Neutrophil-to-Lymphocyte Ratio Predicts in-Hospital Mortality in Intracerebral Hemorrhage.

OBJECTIVES: The neutrophil-to-lymphocyte ratio is gaining popularity as a low-cost biomarker of inflammation and outcome prediction. Intracerebral hemorrhage has high mortality and disability, which strongly influences societal development. This study aimed to research whether the neutrophil-to-lymphocyte ratio can predict hospital mortality in patients with severe intracerebral hemorrhage. MATERIALS AND METHODS: It was a retrospective analysis of prospectively collected data. The survival analysis and proportional hazards models analyzed clinical data from the Medical Information Mart for Intensive Care III database of patients with intracerebral hemorrhage. RESULTS: The records of 1,000 patients were included in our study. Two hundred forty-seven individuals died in the hospital, while 753 survived to discharge. According to data analysis, the neutrophil-to-lymphocyte ratio of the death group (11.21±7.81) significantly exceeded the survival group (7.94±6.04). The univariate Cox regression revealed that the neutrophil-to-lymphocyte ratio is a potential predictor of in-hospital mortality (HR:1.044; 95% CI:1.029-1.059; p <0.001). Furthermore, the proportional hazards model demonstrated that the risk of in-hospital death increased 2.34-fold for each increase in neutrophil-to-lymphocyte ratio when other factors were held constant. Following the ROC analysis, the Kaplan-Meier based on the proportional hazards model showed that patients with a neutrophil-to-lymphocyte ratio >7.68 on the first day of hospitalization had a higher risk of death. CONCLUSIONS: In patients with severe intracerebral hemorrhage, the neutrophil-to-lymphocyte ratio is a potential predictor of in-hospital mortality.

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

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 insufficiency (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, diffuse 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.