ABSTRACT
Objective: Traumatic brain injury (TBI) leads to death and disability. This study developed an effective prognostic nomogram for assessing the risk factors for TBI mortality. Method: Data were extracted from an online database called "Multiparameter Intelligent Monitoring in Intensive Care IV" (MIMIC IV). The ICD code obtained data from 2,551 TBI persons (first ICU stay, >18 years old) from this database. R divided samples into 7:3 training and testing cohorts. The univariate analysis determined whether the two cohorts differed statistically in baseline data. This research used forward stepwise logistic regression after independent prognostic factors for these TBI patients. The optimal variables were selected for the model by the optimal subset method. The optimal feature subsets in pattern recognition improved the model prediction, and the minimum BIC forest of the high-dimensional mixed graph model achieved a better prediction effect. A nomogram-labeled TBI-IHM model containing these risk factors was made by nomology in State software. Least Squares OLS was used to build linear models, and then the Receiver Operating Characteristic (ROC) curve was plotted. The TBI-IHM nomogram model's validity was determined by receiver operating characteristic curves (AUCs), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA). Result: The eight features with a minimal BIC model were mannitol use, mechanical ventilation, vasopressor use, international normalized ratio, urea nitrogen, respiratory rate, and cerebrovascular disease. The proposed nomogram (TBI-IHM model) was the best mortality prediction model, with better discrimination and superior model fitting for severely ill TBI patients staying in ICU. The model's receiver operating characteristic curve (ROC) was the best compared to the seven other models. It might be clinically helpful for doctors to make clinical decisions. Conclusion: The proposed nomogram (TBI-IHM model) has significant potential as a clinical utility in predicting mortality in TBI patients.
ABSTRACT
Sepsis-associated encephalopathy(SAE) caused by infections outside the central nervous system always presents extensive brain damage.It is common in clinical practice and associated with a poor prognosis.There are problems in the assessing and diagnosing of SAE.Many factors,such as sedation and mechanical ventilation,make it difficult to assess SAE,while electrophysiological examination may play a role in the assessment.We reviewed the studies of electrophysiological techniques such as electroencephalography and somatosensory evoked potentials for monitoring SAE,hoping to provide certain evidence for the clinical evaluation and diagnosis of SAE.
