Jiao Yu-Xin, Mu Sheng-Zhi, Kang Bei
Department of Neurology II, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, 710068, People's Republic of China.
Department of Burns and Plastic Surgery, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, 710068, People's Republic of China.
J Inflamm Res. 2025 Aug 30;18:11951-11962. doi: 10.2147/JIR.S537552. eCollection 2025.
Large hemispheric infarction (LHI) represents one of the most severe subtypes of ischemic stroke, associated with high rates of disability and mortality. This study aimed to examine the association between the systemic inflammation response index (SIRI) and LHI, identify independent risk factors, and develop a predictive model for clinical application.
A total of 152 patients diagnosed with LHI and admitted to Shaanxi Provincial People's Hospital between June 2020 and June 2023 were retrospectively selected based on defined inclusion and exclusion criteria. A control group comprising 153 healthy individuals from the same period was included for comparison. Clinical and laboratory data were collected, and statistical analyses were performed using SPSS version 26.0. Univariate and multivariate logistic regression analyses were conducted to determine independent risk factors. The predictive performance of these factors was evaluated using receiver operating characteristic curve analysis, and a nomogram-based predictive model was constructed.
Multivariate logistic regression analysis identified a history of atrial fibrillation, coronary heart disease, prior stroke, elevated systolic blood pressure, increased fasting blood glucose (FBG), elevated homocysteine, and higher SIRI values as independent risk factors for LHI ( < 0.05). A nomogram predictive model based on these factors demonstrated satisfactory calibration and discriminatory capability.
SIRI has certain clinical value in predicting LHI. The developed nomogram-based predictive model incorporating SIRI exhibited robust predictive performance and may assist in guiding clinical decision-making.
大脑半球大面积梗死（LHI）是缺血性卒中最严重的亚型之一，与高致残率和死亡率相关。本研究旨在探讨全身炎症反应指数（SIRI）与LHI之间的关联，确定独立危险因素，并建立一个用于临床应用的预测模型。
根据明确的纳入和排除标准，回顾性选取2020年6月至2023年6月期间在陕西省人民医院确诊为LHI并入院的152例患者。纳入同期153名健康个体作为对照组进行比较。收集临床和实验室数据，并使用SPSS 26.0版进行统计分析。进行单因素和多因素逻辑回归分析以确定独立危险因素。使用受试者工作特征曲线分析评估这些因素的预测性能，并构建基于列线图的预测模型。
多因素逻辑回归分析确定房颤病史、冠心病、既往卒中、收缩压升高、空腹血糖（FBG）升高、同型半胱氨酸升高和较高的SIRI值是LHI的独立危险因素（<0.05）。基于这些因素的列线图预测模型显示出令人满意的校准和鉴别能力。
SIRI在预测LHI方面具有一定的临床价值。所建立的包含SIRI的基于列线图的预测模型具有强大的预测性能，可能有助于指导临床决策。
