入院时的全身免疫炎症指数和外周血二氧化碳浓度可预测严重创伤性脑损伤患者的预后不良。
Systemic immune inflammation index and peripheral blood carbon dioxide concentration at admission predict poor prognosis in patients with severe traumatic brain injury.
机构信息
Department of Neurosurgery, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China.
Department of Neurosurgery, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China.
出版信息
Front Immunol. 2023 Jan 9;13:1034916. doi: 10.3389/fimmu.2022.1034916. eCollection 2022.
BACKGROUND
Recent studies have shown that systemic inflammation responses and hyperventilation are associated with poor outcomes in patients with severe traumatic brain injury (TBI). The aim of this retrospective study was to investigate the relationships between the systemic immune inflammation index (SII = platelet × neutrophil/lymphocyte) and peripheral blood CO concentration at admission with the Glasgow Outcome Score (GOS) at 6 months after discharge in patients with severe TBI.
METHODS
We retrospectively analyzed the clinical data for 1266 patients with severe TBI at three large medical centers from January 2016 to December 2021, and recorded the GOS 6 months after discharge. The receiver operating characteristic (ROC) curve was used to determine the best cutoff values for SII, CO, neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and lymphocyte to monocyte ratio (LMR), and chi-square tests were used to evaluate the relationships among SII, CO and the basic clinical characteristics of patients with TBI. Multivariate logistic regression analysis was used to determine the independent prognostic factors for GOS in patients with severe TBI. Finally, ROC curve, nomogram, calibration curve and decision curve analyses were used to evaluate the value of SII and coSII-CO2 in predicting the prognosis of patients with severe TBI. And we used the multifactor regression analysis method to build the CRASH model and the IMPACT model. The CRASH model included age, GCS score (GCS, Glasgow Coma Scale) and Pupillary reflex to light: one, both, none. The IMPACT model includes age, motor score and Pupillary reflex to light: one, both, none.
RESULTS
The ROC curves indicated that the best cutoff values of SII, CO, PLR, NLR and LMR were 2651.43×10, 22.15mmol/L, 190.98×10, 9.66×10 and 1.5×10, respectively. The GOS at 6 months after discharge of patients with high SII and low CO were significantly poorer than those with low SII and high CO. Multivariate logistic regression analysis revealed that age, systolic blood pressure (SBP), pupil size, subarachnoid hemorrhage (SAH), SII, PLR, serum potassium concentration [K], serum calcium concentration [Ca], international normalized ratio (INR), C-reactive protein (CRP) and co-systemic immune inflammation index combined with carbon dioxide (coSII-CO) (P < 0.001) were independent prognostic factors for GOS in patients with severe TBI. In the training group, the C-index was 0.837 with SII and 0.860 with coSII-CO. In the external validation group, the C-index was 0.907 with SII and 0.916 with coSII-CO. Decision curve analysis confirmed a superior net clinical benefit with coSII-CO rather than SII in most cases. Furthermore, the calibration curve for the probability of GOS 6 months after discharge showed better agreement with the observed results when based on the coSII-CO rather than the SII nomogram. According to machine learning, coSII-CO ranked first in importance and was followed by pupil size, then SII.
CONCLUSIONS
SII and CO have better predictive performance than NLR, PLR and LMR. SII and CO can be used as new, accurate and objective clinical predictors, and coSII-CO, based on combining SII with CO, can be used to improve the accuracy of GOS prediction in patients with TBI 6 months after discharge.
背景
最近的研究表明,全身炎症反应和过度通气与严重创伤性脑损伤(TBI)患者的不良预后有关。本回顾性研究的目的是探讨入院时的全身免疫炎症指数(SII=血小板×中性粒细胞/淋巴细胞)和外周血 CO 浓度与出院后 6 个月格拉斯哥预后评分(GOS)之间的关系在严重 TBI 患者中。
方法
我们回顾性分析了 2016 年 1 月至 2021 年 12 月 3 家大型医疗中心的 1266 例严重 TBI 患者的临床数据,并记录了出院后 6 个月的 GOS。使用接收者操作特征(ROC)曲线确定 SII、CO、中性粒细胞与淋巴细胞比值(NLR)、血小板与淋巴细胞比值(PLR)和淋巴细胞与单核细胞比值(LMR)的最佳截断值,并使用卡方检验评估 SII 和 CO 与 TBI 患者基本临床特征之间的关系。使用多变量逻辑回归分析确定严重 TBI 患者 GOS 的独立预后因素。最后,使用 ROC 曲线、列线图、校准曲线和决策曲线分析评估 SII 和 coSII-CO2 预测严重 TBI 患者预后的价值。并采用多因素回归分析方法建立 CRASH 模型和 IMPACT 模型。CRASH 模型包括年龄、GCS 评分(GCS,格拉斯哥昏迷量表)和瞳孔对光反射:一个、两个、无。IMPACT 模型包括年龄、运动评分和瞳孔对光反射:一个、两个、无。
结果
ROC 曲线表明,SII、CO、PLR、NLR 和 LMR 的最佳截断值分别为 2651.43×10、22.15mmol/L、190.98×10、9.66×10 和 1.5×10。高 SII 和低 CO 的患者出院后 6 个月的 GOS 明显差于低 SII 和高 CO 的患者。多变量逻辑回归分析显示,年龄、收缩压(SBP)、瞳孔大小、蛛网膜下腔出血(SAH)、SII、PLR、血清钾浓度[K]、血清钙浓度[Ca]、国际标准化比值(INR)、C 反应蛋白(CRP)和联合全身免疫炎症指数与二氧化碳(coSII-CO)(P<0.001)是严重 TBI 患者 GOS 的独立预后因素。在训练组中,SII 的 C 指数为 0.837,coSII-CO 的 C 指数为 0.860。在外部验证组中,SII 的 C 指数为 0.907,coSII-CO 的 C 指数为 0.916。决策曲线分析证实,在大多数情况下,coSII-CO 比 SII 具有更好的净临床获益。此外,基于 coSII-CO 的 GOS 6 个月后出院概率校准曲线与观察结果的一致性更好,而不是基于 SII 的列线图。根据机器学习,coSII-CO 在重要性方面排名第一,其次是瞳孔大小,然后是 SII。
结论
SII 和 CO 比 NLR、PLR 和 LMR 具有更好的预测性能。SII 和 CO 可作为新的、准确和客观的临床预测指标,基于 SII 与 CO 的结合的 coSII-CO 可提高 TBI 患者出院后 6 个月 GOS 预测的准确性。