Dong Zhiwu, Guo Qiang, Sun Li, Li Feifei, Zhao Aihong, Liu Jingfan, Qu Peipei, Zhu Qinghua, Xiao Chunhai, Niu Fusheng, Liang Shuang
Department of Laboratory Medicine, Jinshan Branch of Shanghai 6th People's Hospital Affiliated to Shanghai Jiaotong University, Shanghai, China.
Department of Ultrasound Medicine, Jinshan Branch of Shanghai 6th People's Hospital Affiliated to Shanghai Jiaotong University, Shanghai, China.
J Clin Lab Anal. 2018 May;32(4):e22356. doi: 10.1002/jcla.22356. Epub 2017 Nov 11.
This study aims to determine the risk factors and to predict the occurrence of cerebral infarction in patients with carotid artery stenosis.
Two hundred and one subjects with carotid artery stenosis were retrospectively selected from Jinshan Branch of Shanghai Sixth People's Hospital, 115 cases of which with cerebral infarction and 86 without it. Clinical tests were performed including coagulation indices, fasting glucose, serum lipid, and blood rheology. Logistic regression analyses were used to identify the risk factors. Regression model was established, and receiver operating characteristic (ROC) curve was applied to analyze its diagnostic value.
Our data indicated that apolipoprotein AI (OR = 0.051, 95% CI: 0.009-0.295), lipoprotein (a) (OR = 1.003, 95% CI: 1.001-1.005), and RBC rigidity index (OR = 0.383, 95% CI: 0.209-0.702) were independent risk factors. Area under the curve (AUC) of the regression model = 0.78, with the sensitivity of 73.9% (95% CI: 64.9%-81.7%) and specificity of 69.2% (95% CI: 52.4%-83.0%). Prediction probability was determined while logistic regression score >0.748 defaulted as high-risk status. High-risk ratios were 80% in progressive cerebral infarction and 72% in nonprogressive cerebral infarction (P > .05), respectively, while significant differences were found when both compared with controls (P < .001).
We show herein that the regression model based on apolipoprotein AI, lipoprotein (a), and RBC IR is a promising tool to predict the occurrence of cerebral infarction in patients with carotid artery stenosis. However, identification of novel diagnostic markers for progressive cerebral infarction is still necessary.
本研究旨在确定颈动脉狭窄患者发生脑梗死的危险因素并预测其发生情况。
回顾性选取上海第六人民医院金山分院201例颈动脉狭窄患者，其中115例发生脑梗死，86例未发生脑梗死。进行了包括凝血指标、空腹血糖、血脂和血液流变学在内的临床检测。采用逻辑回归分析确定危险因素。建立回归模型，并应用受试者工作特征（ROC）曲线分析其诊断价值。
我们的数据表明，载脂蛋白AI（OR = 0.051，95%CI：0.009 - 0.295）、脂蛋白（a）（OR = 1.003，95%CI：1.001 - 1.005）和红细胞刚性指数（OR = 0.383，95%CI：0.209 - 0.702）是独立危险因素。回归模型的曲线下面积（AUC）= 0.78，敏感性为73.9%（95%CI：64.9% - 81.7%），特异性为69.2%（95%CI：52.4% - 83.0%）。当逻辑回归得分>0.748时，预测概率被确定为高危状态。进展性脑梗死的高危比例为80%，非进展性脑梗死的高危比例为72%（P > 0.05），而与对照组相比均有显著差异（P < 0.001）。
我们在此表明，基于载脂蛋白AI、脂蛋白（a）和红细胞刚性指数的回归模型是预测颈动脉狭窄患者脑梗死发生的有前景的工具。然而，仍有必要识别进展性脑梗死的新型诊断标志物。
