Relationship between systemic inflammation response index and symptomatic cerebral vasospasm after aneurismal subarachnoid hemorrhage as well as construction of a Nomogram predictive model
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摘要: 目的 探讨动脉瘤性蛛网膜下腔出血(aSAH)术后症状性脑血管痉挛(SCVS)的危险因素,并建立SCVS发生的Nomogram预测模型。 方法 将手术治疗的125例aSAH患者依据是否发生SCVS分为SCVS组与非SCVS组。采用Logistic回归分析确定SCVS发生与全身炎症反应指数(SIRI)的关系,以及其他相关危险因素。应用Nomogram法对各个因素进行评分,构建预测模型。采用受试者工作特征曲线(ROC)评价SIRI及Nomogram模型对SCVS发生的预测价值。 结果 19例aSAH患者术后并发SCVS, 发生率为15.20%(19/125)。SCVS组与非SCVS组吸烟、高血压、入院时Hunt-Hess分级、动脉瘤数目、合并脑室积血(IVH)、改良Fisher分级、甘油三酯(TG)、单核细胞计数及SIRI水平有显著差异(P<0.01)。多因素Logistic回归分析显示,合并高血压、入院时Hunt-Hess分级(Ⅳ~Ⅴ级)、合并IVH、改良Fisher分级(Ⅳ~Ⅴ级)、高TG水平和SIRI水平是aSAH患者发生SCVS的独立危险因素(P<0.05)。当TG=2.24 mmol/L、SIRI=3.63×109/L时,其约登指数最大(0.312、0.296), 是预测SCVS发生的最佳截断值,同时其预测准确度［ROC曲线下面积(AUC)］、敏感性、特异性、阳性预测值及阴性预测值分别为0.743、72.70%、80.10%、77.53%、94.24%和0.725、70.60%、76.90%、73.49%、93.59%。ROC分析结果显示,结合SIRI和其他标准变量的模型(AUC=0.896, 95%CI为0.803~0.929, P<0.001)比未结合SIRI的模型(AUC=0.859, 95%CI为0.759~0.912, P<0.001)和仅基于SIRI的模型(AUC=0.725,95%CI为0.586~0.793, P=0.001)对SCVS具有更佳的预测价值。进一步行AUC假设检验,发现AUC结合/不结合SIRI模型与AUC仅基于SIRI的模型的差异均有统计学意义(Z=4.029, P<0.001; Z=3.734, P=0.003)。 结论 SIRI与aSAH术后SCVS密切相关,且结合SIRI共建Nomogram模型将优化预测效能,提高对SCVS发生的早期识别和筛选能力。
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关键词:
- 全身炎症反应指数 /
- 动脉瘤性蛛网膜下腔出血 /
- 症状性脑血管痉挛 /
- Nomogram模型
Abstract: Objective To investigate the risks factors of postoperative symptomatic cerebral vasospasm(SCVS)after aneurysmal subarachnoid hemorrhage(aSAH)and construct a Nomogram model for prediction of SCVS incidence. Methods Totally 125 aSAH patients with surgical treatment were divided into SCVS group and non-SCVS group according to occurrence of SCVS. Logistic regression analysis was used to determine the relationship between the occurrence of SCVS and systemic inflammatory response index(SIRI), and other related risk factors. The Nomogram method was used to evaluate each factor and construct a prediction model. Receiver operating characteristic(ROC)curve- was drawn to assess the values of SIRI and Nomogram model in predicting the occurrence of SCVS. Results The incidence of SCVS was 15.20%(19/125)in 19 aSAH patients. There were significant differences in smoking, hypertension, Hunt-Hess grade at hospital admission, number of aneurysms, intraventricular hematocele(IVH), modified Fisher grade, triglyceride(TG), monocyte count and SIRI between SCVS group and non-SCVS group(P<0.01). Multivariate Logistic regression analysis showed that hypertension, Hunt-Hess grade(IV or V grade), IVH, modified Fisher grade(IV to V grade), high TG level and SIRI level were independent risk factors of SCVS in aSAH patients(P<0.05). When TG level was 2.24 mmol/L and SIRI level was 3.63×109/L, their Youden indexes were the largest(0.312, 0.296), which were the best cut-off values for predicting the occurrence of SCVS. At the same time, their predictive accuracy ［area under ROC curve(AUC)］, sensitivity, specificity, positive predictive value and negative predictive value were 0.743, 72.70%, 80.10%, 77.53%, 94.24% and 0.725, 70.60%, 76.90%, 73.49%, 93.59% respectively. ROC analysis showed that the model combined with SIRI and other standard variables(AUC=0.896, 95%CI=0.803~0.929, P<0.001)had better predictive value for SCVS than the model without SIRI(AUC=0.859, 95%CI=0.759~0.912, P<0.001)and the model only based on SIRI(AUC=0.725, 95%CI=0.586~0.793, P=0.001). The further AUC hypothesis test showed that there were significant differences between the AUCcombined with or without SIRI model and AUConly based on SIRI model(Z=4.029, P<0.001; Z=3.734, P=0.003). Conclusion SIRI is closely correlated with the occurrence of postoperative SCVS in patients with aSAH, and the construction of Nomogram model with combination of SIRI is helpful for optimizing forecast performance and enhancing the early identification and screening abilities for incidence of SCVS. -
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