Systemic Immune-Inflammation Index Predicts the Prognosis of Traumatic Brain Injury

医学 接收机工作特性 置信区间 逻辑回归 内科学 创伤性脑损伤 中性粒细胞与淋巴细胞比率 曲线下面积 全身炎症 生物标志物 单变量分析 多元分析 炎症 淋巴细胞 精神科 生物化学 化学
作者
Huajie Xu,Wei Wu,Qi Zhu,Jie Wang,Peng-Fei Ding,Zong Zhuang,Wei Li,Yongyue Gao,Chun‐Hua Hang
出处
期刊:World Neurosurgery [Elsevier BV]
卷期号:183: e22-e27 被引量:9
标识
DOI:10.1016/j.wneu.2023.10.081
摘要

Systemic inflammation following traumatic brain injury (TBI) has been extensively studied over the past decades, as it contributes significantly to the pathophysiological injury mechanisms and subsequent poor outcomes. Systemic immune-inflammation (SII) index is a novel biomarker of systemic inflammatory response. However, its predictive value regarding TBI prognosis in clinical practice remains insufficiently investigated. A total of 102 TBI patients admitted to Nanjing Drum Tower Hospital from July 2019 to February 2022 were enrolled. We employed various statistical analyses to evaluate the correlation between inflammatory indicators upon admission and patient prognosis, compared the predictive accuracy of these indicators, and generated receiver operating curve analysis to test their prognostic performance. The SII index, platelet count, absolute lymphocyte count, and neutrophil/lymphocyte ratio (NLR) were capable of distinguishing TBI prognosis according to univariate logistic regression models (P < 0.05). Multivariate logistic regression models revealed that increased SII index, platelet count, and NLR upon admission were independent predictors of poor TBI prognosis (P < 0.05). Receiver operating curve analysis further demonstrated that the SII index (area under the curve = 0.845, 95% confidence interval 0.769–0.921, P = 0.000) exhibited higher predictive ability than the NLR (area under the curve = 0.694, 95% confidence interval 0.591–0.796, P = 0.001). Our findings suggested that increased SII index during the early stages of TBI was an independent risk factor for poor prognosis with satisfactory predictive value. The SII index provides a reliable, convenient, and cost-effective prognostic model to evaluate systemic inflammation after TBI and identify patients at risk of poor outcomes, thereby offering valuable guidance for clinical practice.
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