Yu-Xin Jiao,1 Sheng-Zhi Mu,2 Bei Kang1 

1Department of Neurology II, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, 710068, People’s Republic of China; 2Department of Burns and Plastic Surgery, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, 710068, People’s Republic of China

Correspondence: Bei Kang, Department of Neurology II, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, 710068, People’s Republic of China, Tel +86 13759953766, Email beikangkb@126.com

Objective: 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.
Methods: 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.
Results: 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 (p < 0.05). A nomogram predictive model based on these factors demonstrated satisfactory calibration and discriminatory capability.
Conclusion: 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.

Keywords: inflammation, large hemispheric infarction, nomogram, predictive model, risk factors, SIRI