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    基于非传统血脂参数联合传统血脂参数构建冠心病PCI术后支架内再狭窄的风险预测列线图模型及验证

    Construction of the column chart model for predicting the risk of in-stent restenosis after coronary heart disease PCI based on non-traditional lipid parameters and its validation

      摘要
    • 摘要:
      目的： 基于非传统血脂参数联合传统血脂参数构建冠心病经皮冠状动脉介入治疗（percutaneous coronary intervention，PCI）术后支架内再狭窄（in–stent restenosis，ISR）的风险预测列线图模型并验证。
      方法： 回顾性分析PCI术后139例病人，随访不低于12个月，根据是否存在ISR分为ISR组（n = 39）和非ISR组（n = 100）；收集病人年龄、性别、体质量指数（BMI）、收缩压、舒张压、高血压病史、糖尿病史、吸烟史、饮酒史、HDL−C、LDL−C、TC和TG水平，并计算出非传统脂质参数（non−HDL−C、AC、CRI−Ⅰ及CRI−Ⅱ）。采用logistic回归分析并绘制列线图模型、ROC曲线、校正曲线及DCA曲线。
      结果： ISR组与非ISR组在性别、年龄、BMI、收缩压、舒张压、高血压病史、糖尿病史、吸烟史、饮酒史、HDL−C及TG方面相比较差异均无统计学意义（P ＞ 0.05）；ISR组病人的LDL−C、TC、non−HDL−C、AC、CRI−Ⅰ及CRI−Ⅱ高于非ISR组，差异均有统计学意义（P ＜ 0.01）；二元logistic回归分析表明，LDL−C、TC、non−HDL−C、AC、CRI−Ⅰ及CRI−Ⅱ是ISR发生的危险因素（P ＜ 0.05）；依据logistic回归分析构建ISR发生的风险预测列线图模型，根据模型预测概率绘制ROC曲线，其AUC为0.922，模型准确度为0.889，灵敏度为78.6%，特异度为93.0%，Hosmer–Lemeshow检验结果χ2 = 6.93，P = 0.55，C–index值为0.918，预测校正曲线趋近于理论曲线，DCA曲线显示，列线图预测模型可以使行PCI的冠心病病人获益。
      结论： LDL−C、TC、non−HDL−C、AC、CRI−Ⅰ及CRI−Ⅱ是PCI术后ISR发生的主要危险因素。通过以上危险因素构建的列线图模型具有较好的预测效能，有利于识别ISR发生的高危人群。

       

      Abstract:
      Objective To construct a risk prediction nomogram model for in-stent restenosis (ISR) after percutaneous coronary intervention (PCI) for coronary heart disease based on the combination of non-traditional lipid parameters and traditional lipid parameters and verified.
      Methods A retrospective analysis was conducted on 139 patients after PCI. The patients were followed up for no less than 12 months, and divided into the ISR group (n = 39) and non-ISR group (n = 100) according to the presence or absence of ISR. The age, gender, body mass index (BMI), systolic blood pressure, diastolic blood pressure, history of hypertension, history of diabetes, smoking history, drinking history, HDL−C, LDL−C, TC and TG levels of patients were collected, and the non-traditional lipid parameters (non−HDL−C, AC, CRI−Ⅰ and CRI−Ⅱ) were calculated. The logistic regression analysis was used, and the nomogram model, ROC curve, correction curve and DCA curve were plotted.
      Results There was no statistical significance in the of gender, age, BMI, systolic blood pressure, diastolic blood pressure, history of hypertension, history of diabetes, smoking history, drinking history, HDL−C and TG between the ISR group and non-ISR group (P ＞ 0.05). The levels of LDL−C, TC, non-HDL−C, AC, CRI−Ⅰ and CRI−Ⅱ in the ISR group were higher than those in non-ISR group, and the differences were statistically significant (P ＜ 0.01). The results of binary logistic regression analysis indicated that the LDL−C, TC, non-HDL−C, AC, CRI−Ⅰ and CRI−Ⅱ were the risk factors of ISR (P ＜ 0.05). Based on logistic regression analysis, a nomogram model for risk prediction of ISR occurrence was constructed. The ROC curve was plotted according to the predicted probability of model. Its AUC was 0.922, the model accuracy was 0.889, the sensitivity was 78.6%, the specificity was 93.0%, and the Hosmer-Lemeshow test result was χ2 = 6.93. P = 0.55, the C-index value was 0.918, the predictive correction curve approached the theoretical curve, and the DCA curve showed that the nomogram prediction model could benefit coronary heart disease patients treated with PCI.
      Conclusions The LDL−C, TC, non HDL C, AC, CRI−Ⅰ, and CRI−Ⅱ are the main risk factors of ISR after PCI. The column chart model constructed based on the above risk factors has good predictive performance, and is conducive to identifying high-risk populations for ISR occurrence.

       

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