影响冠心病患者介入治疗效果和预后的血清指标分析与预测模型构建

    Analysis of serum indexes affecting effect and prognosis of interventional therapy for coronary heart disease and construction of prediction model

      摘要
    • 摘要:
      目的 分析影响冠心病（CHD）患者经皮冠状动脉介入（PCI）治疗效果和预后的患者血清指标，并构建预测模型。
      方法 本研究共纳入2020年10月～2021年5月期间新诊断的CHD并在西安交通大学第一附属医院接受PCI治疗的326例患者进行回顾性分析。计算患者的系统免疫炎症指数（SII），定义为外周血小板计数 × 中性粒细胞/淋巴细胞计数。使用酶联免疫吸附测定法（ELISA）试剂盒评估血浆中全身炎症生物标志物C-反应蛋白（CRP）、α肿瘤坏死因子（TNF-α）、白细胞介素-6（IL-6）、IL-1β。随访终点事件为PCI治疗后3年内发生主要不良心血管事件（MACE）。构建CHD患者PCI治疗后3年发生MACE的列线图预测模型。
      结果 59例（18.1%）患者在随访期间发生了MACE。与无MACE组患者相比，MACE组患者的年龄、糖尿病比例、TNF-α、IL-6、CRP、SII增加，eGFR降低（均P<0.05）。多变量Logistic回归模型显示，年龄（OR=1.070，95%CI：1.005～1.140）、糖尿病（OR=3.326，95%CI：1.033～10.706）、SII（OR=1.001，95%CI：1.000～1.002）、CRP（OR=2.583，95%CI：1.827～3.651）、TNF-α（OR=1.167，95%CI：1.096～1.243）、IL-6（OR=1.167，95%CI：1.096～1.243）和eGFR（OR=0.974，95%CI：0.950～0.999）是冠心病患者在3年随访期间发生MACE的独立影响因素。将上述独立影响因素用于生成预测MACE发生的列线图。列线图预测MACE的AUC为0.98（95%CI：0.95～1.00），准确度为0.96 （0.92～0.98），灵敏度为0.96 （0.94～0.99），特异度为0.92 （0.83～1.00）。
      结论 本研究开发了一种预测CHD患者PCI术后发生MACE的列线图模型。该模型易于操作，具有良好的区分度和校准度。

       

      Abstract:
      AIM  To analyze the serum indicators affecting the efficacy and prognosis of percutaneous coronary intervention (PCI) in patients with coronary heart disease (CHD) and construct a prediction model.
      METHODS This study retrospectively analyzed 326 patients with newly diagnosed CHD who received PCI treatment in our hospital between October 2020 and May 2021. The systemic immune inflammatory index (SII) was calculated, which was defined as peripheral platelet count × neutrophil/lymphocyte count. The biomarkers of systemic inflammation C-reactive protein (CRP), tumor necrosis factor (TNF-α), interleukin-6 (IL-6) and IL-1β in plasma were evaluated by Enzyme-linked immunosorbent assay (ELISA) kit. The endpoint event of follow-up was the occurrence of major adverse cardiovascular events (MACE) within 3 years after PCI treatment. A nomogram prediction model of MACE events in CHD patients three years after PCI was constructed.
      RESULTS 59 patients (18.1%) experienced MACE during the follow-up period. Compared with the patients without MACE, the age, diabetes ratio, TNF - α, IL-6, CRP, eGFR、and SII of patients with MACE were increased, eGFR was reduced (all P<0.05). Multivariate logistic regression model showed that age (OR=1.070, 95% CI: 1.005~1.140), diabetes (OR=3.326, 95% CI: 1.033~10.706), SII (OR=1.001, 95% CI: 1.000~1.002), CRP (OR=2.583, 95% CI: 1.827~3.651), TNF-α (OR=1.167, 95% CI: 1.096~1.243), IL-6 (OR=1.167, 95% CI: 1.096~1.243) and eGFR (OR=0.974, 95% CI: 0.950~0.999) were the major risk factors for coronary heart disease in three years. Independent influencing factors of MACE during follow-up. Use the above independent influencing factors to generate a column chart for predicting the occurrence of MACE. The AUC of MACE predicted by the column chart is 0.98 (95% CI: 0.95~1.00), with an accuracy of 0.96 (0.92~0.98), sensitivity of 0.96 (0.94~0.99), and specificity of 0.92 (0.83~1.00).
      CONCLUSION This study has developed a nomogram model for predicting MACE in CHD patients after PCI. This model is easy to operate and has good discrimination and calibration.

       

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