冠心病患者经皮冠状动脉介入治疗术后院内心力衰竭风险预测模型的建立与验证

Risk prediction model of in-hospital heart failure in patients with coronary heart disease after percutaneous coronary intervention: development and validation

目的 分析影响冠心病患者经皮冠状动脉介入治疗(percutaneous coronary intervention, PCI)术后院内心力衰竭发生的风险因素,依此构建个性化的冠心病术后心力衰竭风险预测模型。方法 回顾性分析2017年1月至2018年12月在本科住院且接受PCI的640例冠心病患者的临床资料,依据术后院内心力衰竭发生情况将研究对象分为心力衰竭组(44例)和非心力衰竭组(596例)。采用Logistic多因素回归分析筛选心力衰竭发生的独立预测因素,应用R软件绘制心力衰竭发生风险的列线图预测模型。采用一致性指数(C-index)、ROC曲线下面积和校准曲线评价列线图模型的预测效率。结果 多因素分析显示:左心室射血分数(LVEF)、年龄、对比剂用量、合并脑梗死、NYHA心功能分级是冠心病患者PCI术后院内心力衰竭发生的独立预测因素(P<0.05);列线图预测心力衰竭发生风险的C-index为0.796(95%CI:0.723~0.869),内部验证后的ROC曲线下面积为0.797(95%CI:0.726~0.861),Hosmer-Lemeshow拟合优度检验及校准曲线提示列线图具有较好的预测一致性(χ^(2)=12.360,P=0.135)。结论 构建了预测心力衰竭发生风险的列线图模型,可协助心内科医务人员筛选出术后心力衰竭高发生风险的人群,实施针对性的干预措施。

Objective To analyze the risk factors for heart failure in hospitalized patients with coronary heart disease(CHD) after percutaneous coronary intervention(PCI) and establish a personalized risk prediction model for heart failure after PCI. Methods The clinical data of 640 patients with CHD who were hospitalized and underwent PCI in our department from January 2017 to December 2018 were collected and retrospectively analyzed. The subjects were divided into heart failure group(n=44) and non-heart failure group(n=596) according to the occurrence of postoperative heart failure. Logistic multivariate regression analysis and R software were used respectively to screen the independent predictors and plot the nomogram prediction model of heart failure risk. Then, consistency index(C-index), area under the receiver operating characteristic(ROC) curve and calibration curve were subsequently adopted to evaluate the prediction efficiency of the model. Results Multivariate analysis showed that left ventricular ejection fraction(LVEF), age, contrast agent dosage, concomitant cerebral infarction, and grade of New York Heart Association(NYHA) functional classification were independent predictors of heart failure after PCI(P<0.05). The C-index of the nomogram for predicting heart failure risk was 0.796(95%CI: 0.723~0.869), and the area under the ROC curve was 0.797 after internal validation(95%CI: 0.726~0.861). Hosmer-Lemeshow test and calibration curves indicated that the nomogram had good prediction consistency(Chi-square=12.360, P=0.135). Conclusion The nomogram model for predicting heart failure risk is successfully constructed, which can assist the medical staff to screen out the population with high risk of heart failure after PCI, and then to implement targeted intervention measures.