脑血管狭窄患者狭窄分布及筛查模型的建立

目的 构建基于Logistic回归的脑血管狭窄筛查模型。 方法 在体检者中选取经核磁共振血管成像(MRA)检查的1 118例脑血管狭窄阴性者,在体检者中经MRA检查高度怀疑有脑血管狭窄并住院的患者中选取经数字减影脑血管造影(DSA)检查确诊的1 329例脑血管狭窄者。将1 118例脑血管狭窄阴性者作为对照组,将1 329例脑血管狭窄者作为病例组,采用病例-对照研究分析。通过描述脑血管狭窄部位的分布特点,进而使用经济廉价的常规检查指标在男女中构建脑血管狭窄Logistic筛查模型,用ROC曲线下面积(AUC)等指标进行模型评价。 结果 1 329例脑血管狭窄者中,男性有985例,女性有344例。调整年龄后的脑血管狭窄Logistic筛查模型中,男性共纳入8个变量:年龄、谷草转氨酶、白蛋白、高密度脂蛋白、中性粒细胞值、高血压、吸烟和饮酒,女性共纳入5个变量:白蛋白、高密度脂蛋白、空腹血糖、中性粒细胞值和高血压。其中高密度脂蛋白、白蛋白、高血压和中性粒细胞值为男女共同纳入的变量。AUC男女分别为0.927和0.888,经十折交叉验证后AUC男女分别为0.928和0.885。 结论 该模型具有较好的筛查能力,可用来识别脑血管狭窄高风险个体。

Distribution of cerebrovascular stenosis and construction of a screening model

Objective To establish a screening model of cerebrovascular stenosis based on Logistic regression. Methods This study selected 1,118 physical examiners without cerebrovascular stenosis by magnetic resonance angiography(MRA)as the control group, and 1,329 with cerebrovascular stenosis diagnosed by digital subtraction angiography(DSA)as the case group. Distribution of the sites of cerebrovascular stenosis was described. Routine and economic examination indicators were adopted to construct a Logistic screening model of cerebrovascular stenosis in both male and female. The model was evaluated with area under the receiver operator characteristic curve(AUC). Results Among the 1,329 cerebrovascular stenosis patients, 985 were male and 344 were female. In the Logistic screening model with age adjustment, 8 variables were screened out for male, including age, aspartate transaminase, albumin, high-density lipoprotein, neutrophil value, hypertension, smoking and drinking, while 5 variables were screened out for female, including albumin, high-density lipoprotein, fasting blood glucose and hypertension. The high-density lipoprotein, albumin, hypertension and neutrophil value were involved in both male and female. The AUC was 0.927 and 0.888 for male and female, respectively. After 10-fold cross validation, the AUC was 0.928 and 0.885, respectively. Conclusion The model has good screening ability and is useful for identifying high-risk individuals with cerebrovascular stenosis.