Systemic inflammatory response index as a predictor of stroke-associated pneumonia in patients with acute ischemic stroke treated by thrombectomy: a retrospective study

Author:

Zheng Feng,Gao Wen,Xiao Yinfeng,Guo Xiumei,Xiong Yu,Chen Chunhui,Zheng Hanlin,Pan Zhigang,Wang Lingxing,Zheng Shuni,Ke Chuhan,Liu Qiaoling,Liu Aihua,Huang Xinyue,Hu Weipeng

Abstract

Abstract Background The predictive value of systemic inflammatory response index (SIRI) for stroke-associated pneumonia (SAP) risk in patients with acute ischemic stroke (AIS) treated by thrombectomy remains unclear. This study aimed to investigate the predictive value of SIRI for SAP in patients with AIS treated by thrombectomy. Methods We included AIS patients treated by thrombectomy between August 2018 and August 2022 at our institute. We used multivariate logistic regression to construct the prediction model and performed a receiver operating characteristic curve analysis to evaluate the ability of SIRI to predict SAP and constructed a calibration curve to evaluate the prediction accuracy of the model. We evaluated the clinical application value of the nomogram using decision curve analysis. Results We included 84 eligible patients with AIS in the analysis, among which 56 (66.7%) had SAP. In the univariate analysis, there were significant differences in sex (p = 0.035), National Institute of Health Stroke Scale score at admission ≥ 20 (p = 0.019) and SIRI (p < 0.001). The results of multivariable logistic analysis showed that the risk of SAP increased with the SIRI value (OR = 1.169, 95% CI = 1.049–1.344, p = 0.014). Age ≥ 60 (OR = 4.076, 95% CI = 1.251–14.841, p = 0.024) was also statistically significant. A nomogram with SIRI showed good prediction accuracy for SAP in AIS patients treated by thrombectomy (C-index value = 0.774). Conclusions SIRI is an independent predictor for SAP in patients with AIS treated by thrombectomy. A high SIRI value may allow for the early identification of patients with AIS treated by thrombectomy at high risk for SAP.

Funder

Quanzhou City Science and Technology Program

Doctoral Startup Fund of the Second Affliated Hospital of Fujian Medical University

Publisher

Springer Science and Business Media LLC

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