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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Hu, Fangqi Zhu, Jiaqiu Zhang, Sheng Wang, Cheng Zhang, Liangjia Zhou, Hui Shi, Hui |
| Abstract | Purpose Traumatic brain injury (TBI) patients admitted to the intensive care unit (ICU) are at a high risk of infection and sepsis. However, there are few studies on predicting secondary sepsis in TBI patients in the ICU. This study aimed to build a prediction model for the risk of secondary sepsis in TBI patients in the ICU, and provide effective information for clinical diagnosis and treatment. Methods Using the MIMIC IV database version 2.0 (Medical Information Mart for Intensive Care IV), we searched data on TBI patients admitted to ICU and considered them as a study cohort. The extracted data included patient demographic information, laboratory indicators, complications, and other clinical data. The study cohort was divided into a training cohort and a validation cohort. In the training cohort, variables were screened by LASSO (Least absolute shrinkage and selection operator) regression and stepwise Logistic regression to assess the predictive ability of each feature on the incidence of patients. The screened variables were included in the final Logistic regression model. Finally, the decision curve, calibration curve, and receiver operating character (ROC) were used to test the performance of the model. Results Finally, a total of 1167 patients were included in the study, and these patients were randomly divided into the training (N = 817) and validation (N = 350) cohorts at a ratio of 7:3. In the training cohort, seven features were identified as key predictors of secondary sepsis in TBI patients in the ICU, including acute kidney injury (AKI), anemia, invasive ventilation, GCS (Glasgow Coma Scale) score, lactic acid, and blood calcium level, which were included in the final model. The areas under the ROC curve in the training cohort and the validation cohort were 0.756 and 0.711, respectively. The calibration curve and ROC curve show that the model has favorable predictive accuracy, while the decision curve shows that the model has favorable clinical benefits with good and robust predictive efficiency. Conclusion We have developed a nomogram model for predicting secondary sepsis in TBI patients admitted to the ICU, which can provide useful predictive information for clinical decision-making. |
| Related Links | https://eurjmedres.biomedcentral.com/counter/pdf/10.1186/s40001-023-01255-8.pdf |
| Ending Page | 11 |
| Page Count | 11 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| DOI | 10.1186/s40001-023-01255-8 |
| Journal | European Journal of Medical Research |
| Issue Number | 1 |
| Volume Number | 28 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2023-08-18 |
| Access Restriction | Open |
| Subject Keyword | Medicine Public Health Infectious Diseases Internal Medicine Surgery Oncology Biomedicine Traumatic brain injury Sepsis Intensive care unit MICMIC database Nomogram Medicine/Public Health |
| Content Type | Text |
| Resource Type | Article |
| Subject | Medicine |
| Journal Impact Factor | 2.8/2023 |
| 5-Year Journal Impact Factor | 2.9/2023 |
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