Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy
Abstract Sepsis-associated encephalopathy (SAE) is a frequent and severe complication in septic patients, characterized by diffuse brain dysfunction resulting from systemic inflammation. Accurate prediction of long-term mortality in these patients is critical for improving clinical outcomes and guid...
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Nature Portfolio
2024-10-01
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| author | Guangyong Jin Menglu Zhou Jiayi Chen Buqing Ma Jianrong Wang Rui Ye Chunxiao Fang Wei Hu Yanan Dai |
| author_facet | Guangyong Jin Menglu Zhou Jiayi Chen Buqing Ma Jianrong Wang Rui Ye Chunxiao Fang Wei Hu Yanan Dai |
| author_sort | Guangyong Jin |
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| description | Abstract Sepsis-associated encephalopathy (SAE) is a frequent and severe complication in septic patients, characterized by diffuse brain dysfunction resulting from systemic inflammation. Accurate prediction of long-term mortality in these patients is critical for improving clinical outcomes and guiding treatment strategies. We conducted a retrospective cohort study using the MIMIC IV database to identify adult patients diagnosed with SAE. Patients were randomly divided into a training set (70%) and a validation set (30%). Least absolute shrinkage and selection operator regression and multivariate logistic regression were employed to identify significant predictors of 1-year mortality, which were then used to develop a prognostic nomogram. The model’s discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis, respectively. A total of 3,882 SAE patients were included in the analysis. The nomogram demonstrated strong predictive performance with AUCs of 0.881 (95% CI: 0.865, 0.896) in the training set and 0.859 (95% CI: 0.830, 0.888) in the validation set. Calibration plots indicated good agreement between predicted and observed 1-year mortality rates. The decision curve analysis showed that the nomogram provided greater net benefit across a range of threshold probabilities compared to traditional scoring systems such as Glasgow Coma Scale and Sequential Organ Failure Assessment. Our study presents a robust and clinically applicable nomogram for predicting 1-year mortality in SAE patients. This tool offers superior predictive performance compared to existing severity scoring systems and has significant potential to enhance clinical decision-making and patient management in critical care settings. |
| format | Article |
| id | doaj-art-46a65258ed7840d1a7fe3fbd343ba63c |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-46a65258ed7840d1a7fe3fbd343ba63c2025-08-20T02:17:37ZengNature PortfolioScientific Reports2045-23222024-10-0114111310.1038/s41598-024-74837-zComprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathyGuangyong Jin0Menglu Zhou1Jiayi Chen2Buqing Ma3Jianrong Wang4Rui Ye5Chunxiao Fang6Wei Hu7Yanan Dai8Department of Critical Care Medicine, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake UniversityDepartment of Neurology, Affiliated Hospital of Hangzhou Normal UniversityDepartment of Critical Care Medicine, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake UniversityDepartment of Critical Care Medicine, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake UniversityDepartment of Critical Care Medicine, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake UniversityDepartment of Critical Care Medicine, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake UniversityDepartment of Critical Care Medicine, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake UniversityDepartment of Critical Care Medicine, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake UniversityDepartment of Critical Care Medicine, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake UniversityAbstract Sepsis-associated encephalopathy (SAE) is a frequent and severe complication in septic patients, characterized by diffuse brain dysfunction resulting from systemic inflammation. Accurate prediction of long-term mortality in these patients is critical for improving clinical outcomes and guiding treatment strategies. We conducted a retrospective cohort study using the MIMIC IV database to identify adult patients diagnosed with SAE. Patients were randomly divided into a training set (70%) and a validation set (30%). Least absolute shrinkage and selection operator regression and multivariate logistic regression were employed to identify significant predictors of 1-year mortality, which were then used to develop a prognostic nomogram. The model’s discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis, respectively. A total of 3,882 SAE patients were included in the analysis. The nomogram demonstrated strong predictive performance with AUCs of 0.881 (95% CI: 0.865, 0.896) in the training set and 0.859 (95% CI: 0.830, 0.888) in the validation set. Calibration plots indicated good agreement between predicted and observed 1-year mortality rates. The decision curve analysis showed that the nomogram provided greater net benefit across a range of threshold probabilities compared to traditional scoring systems such as Glasgow Coma Scale and Sequential Organ Failure Assessment. Our study presents a robust and clinically applicable nomogram for predicting 1-year mortality in SAE patients. This tool offers superior predictive performance compared to existing severity scoring systems and has significant potential to enhance clinical decision-making and patient management in critical care settings.https://doi.org/10.1038/s41598-024-74837-zCritical careEncephalopathyMalignant cancerNomogramSepsis |
| spellingShingle | Guangyong Jin Menglu Zhou Jiayi Chen Buqing Ma Jianrong Wang Rui Ye Chunxiao Fang Wei Hu Yanan Dai Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy Scientific Reports Critical care Encephalopathy Malignant cancer Nomogram Sepsis |
| title | Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy |
| title_full | Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy |
| title_fullStr | Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy |
| title_full_unstemmed | Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy |
| title_short | Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy |
| title_sort | comprehensive risk factor based nomogram for predicting one year mortality in patients with sepsis associated encephalopathy |
| topic | Critical care Encephalopathy Malignant cancer Nomogram Sepsis |
| url | https://doi.org/10.1038/s41598-024-74837-z |
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