A predictive model for sepsis risk in patients with non-traumatic cerebral hemorrhage based on the MIMIC-IV database
Abstract Patients with non-traumatic cerebral hemorrhage admitted to the intensive care unit (ICU) are known to be at high risk for developing sepsis. However, limited research exists to quantify this risk. Therefore, this study aimed to develop a reliable predictive model to assess the risk of seps...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-10119-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849235059762003968 |
|---|---|
| author | Xinxu Wu Fangqi Hu Tianpeng Zhang Yunsong Pan Jie He Rui Zhang Hui Zhou Hui Shi |
| author_facet | Xinxu Wu Fangqi Hu Tianpeng Zhang Yunsong Pan Jie He Rui Zhang Hui Zhou Hui Shi |
| author_sort | Xinxu Wu |
| collection | DOAJ |
| description | Abstract Patients with non-traumatic cerebral hemorrhage admitted to the intensive care unit (ICU) are known to be at high risk for developing sepsis. However, limited research exists to quantify this risk. Therefore, this study aimed to develop a reliable predictive model to assess the risk of sepsis in ICU patients with non-traumatic cerebral hemorrhage. We extracted data on patients admitted to the ICU with non-traumatic cerebral hemorrhage from the Medical Information Mart for Intensive Care IV (MIMIC IV) database. Afterward, the patients were then randomized in a 7:3 ratio into a training set (N = 1,365) and a validation set (N = 585). Least Absolute Shrinkage and Selection Operator (LASSO) regression and stepwise logistic regression were employed to screen variables within the training set. The final logistic regression model was constructed using the identified key predictors. Finally, the model’s performance was evaluated using decision curves, calibration curves, and receiver operating characteristic (ROC) curves. A total of 1,950 patients were included in the study. The training and validation sets comprised 1,365 and 585 patients, respectively. The training set analysis revealed nine crucial predictors for secondary sepsis in ICU patients with non-traumatic cerebral hemorrhage. These factors included liver disease, acidosis, anemia, thrombocytopenia, urinary tract infection, invasive mechanical ventilation, Glasgow Coma Scale (GCS) scores, leukocyte counts, and blood calcium levels. These factors were incorporated into the final model. The area under the ROC curve (AUC) was 0.821 for the training set and 0.845 for the validation set, indicating the model’s high accuracy in predicting sepsis. Calibration curves demonstrated good agreement between the model’s predictions and actual outcomes. Furthermore, the decision curve analysis indicated that the model offers favorable clinical utility. This study successfully developed a dynamic nomogram model for predicting the risk of secondary sepsis in ICU patients with non-traumatic cerebral hemorrhage. The model is expected to provide valuable predictive information to facilitate timely interventions by healthcare professionals. |
| format | Article |
| id | doaj-art-2ca3e93fa0144cf397e4f0ed8307b0c0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2ca3e93fa0144cf397e4f0ed8307b0c02025-08-20T04:02:55ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-10119-6A predictive model for sepsis risk in patients with non-traumatic cerebral hemorrhage based on the MIMIC-IV databaseXinxu Wu0Fangqi Hu1Tianpeng Zhang2Yunsong Pan3Jie He4Rui Zhang5Hui Zhou6Hui Shi7Department of Neurosurgery, Lianyungang Clinical Medical College, Xuzhou Medical UniversityDepartment of Neurosurgery, The First People’s Hospital of Lianyungang CityDepartment of Neurosurgery, Lianyungang Clinical Medical College, Xuzhou Medical UniversityDepartment of Neurosurgery, Lianyungang Clinical Medical College, Nanjing Medical UniversityDepartment of Neurosurgery, Lianyungang Clinical Medical College, Xuzhou Medical UniversityDepartment of Neurosurgery, Lianyungang Clinical Medical College, Xuzhou Medical UniversityDepartment of Neurosurgery, The First People’s Hospital of Lianyungang CityDepartment of Neurosurgery, The First People’s Hospital of Lianyungang CityAbstract Patients with non-traumatic cerebral hemorrhage admitted to the intensive care unit (ICU) are known to be at high risk for developing sepsis. However, limited research exists to quantify this risk. Therefore, this study aimed to develop a reliable predictive model to assess the risk of sepsis in ICU patients with non-traumatic cerebral hemorrhage. We