Ji Zeqiang, Ye Wanxing, Wen Xinyu, Zhao Xingquan, Li Na
Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Neuropsychiatr Dis Treat. 2024 Oct 3;20:1879-1887. doi: 10.2147/NDT.S475550. eCollection 2024.
We aimed to investigate whether a combination of inflammatory and radiological biomarkers can improve intracerebral hemorrhage (ICH) hematoma expansion (HE) prediction.
A retrospective analysis was conducted on patients with primary supratentorial ICH within 6 h of symptom onset between September 2021 and April 2022. Predictors were explored using univariate and logistic regression analysis. We compared the discrimination of inflammatory indice-based model 1 with models 2 and 3, which included image biomarkers, using the receiver operating characteristic curve and De Long test for area under the curve comparison.
A total of 205 eligible participants were included, 56 (27.3%) of whom experienced HE. The neutrophil-to-lymphocyte ratio (NLR), black hole sign, BAT score, and computed tomography angiography (CTA) spot sign were independently associated with HE in the logistic regression (P<0.05). The addition of non-contrast computed tomography (NCCT) signs did not provide significant discrimination improvement (AUC, Model 2 0.875 [95% CI, 0.822-0.929] versus Model 1. 0.811 [95% CI, 0.747-0.875], =0.089), whereas the added value of the CTA spot sign remained statistically significant (AUC, Model 3 0.922 [95% CI, 0.878-0.966] versus Model 2; =0.030; Model 3 versus Model 1, =0.005).
The combination of inflammatory and radiological biomarkers can predict HE with a satisfactory performance.
我们旨在研究炎症生物标志物和放射学生物标志物的组合是否能改善脑出血（ICH）血肿扩大（HE）的预测。
对2021年9月至2022年4月期间症状发作6小时内的原发性幕上ICH患者进行回顾性分析。使用单变量和逻辑回归分析探索预测因素。我们使用受试者工作特征曲线和曲线下面积比较的De Long检验，比较了基于炎症指标的模型1与包含影像生物标志物的模型2和模型3的辨别能力。
共纳入205名符合条件的参与者，其中56名（27.3%）发生了HE。在逻辑回归中，中性粒细胞与淋巴细胞比值（NLR）、黑洞征、BAT评分和计算机断层血管造影（CTA）斑点征与HE独立相关（P<0.05）。添加非增强计算机断层扫描（NCCT）征象并未显著提高辨别能力（曲线下面积，模型2为0.875[95%可信区间，0.822 - 0.929]，模型1为0.811[95%可信区间，0.747 - 0.875]，P = 0.089），而CTA斑点征的增加值仍具有统计学意义（曲线下面积，模型3为0.922[95%可信区间，0.878 - 0.966]，与模型2相比，P = 0.030；模型3与模型1相比，P = 0.005）。
炎症生物标志物和放射学生物标志物的组合能够以令人满意的性能预测HE。
