Relationship between systemic inflammation response index and symptomatic cerebral vasospasm after aneurismal subarachnoid hemorrhage as well as construction of a Nomogram predictive model

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 AUC_{combined with or without SIRI model} and AUC_{only 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.