extracted data on patients admitted to the ICU with non-traumatic cerebral hemorrhage from the Medical Information Mart for Intensive Care IV (MIMIC IV) database. Afterward, the patients were then randomized in a 7:3 ratio into a training set (N = 1,365) and a validation set (N = 585). Least Absolute Shrinkage and Selection Operator (LASSO) regression and stepwise logistic regression were employed to screen variables within the training set. The final logistic regression model was constructed using the identified key predictors. Finally, the model’s performance was evaluated using decision curves, calibration curves, and receiver operating characteristic (ROC) curves. A total of 1,950 patients were included in the study. The training and validation sets comprised 1,365 and 585 patients, respectively. The training set analysis revealed nine crucial predictors for secondary sepsis in ICU patients with non-traumatic cerebral hemorrhage. These factors included liver disease, acidosis, anemia, thrombocytopenia, urinary tract infection, invasive mechanical ventilation, Glasgow Coma Scale (GCS) scores, leukocyte counts, and blood calcium levels. These factors were incorporated into the final model. The area under the ROC curve (AUC) was 0.821 for the training set and 0.845 for the validation set, indicating the model’s high accuracy in predicting sepsis. Calibration curves demonstrated good agreement between the model’s predictions and actual outcomes. Furthermore, the decision curve analysis indicated that the model offers favorable clinical utility. This study successfully developed a dynamic nomogram model for predicting the risk of secondary sepsis in ICU patients with non-traumatic cerebral hemorrhage. The model is expected to provide valuable predictive information to facilitate timely interventions by healthcare professionals.https://doi.org/10.1038/s41598-025-10119-6Non-traumatic cerebral hemorrhageStrokeSepsisIntensive care unitMIMIC databaseDynamic nomogram |
| spellingShingle | Xinxu Wu Fangqi Hu Tianpeng Zhang Yunsong Pan Jie He Rui Zhang Hui Zhou Hui Shi A predictive model for sepsis risk in patients with non-traumatic cerebral hemorrhage based on the MIMIC-IV database Scientific Reports Non-traumatic cerebral hemorrhage Stroke Sepsis Intensive care unit MIMIC database Dynamic nomogram |
| title | A predictive model for sepsis risk in patients with non-traumatic cerebral hemorrhage based on the MIMIC-IV database |
| title_full | A predictive model for sepsis risk in patients with non-traumatic cerebral hemorrhage based on the MIMIC-IV database |
| title_fullStr | A predictive model for sepsis risk in patients with non-traumatic cerebral hemorrhage based on the MIMIC-IV database |
| title_full_unstemmed | A predictive model for sepsis risk in patients with non-traumatic cerebral hemorrhage based on the MIMIC-IV database |
| title_short | A predictive model for sepsis risk in patients with non-traumatic cerebral hemorrhage based on the MIMIC-IV database |
| title_sort | predictive model for sepsis risk in patients with non traumatic cerebral hemorrhage based on the mimic iv database |
| topic | Non-traumatic cerebral hemorrhage Stroke Sepsis Intensive care unit MIMIC database Dynamic nomogram |
| url | https://doi.org/10.1038/s41598-025-10119-6 |
| work_keys_str_mv | AT xinxuwu apredictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT fangqihu apredictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT tianpengzhang apredictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT yunsongpan apredictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT jiehe apredictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT ruizhang apredictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT huizhou apredictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT huishi apredictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT xinxuwu predictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT fangqihu predictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT tianpengzhang predictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT yunsongpan predictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT jiehe predictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT ruizhang predictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT huizhou predictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase AT huishi predictivemodelforsepsisriskinpatientswithnontraumaticcerebralhemorrhagebasedonthemimicivdatabase |