ABSTRACT
Objective:To investigate the association between systemic inflammation response index (SIRI) and early neurological deterioration (END) in patients with basal ganglia hemorrhage (BGH), and then set up a prediction Nomogram model for END.Methods:The retrospective cohort study was conducted. A total of 146 patients with BGH from January 2016 to December 2018 were chosen in the Affiliated Jiangyin Hospital of Southeast University Medical College. The patients were divided into the END group ( n=34) and non-END group ( n=112), according to whether END occurred or not. The normally distributed data were presented as the mean±standard deviation ( Mean± SD), and the groups were compared using the t test. The non-normally distributed data were expressed as M ( P25, P75), and this data was analysed via the Kruskal-Wallis test. Categorical variables were described as numbers of patients (%) and compared using chi-square analysis or Fisher exact test, as appropriate. Univariate analysis and multivariate logistic regression analysis were used to identify the risk factors of END occurrence, and the relationship with SIRI. Then, each factor was scored by Nomogram method to construct the prediction model. Receiver operating characteristic curve (ROC) was drawn to assess the predictive value of SIRI and Nomogram model in the occurrence of END. Results:Univariate analysis showed that the occurrence of END was associated with hematoma volume, presence of intraventricular hemorrhage, blood glucose, lymphocyte count and SIRI ( P<0.05). Multivariate logistic regression analysis showed that hematoma volume ( P<0.001), presence of intraventricular hemorrhage ( P=0.012) and SIRI ( P=0.023) are independent risk factors for END occurrence. ROC curve analysis showed that SIRI has certain predictive value for END occurrence, and the optimal cut-off value was SIRI=5.40×10 9/L. Then these risk factors were incorporated into the Nomogram. Statistically analysis showed the model had a good predictive value, and the model combining the SIRI and other prognostic factors (AUC=0.869, 95% CI: 0.804-0.935, P<0.001) showed more favorable discriminative ability than the model without the SIRI (AUC=0.811, 95% CI: 0.734-0.889, P<0.001) and the model using the SIRI only (AUC=0.716, 95% CI: 0.622-0.810, P<0.001). Conclusion:SIRI is closely correlated with the occurrence of END in patients with BGH, and the nomogram model combining the SIRI has a more accurately predictive value, which improved the early identification and screening of END, and patient outcomes.