Li Lihui, Sun Guangfeng, Yu Jiangbo, Shan Gaojun, Su Lide, Dong Guo
Department of Cardiovascular, First Affiliated Hospital of Harbin Medical University, Harbin, China.
Department of Emergency, Xiamen Cardiovascular Hospital, Xiamen University, Xiamen, China.
Front Cardiovasc Med. 2023 Apr 6;10:1046895. doi: 10.3389/fcvm.2023.1046895. eCollection 2023.
Acute coronary syndrome (ACS) is the most common cause of death in patients with coronary artery disease. The aim of the study was to identify the predictors of both comprehensive clinical risk and severity of coronary lesions by comprehensive use of GRACE and SYNTAX scores in patients with ACS.
Clinical data of 225 ACS patients who underwent coronary angiography between 2015 and 2016 were collected. Multiple logistic regression analysis (stepwise) was used to identify the predictors. The predictive ability of predictors and the model were determined using receiver operating characteristics analyses.
Multivariable logistic regression analyses showed that high aspartate aminotransferase (AST) predicted the comprehensive clinical risk with odds ratios (ORs) and 95% confidence intervals (CIs) of 1.011 (1.002-1.021). High total cholesterol (TC) and red blood cell distribution width (RDW) predicted the severity of coronary lesions with ORs and 95% CIs of 1.517 (1.148-2.004) and 1.556 (1.195-2.028), respectively. Low prealbumin predicted both severity of coronary lesions and comprehensive clinical risk of ACS patients with ORs and 95% CIs of 0.743 (0.672-0.821) and 0.836 (0.769-0.909), respectively. The model with a combination of prealbumin and AST had the highest predictive efficacy for comprehensive clinical risk, and the combination of prealbumin, TC, and RDW had the highest predictive efficacy for the severity of coronary lesions. The sensitivity and specificity, and the optimal cut-off values of these four indexes were determined.
Four predictors for the comprehensive clinical risk and severity of coronary lesions of ACS were identified, which provided important information for the early diagnosis and appropriate treatment of ACS.
急性冠状动脉综合征(ACS)是冠心病患者最常见的死亡原因。本研究的目的是通过综合应用GRACE和SYNTAX评分来确定ACS患者综合临床风险和冠状动脉病变严重程度的预测因素。
收集了2015年至2016年间接受冠状动脉造影的225例ACS患者的临床资料。采用多因素逻辑回归分析(逐步法)来确定预测因素。使用受试者工作特征分析来确定预测因素和模型的预测能力。
多变量逻辑回归分析显示,高天冬氨酸转氨酶(AST)预测综合临床风险,比值比(OR)和95%置信区间(CI)为1.011(1.002 - 1.021)。高总胆固醇(TC)和红细胞分布宽度(RDW)预测冠状动脉病变严重程度,OR和95%CI分别为1.517(1.148 - 2.004)和1.556(1.195 - 2.028)。低前白蛋白预测ACS患者冠状动脉病变严重程度和综合临床风险,OR和95%CI分别为0.743(0.672 - 0.821)和0.836(0.769 - 0.909)。前白蛋白和AST联合的模型对综合临床风险具有最高的预测效能,前白蛋白、TC和RDW联合对冠状动脉病变严重程度具有最高的预测效能。确定了这四个指标的敏感性、特异性及最佳截断值。
确定了ACS患者综合临床风险和冠状动脉病变严重程度的四个预测因素,为ACS的早期诊断和恰当治疗提供了重要信息。