摘要
目的探讨碱性磷酸酶(ALP)与冠心病及冠状动脉病变范围的相关性。方法纳入临床高度怀疑冠心病需完善冠状动脉造影(CAG)的294例患者。根据CAG检查结果,将患者分为非冠心病组77例和冠心病组217例,冠心病患者分为单支病变组82例、多支病变组135例。采用Logistic回归模型分析ALP与冠心病及其病变范围的关系,并采用受试者工作特征曲线分析ALP预测冠心病的效能。结果ALP水平升高、合并糖尿病、吸烟、LDL-C水平异常是临床高度怀疑冠心病患者诊断为冠心病的危险因素(均P<0.05);ALP预测冠心病的曲线下面积为0.659,特异度、灵敏度分别为0.701、0.548。ALP水平和LDL-C水平均为冠心病患者病变范围的影响因素(均P<0.05)。结论在临床高度怀疑冠心病患者中,ALP水平较高者患冠心病的可能性大,且ALP有助于评估冠心病患者的病变范围。
Objective To explore the correlation of alkaline phosphatase(ALP)with coronary heart disease(CHD)and range of coronary lesions.Methods A total of 294 patients highly suspected as CHD clinically and needed undergoing coronary angiography(CAG)were selected.On the basis of CAG result,the patients were divided into non-CHD group(n=77)and CHD group(n=217),and the CHD patients were allocated to single-lesion group(n=82)or multi-lesion group(n=135).Logistic regression model was used to analyze the relation of ALP with CHD and its lesion range,and receiver operating characteristic curve was employed to analyze the predictive efficacy of ALP for CHD.Results Among patients highly suspected as CHD clinically,elevated ALP level,complication of diabetes mellitus,smoking,and abnormal LDL-C level were the risk factors for CHD in patients highly suspected as CHD clinically(all P<0.05);the area under the curve of ALP for predicting CHD was 0.659,with a specificity of 0.701 and a sensitivity of 0.548.ALP and LDL-C levels were the factors influencing the range of coronary lesions in CHD patients(all P<0.05).Conclusion Among patients highly suspected as CHD clinically,those with higher ALP level are at higher risk of suffering from CHD,besides,ALP is conducive to evaluating the lesion range in CHD patients.
作者
吴玉芳
张春容
WU Yu-fang;ZHANG Chun-rong(The Fifth Clinical College,Chongqing Medical University,Chongqing 402160,China;Department of Emergency Medicine,Yongchuan Hospital of Chongqing Medical University,Chongqing 402160,China)
出处
《广西医学》
CAS
2020年第6期681-684,共4页
Guangxi Medical Journal
关键词
冠心病
碱性磷酸酶
病变范围
预测效能
诊断
Coronary heart disease
Alkaline phosphatase
Lesion range
Predictive efficacy
Diagnosis