Risk factors and predictive model of adrenocortical insufficiency in patients with traumatic brain injury

Abstract

BACKGROUND: Neuroendocrine dysfunction after traumatic brain injury (TBI) has received increased attention due to its impact on the recovery of neural function. The purpose of this study is to investigate the incidence and risk factors of adrenocortical insuffi ciency (AI) after TBI to reveal independent predictors and build a prediction model of AI after TBI.METHODS: Enrolled patients were grouped into the AI and non-AI groups. Fourteen preset impact factors were recorded. Patients were regrouped according to each impact factor as a categorical variable. Univariate and multiple logistic regression analyses were performed to screen the related independent risk factors of AI after TBI and develop the predictive model. RESULTS: A total of 108 patients were recruited, of whom 34 (31.5%) patients had AI. Nine factors (age, Glasgow Coma Scale [GCS] score on admission, mean arterial pressure [MAP], urinary volume, serum sodium level, cerebral hernia, frontal lobe contusion, diff use axonal injury [DAI], and skull base fracture) were probably related to AI after TBI. Three factors (urinary volume [X 4 ], serum sodium level [X 5 ], and DAI [X 8 ]) were independent variables, based on which a prediction model was developed (logit P= -3.552+2.583X 4 +2.235X 5 +2.269X 8 ). CONCLUSIONS:The incidence of AI after TBI is high. Factors such as age, GCS score, MAP, urinary volume, serum sodium level, cerebral hernia, frontal lobe contusion, DAI, and skull base fracture are probably related to AI after TBI. Urinary volume, serum sodium level, and DAI are the independent predictors of AI after TBI.

Efficacy of perampanel as an adjunctive therapy in pediatric focal epilepsy

“…Patients whose seizure frequency was reduced by ≥ 50% or who were seizure-free were defined as responders, while a 100% reduction in seizure frequency was deemed seizure-free. SPSS Statistics V21.0 (IBM Corp., Armonk, NY, USA) was employed for statistical analysis [ 12 ]. Continuous variables are represented by means ± standard deviations, while categorical variables are represented by medians and interquartile intervals.…”