Serum lipoprotein and RBC rigidity index to predict cerebral infarction in patients with carotid artery stenosis. Issue 4 (11th November 2017) - Dong, Zhiwu; Guo, Qiang; Sun, Li; Li, Feifei; Zhao, Aihong; Liu, Jingfan; Qu, Peipei; Zhu, Qinghua; Xiao, Chunhai; Niu, Fusheng; Liang, Shuang
Abstract : Objective: This study aims to determine the risk factors and to predict the occurrence of cerebral infarction in patients with carotid artery stenosis. Methods: 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. Results: 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). Conclusions: 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.