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Liu X, Niu H, Peng J. Enhancing predictions with a stacking ensemble model for ICU mortality risk in patients with sepsis-associated encephalopathy. J Int Med Res 2024; 52:3000605241239013. [PMID: 38530021 DOI: 10.1177/03000605241239013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024] Open
Abstract
OBJECTIVE We identified predictive factors and developed a novel machine learning (ML) model for predicting mortality risk in patients with sepsis-associated encephalopathy (SAE). METHODS In this retrospective cohort study, data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database were used for model development and external validation. The primary outcome was the in-hospital mortality rate among patients with SAE; the observed in-hospital mortality rate was 14.74% (MIMIC IV: 1112, eICU: 594). Using the least absolute shrinkage and selection operator (LASSO), we built nine ML models and a stacking ensemble model and determined the optimal model based on the area under the receiver operating characteristic curve (AUC). We used the Shapley additive explanations (SHAP) algorithm to determine the optimal model. RESULTS The study included 9943 patients. LASSO identified 15 variables. The stacking ensemble model achieved the highest AUC on the test set (0.807) and 0.671 on external validation. SHAP analysis highlighted Glasgow Coma Scale (GCS) and age as key variables. The model (https://sic1.shinyapps.io/SSAAEE/) can predict in-hospital mortality risk for patients with SAE. CONCLUSIONS We developed a stacked ensemble model with enhanced generalization capabilities using novel data to predict mortality risk in patients with SAE.
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Affiliation(s)
- Xuhui Liu
- Baise People's Hospital, Baise, Guangxi Province, China
- Department of Critical Care Medicine, Affiliated Southwest Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi Province, China
| | - Hao Niu
- Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiahua Peng
- Department of Critical Care Medicine, Affiliated Southwest Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi Province, China
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Zhang J, Chen S, Hu X, Huang L, Loh P, Yuan X, Liu Z, Lian J, Geng L, Chen Z, Guo Y, Chen B. The role of the peripheral system dysfunction in the pathogenesis of sepsis-associated encephalopathy. Front Microbiol 2024; 15:1337994. [PMID: 38298892 PMCID: PMC10828041 DOI: 10.3389/fmicb.2024.1337994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/04/2024] [Indexed: 02/02/2024] Open
Abstract
Sepsis is a condition that greatly impacts the brain, leading to neurological dysfunction and heightened mortality rates, making it one of the primary organs affected. Injury to the central nervous system can be attributed to dysfunction of various organs throughout the entire body and imbalances within the peripheral immune system. Furthermore, central nervous system injury can create a vicious circle with infection-induced peripheral immune disorders. We collate the pathogenesis of septic encephalopathy, which involves microglial activation, programmed cell death, mitochondrial dysfunction, endoplasmic reticulum stress, neurotransmitter imbalance, and blood-brain barrier disruption. We also spotlight the effects of intestinal flora and its metabolites, enterocyte-derived exosomes, cholinergic anti-inflammatory pathway, peripheral T cells and their cytokines on septic encephalopathy.
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Affiliation(s)
- Jingyu Zhang
- Research Center of Experimental Acupuncture Science, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Shuangli Chen
- Research Center of Experimental Acupuncture Science, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xiyou Hu
- Research Center of Experimental Acupuncture Science, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Lihong Huang
- Research Center of Experimental Acupuncture Science, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - PeiYong Loh
- School of International Education, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xinru Yuan
- Research Center of Experimental Acupuncture Science, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Zhen Liu
- Research Center of Experimental Acupuncture Science, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Jinyu Lian
- Research Center of Experimental Acupuncture Science, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Lianqi Geng
- Binhai New Area Hospital of TCM, Fourth Teaching Hospital of Tianjin University of TCM, Tianjin, China
| | - Zelin Chen
- Research Center of Experimental Acupuncture Science, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Tianjin Key Laboratory of Modern Chinese Medicine Theory of Innovation and Application, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- School of Acupuncture and Moxibustion and Tuina, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Yi Guo
- Research Center of Experimental Acupuncture Science, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Tianjin Key Laboratory of Modern Chinese Medicine Theory of Innovation and Application, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Bo Chen
- Research Center of Experimental Acupuncture Science, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Binhai New Area Hospital of TCM, Fourth Teaching Hospital of Tianjin University of TCM, Tianjin, China
- Tianjin Key Laboratory of Modern Chinese Medicine Theory of Innovation and Application, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- School of Acupuncture and Moxibustion and Tuina, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
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Zhao X, Wan X, Gu C, Gao S, Yin J, Wang L, Quan L. Association between Red Blood Cell Distribution Width and Short-Term Mortality in Patients with Paralytic Intestinal Obstruction: Retrospective Data Analysis Based on the MIMIC-III Database. Emerg Med Int 2023; 2023:6739136. [PMID: 37908808 PMCID: PMC10615582 DOI: 10.1155/2023/6739136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 11/10/2022] [Accepted: 11/26/2022] [Indexed: 11/02/2023] Open
