Construction of a risk model for hemorrhagic transformation in elderly patients with acute cerebral infarction complicated by cerebral small vessel disease based on thromboelastography and coagulation function
10.3760/cma.j.cn431274-20240930-01503
- VernacularTitle:基于血栓弹力图、凝血功能构建伴脑小血管病的老年急性脑梗死患者出血性转化的风险模型
- Author:
Xiaokang FANG
1
;
Keru ZHANG
;
Xiaofeng SUN
;
Yinke FENG
Author Information
1. 西安交通大学第一附属医院神经内科，西安 710089
- Publication Type:Journal Article
- Keywords:
Cerebral small vessel disease;
Acute cerebral infarction;
Hemorrhagic transformation;
Thromboelastography;
Blood coagulation function;
Model of risk
- From:
Journal of Chinese Physician
2025;27(11):1643-1647
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a risk prediction model for hemorrhagic transformation (HT) in elderly patients with acute cerebral infarction (ACI) complicated by cerebral small vessel disease (CSVD) based on thromboelastography (TEG) and coagulation function indicators.Methods:Clinical data of 120 elderly ACI patients with CSVD admitted to the First Affiliated Hospital of Xi′an Jiaotong University from June 2021 to June 2024 were retrospectively analyzed. Patients were divided into HT group (42 cases) and non-HT group (78 cases) according to the occurrence of HT within 7 days of admission. TEG parameters, coagulation function indicators, and general data were compared between the two groups. Multivariate logistic regression analysis was used to screen the influencing factors of HT, and a nomogram model was constructed accordingly. The receiver operating characteristic (ROC) curve was used to evaluate its predictive efficacy.Results:Compared with the non-HT group, the HT group had significantly longer coagulation reaction time (R), higher 30-minute clot lysis rate (LY30), longer activated partial thromboplastin time (APTT), older age, higher prevalence of diabetes mellitus, higher National Institutes of Health Stroke Scale (NIHSS) score, and larger infarct size, while the fibrinogen (FIB) level was lower (all P<0.05). Multivariate stepwise logistic regression showed that R ( OR=3.295, 95% CI: 1.226-8.848), LY30 ( OR=6.118, 95% CI: 3.111-12.030), FIB ( OR=0.213, 95% CI: 0.085-0.527), NIHSS score ( OR=4.061, 95% CI: 1.431-11.520), and infarct size ( OR=5.314, 95% CI: 2.588-10.909) were independent influencing factors for HT in elderly ACI patients with CSVD (all P<0.05). The C-index of the nomogram model constructed based on the above factors was 0.836, and the calibration curve for predicting HT was close to the ideal curve ( P>0.05). ROC curve analysis showed that the model had a sensitivity of 85.70%, specificity of 83.30%, and area under the curve (AUC) of 0.879 (95% CI: 0.810-0.949, P<0.05) for predicting HT. Conclusions:R, LY30, and FIB levels are influencing factors for HT in elderly ACI patients with CSVD. The nomogram model constructed based on these factors can effectively predict the risk of HT.
