基于冠心病患者临床特征构建PCI术后发生再狭窄的决策树模型  

A decision tree model for predicting restenosis after PCI based on clinical characteristics in patients with coronary heart disease

作　　者：杜建青[1] 张康建[1] 郭二兵 龚玲辉 Du Jianqing;Zhang Kangjian;Guo Erbing;Gong Linghui(Department of Cardiology,People's Hospital of Pudong New District of Shanghai City,Shanghai 201299,China;不详)

机构地区：[1]上海市浦东新区人民医院心内科,上海201299

出　　处：《中国循证心血管医学杂志》2024年第12期1538-1542,共5页Chinese Journal of Evidence-Based Cardiovascular Medicine

摘　　要：目的基于冠状动脉粥样硬化性心脏病(冠心病)患者临床特征构建经皮冠状动脉介入治疗(PCI)术后发生再狭窄的决策树模型。方法选取2023年6月至2024年3月于上海市浦东新区人民医院心血管内科收治的120例行PCI的冠心病患者作为研究对象,以术后是否发生再狭窄分为再狭窄组(n=27)与非再狭窄组(n=93)。比较两组患者的基本临床资料,采用二元Logistic回归模型分析PCI术后发生再狭窄的独立危险因素,构建决策树模型,并用ROC分析模型预测价值。结果两组患者的吸烟、糖尿病、支架数、支架直径、尿酸(UA)、超敏C反应蛋白(hs-CRP)、脂蛋白a[Lp(a)]、总胆固醇、低密度脂蛋白比较有差异(P<0.05)。二元Logistic回归分析发现:合并糖尿病、UA偏高、hs-CRP>10 mg/L、Lp(a)>300 mg/L均是冠心病患者PCI术后发生再狭窄的独立危险因素(P<0.05),可分别将风险提升7.09倍、5.93倍、9.22倍及5.17倍。决策树模型显示:hs-CRP>10 mg/L的冠心病患者PCI术后发生再狭窄的比例高于hs-CRP≤10 mg/L的冠心病患者;在hs-CRP>10 mg/L的节点下,Lp(a)>300 mg/L的冠心病患者PCI术后发生再狭窄的比例高于Lp(a)≤300 mg/L的冠心病患者;在Lp(a)≤300 mg/L和hs-CRP≤10 mg/L节点下,UA偏高的冠心病患者PCI术后发生再狭窄的比例高于UA正常的冠心病患者;在hs-CRP≤10 mg/L节点下的UA偏高节点下,合并糖尿病的冠心病患者PCI术后发生再狭窄的比例高于未合并糖尿病的冠心病患者。Logistic回归模型及决策树模型的曲线下面积(AUC)分别为0.884、0.862。取cut-off值时,敏感度分别为0.926、0.852,特异度分别为0.710、0.817。Delong检验显示,两种模型的AUC比较,无明显差异(Z=0.793,P=0.428)。结论合并糖尿病、UA偏高、hs-CRP>10 mg/L、Lp(a)>300 mg/L均是冠心病患者PCI术后发生再狭窄的独立危险因素,基于上述因素构建的决策树模型可较准确预测患者PCI术后再狭窄的发生。Objective To establish a decision tree model for predicting restenosis after percutaneous coronary intervention(PCI) based on clinical characteristics in patients with coronary heart disease(CHD).Methods CHD patients undergone PCI(n=120)were chosen from Department of Cardiology in People’s Hospital of Pudong New District of Shanghai City from July 2023 to Mar.2024.The patients were divided,according to restenosis occurrence status after PCI,into restenosis group(n=27)and non-restenosis group(n=93).The basic clinical materials were compared between 2 groups.The independent risk factors for restenosis occurrence after PCI were analyzed by using binary Logistic regression model.A decision tree model was established and its predictive value to restenosis was analyzed by using ROC curve.Results There were significant differences in smoking,diabetes,stent number,stent diameter,uric acid(UA),high-sensitivity C-reactive protein(hs-CRP),lipoprotein a[Lp(a)],total cholesterol(TC)and low-density lipoprotein(LDL)between 2 groups(P<0.05).The results of binary Logistic regression analysis showed that complicated diabetes,higher UA level,hs-CRP>10 mg/L and Lp(a)>300 mg/L were independent risk factors for restenosis occurrence after PCI in CHD patients(P<0.05),which could increase the risk by 7.09 times,5.93 times,9.22 times and 5.17 times,respectively.The results of decision tree model showed that the percentage of restenosis occurrence after PCI was higher in patients with hs-CRP>10 mg/L than that in those with hs-CRP≤10 mg/L.At the node of hs-CRP>10 mg/L,the percentage of restenosis occurrence after PCI was higher in patients with Lp(a)>300 mg/L than that in those with Lp(a)≤300 mg/L,at the nodes of Lp(a)≤300 mg/L and hs-CRP≤10 mg/L,the percentage of restenosis occurrence after PCI was higher in patients with higher UA level than that in those with normal UA level,and at the nodes of hs-CRP≤10 mg/L and higher UA level,the percentage of restenosis occurrence after PCI was higher in patients with complicated diabete

关 键 词：冠心病 经皮冠状动脉介入治疗 再狭窄 决策树模型 

分 类 号：R541.4[医药卫生—心血管疾病]