Abstract
Objective Elevated red cell distribution (RDW) has been reported to be associated with mortality in patients with acute pancreatitis and cholecystitis admitted to the intensive care unit (ICU). However, evidence for the relationship between RDW and paralytic intestinal obstruction is lacking. Therefore, the article aims to investigate the relationship between RDW and 28-day mortality of the patients with paralytic intestinal obstruction. Patients and Methods. This is a single-center retrospective study. Based on a particular screening criterion, 773 patients with paralytic intestinal obstruction were selected from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Indicators of the first 24 h into the ICU were used to analyze the relationship between RDW and 28-day death from paralytic intestinal obstruction by Kaplan-Meier (K-M) analysis, logistic regression analysis, and stratification analysis. Results The curve fitting exhibited a nonlinear relationship. The K-M curve showed that groups with higher RDW values had lower survival rates. The logistic regression analysis revealed that RDW increased with 28-day mortality in patients with paralytic intestinal obstruction in the fully adjusted model. In the fully adjusted model, OR value and 95% CI from the second to the third quantiles compared to the first quartile (reference group) were 1.89 (1.04, 3.44) and 3.29 (1.82, 5.93), respectively. The results of stratified analysis of each layer had the same trend as those of regression analysis, and the interaction results were not significant. Conclusion Elevated RDW was associated with increased 28-day mortality from paralytic intestinal obstruction in the ICU. This study can help to further explore the relationship between RDW and death in patients with paralytic intestinal obstruction.
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Affiliation(s)
- Xuelian Zhao
- The First Clinical College, Shandong University of Traditional Chinese Medicine, Jinan 250013, Shandong Province, China
| | - Xinhuan Wan
- School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250013, Shandong Province, China
| | - Chao Gu
- Department of Anorectal, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250014, Shandong Province, China
| | - Shanyu Gao
- Department of Anorectal, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250014, Shandong Province, China
| | - Jiahui Yin
- School of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250013, Shandong Province, China
| | - Lizhu Wang
- Department of Anorectal, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250014, Shandong Province, China
| | - Longfang Quan
- Department of Anorectal, China Academy of Chinese Medical Sciences Xiyuan Hospital, Beijing 100091, China
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Zhang Y, Hu J, Hua T, Zhang J, Zhang Z, Yang M. Development of a machine learning-based prediction model for sepsis-associated delirium in the intensive care unit. Sci Rep 2023; 13:12697. [PMID: 37542106 PMCID: PMC10403605 DOI: 10.1038/s41598-023-38650-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 07/12/2023] [Indexed: 08/06/2023] Open
Abstract
Septic patients in the intensive care unit (ICU) often develop sepsis-associated delirium (SAD), which is strongly associated with poor prognosis. The aim of this study is to develop a machine learning-based model for the early prediction of SAD. Patient data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV data were divided into a training set and an internal validation set, while the eICU-CRD data served as an external validation set. Feature variables were selected using least absolute shrinkage and selection operator regression, and prediction models were built using logistic regression, support vector machines, decision trees, random forests, extreme gradient boosting (XGBoost), k-nearest neighbors and naive Bayes methods. The performance of the models was evaluated in the validation set. The model was also applied to a group of patients who were not assessed or could not be assessed for delirium. The MIMIC-IV and eICU-CRD databases included 14,620 and 1723 patients, respectively, with a median time to diagnosis of SAD of 24 and 30 h. Compared with Non-SAD patients, SAD patients had higher 28-days ICU mortality rates and longer ICU stays. Among the models compared, the XGBoost model had the best performance and was selected as the final model (internal validation area under the receiver operating characteristic curves (AUROC) = 0.793, external validation AUROC = 0.701). The XGBoost model outperformed other models in predicting SAD. The establishment of this predictive model allows for earlier prediction of SAD compared to traditional delirium assessments and is applicable to patients who are difficult to assess with traditional methods.
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Affiliation(s)
- Yang Zhang
- The Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, People's Republic of China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, People's Republic of China
| | - Juanjuan Hu
- The Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, People's Republic of China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, People's Republic of China
| | - Tianfeng Hua
- The Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, People's Republic of China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, People's Republic of China
| | - Jin Zhang
- The Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, People's Republic of China
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, People's Republic of China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3, Hangzhou, 310016, Zhejiang, People's Republic of China
| | - Min Yang
- The Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, People's Republic of China.
- The Laboratory of Cardiopulmonary Resuscitation and Critical Care, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, People's Republic of China.
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