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Establishment and validation of a logistic regression model for prediction of septic shock severity in children
Yujie Han1, Lili Kang1, Xianghong Liu1
1Department of Neonatal, Qilu Children's Hospital of Shandong University, No. 23976, Huaiyin District, Jinan City, 250022, Shandong, People's Republic of China.
Insights
This study developed a gene expression model to predict septic shock survival in children. The model accurately identifies patients with activated immune pathways, aiding personalized treatment and prognosis.
Area of Science:
- Pediatric critical care medicine
- Genomics and bioinformatics
- Molecular diagnostics
Background:
- Septic shock, a severe sepsis complication, significantly impacts childhood mortality and public health.
- Understanding the molecular mechanisms of septic shock is crucial for improving patient outcomes.
Purpose of the Study:
- To develop a predictive model for septic shock patient survival using gene expression data.
- To identify key molecular pathways associated with septic shock severity and patient prognosis.
Main Methods:
- Analysis of gene expression profiles from septic shock and control samples via the Gene Expression Omnibus (GEO) database.
- Selection of four differentially expressed genes (DEGs) across survivor, non-survivor, and control groups.
- Development of a logistic regression model for survival prediction, validated using cross-validation and ROC analysis.
Main Results:
- The predictive model demonstrated good accuracy in distinguishing between patient outcomes.
- Gene Set Enrichment Analysis (GSEA) revealed activation of the systemic lupus erythematosus pathway in high-risk patients.
- Inactivation of limonene and pinene degradation pathways was observed in the high-risk group.
Conclusions:
- A novel gene expression-based approach can predict septic shock severity and patient survival.
- This predictive model supports personalized treatment strategies and prognostic assessments for pediatric septic shock.
- Identifying activated and inactivated molecular pathways offers insights into disease mechanisms.
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.
