili Kang, Xianghong Liu, Yuanhua Zhuang, Xiao Chen, Xiaoying Li
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Septic shock Logistic regression model Survival Systemic lupus erythematosus pathway Limonene and pinene degradation pathway Genetics QH426-470
Acceso en línea:	https://doaj.org/article/01dd80d3d594484c94d0ba87f4b528de
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spelling	oai:doaj.org-article:01dd80d3d594484c94d0ba87f4b528de2021-11-14T12:10:58ZEstablishment and validation of a logistic regression model for prediction of septic shock severity in children10.1186/s41065-021-00206-91601-5223https://doaj.org/article/01dd80d3d594484c94d0ba87f4b528de2021-11-01T00:00:00Zhttps://doi.org/10.1186/s41065-021-00206-9https://doaj.org/toc/1601-5223Abstract Background Septic shock is the most severe complication of sepsis, and is a major cause of childhood mortality, constituting a heavy public health burden. Methods We analyzed the gene expression profiles of septic shock and control samples from the Gene Expression Omnibus (GEO). Four differentially expressed genes (DEGs) from survivor and control groups, non-survivor and control groups, and survivor and non-survivor groups were selected. We used data about these genes to establish a logistic regression model for predicting the survival of septic shock patients. Results Leave-one-out cross validation and receiver operating characteristic (ROC) analysis indicated that this model had good accuracy. Differential expression and Gene Set Enrichment Analysis (GSEA) between septic shock patients stratified by prediction score indicated that the systemic lupus erythematosus pathway was activated, while the limonene and pinene degradation pathways were inactivated in the high score group. Conclusions Our study provides a novel approach for the prediction of the severity of pathology in septic shock patients, which are significant for personalized treatment as well as prognostic assessment.Yujie HanLili KangXianghong LiuYuanhua ZhuangXiao ChenXiaoying LiBMCarticleSeptic shockLogistic regression modelSurvivalSystemic lupus erythematosus pathwayLimonene and pinene degradation pathwayGeneticsQH426-470ENHereditas, Vol 158, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Septic shock Logistic regression model Survival Systemic lupus erythematosus pathway Limonene and pinene degradation pathway Genetics QH426-470
spellingShingle	Septic shock Logistic regression model Survival Systemic lupus erythematosus pathway Limonene and pinene degradation pathway Genetics QH426-470 Yujie Han Lili Kang Xianghong Liu Yuanhua Zhuang Xiao Chen Xiaoying Li Establishment and validation of a logistic regression model for prediction of septic shock severity in children
description	Abstract Background Septic shock is the most severe complication of sepsis, and is a major cause of childhood mortality, constituting a heavy public health burden. Methods We analyzed the gene expression profiles of septic shock and control samples from the Gene Expression Omnibus (GEO). Four differentially expressed genes (DEGs) from survivor and control groups, non-survivor and control groups, and survivor and non-survivor groups were selected. We used data about these genes to establish a logistic regression model for predicting the survival of septic shock patients. Results Leave-one-out cross validation and receiver operating characteristic (ROC) analysis indicated that this model had good accuracy. Differential expression and Gene Set Enrichment Analysis (GSEA) between septic shock patients stratified by prediction score indicated that the systemic lupus erythematosus pathway was activated, while the limonene and pinene degradation pathways were inactivated in the high score group. Conclusions Our study provides a novel approach for the prediction of the severity of pathology in septic shock patients, which are significant for personalized treatment as well as prognostic assessment.
format	article
author	Yujie Han Lili Kang Xianghong Liu Yuanhua Zhuang Xiao Chen Xiaoying Li
author_facet	Yujie Han Lili Kang Xianghong Liu Yuanhua Zhuang Xiao Chen Xiaoying Li
author_sort	Yujie Han
title	Establishment and validation of a logistic regression model for prediction of septic shock severity in children
title_short	Establishment and validation of a logistic regression model for prediction of septic shock severity in children
title_full	Establishment and validation of a logistic regression model for prediction of septic shock severity in children
title_fullStr	Establishment and validation of a logistic regression model for prediction of septic shock severity in children
title_full_unstemmed	Establishment and validation of a logistic regression model for prediction of septic shock severity in children
title_sort	establishment and validation of a logistic regression model for prediction of septic shock severity in children
publisher	BMC
publishDate	2021
url	https://doaj.org/article/01dd80d3d594484c94d0ba87f4b528de
work_keys_str_mv	AT yujiehan establishmentandvalidationofalogisticregressionmodelforpredictionofsepticshockseverityinchildren AT lilikang establishmentandvalidationofalogisticregressionmodelforpredictionofsepticshockseverityinchildren AT xianghongliu establishmentandvalidationofalogisticregressionmodelforpredictionofsepticshockseverityinchildren AT yuanhuazhuang establishmentandvalidationofalogisticregressionmodelforpredictionofsepticshockseverityinchildren AT xiaochen establishmentandvalidationofalogisticregressionmodelforpredictionofsepticshockseverityinchildren AT xiaoyingli establishmentandvalidationofalogisticregressionmodelforpredictionofsepticshockseverityinchildren
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Establishment and validation of a logistic regression model for prediction of septic shock severity in children

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