基于检验指标构建急性心肌梗死患者预后不良的列线图预测模型

Nomogram prediction model construction based on test indexes for poor prognosis in patients with acute myocardial infarction

目的基于检验指标分析急性心肌梗死(AMI)患者预后不良的相关危险因素,并构建列线图预测模型。方法回顾性分析2020年9月至2022年8月于北京大学第三医院秦皇岛医院接受经皮冠状动脉介入术(PCI)治疗的300例急性AMI患者临床资料,根据术后随访1年是否发生不良心血管事件分为观察组(发生,61例)和对照组(未发生,239例)。对2组的临床资料进行统计分析;采用多因素Logistic回归分析AMI患者预后不良的独立危险因素;采用R软件构建预测AMI预后的列线图模型;采用校正曲线对预测模型进行内部验证;采用决策曲线评估预测模型的临床净收益。结果观察组冠状动脉病变程度评分(Gensini评分),胱抑素C(CysC)、脑钠肽(BNP)、高敏C反应蛋白(hs-CRP)、高迁移率族蛋白1(HMGB1)水平,以及白细胞计数>10.0×10^(9)/L的患者比例均高于对照组,差异均有统计学意义(P<0.05)。白细胞计数、CysC、BNP、hs-CRP和HMGB1水平升高是AMI患者预后不良的独立危险因素(P<0.05)。基于此构建的列线图预测模型C指数为0.791(95%CI:0.486~0.984),校准曲线趋近于理想曲线。决策曲线显示,当风险阈值>0.17时,此预测模型在预测AMI患者预后不良风险方面可以提供额外的临床净收益。结论该研究构建了AMI患者预后不良危险因素的列线图预测模型,有助于医护人员认识AMI预后不良的相关因素,尽早制订个性化对策以改善预后。

Objective To analyze the risk factors related to poor prognosis in the patients with acute myocardial infarction(AMI)based on the test indexes,and to construct a nomogram predictive model.Methods The clinical data of 300 patients with AMI receiving percutaneous coronary intervention(PCI)in Qinhuangdao Hospital of Peking University Third Hospital from September 2020 to August 2022 were retrospectively analyzed.The patients were divided into the observation group(occurrence,61 cases)and control group(non-occurrence,239 cases)according to whether the adverse cardiovascular events occurred or not during 1-year follow-up.The clinical data of the two groups were statistically analyzed.The independent risk factors of poor prognosis in the patients with AMI were analyzed by the multivariate Logistic regression.The R software was used to construct a nomogram model for predicting the prognosis of AMI.The correction curve was adopted to conduct the internal verification for the predictive model.The decision curve was used to evaluate the clinical net benefit of the predictive model.Results The Gensini score,cystatin C(CysC)level,brain natriuretic peptide(BNP)level,high sensitive C-reactive protein(hs-CRP)level,high mobility group protein 1(HMGB1)level,proportion of the patients with white blood cell count>10.0×10^(9)/L in observation group were higher than those in the control group,and the differences were statistically significant(P<0.05).The increases of white blood cell count,CysC,BNP,hs-CRP and HMGB1 levels were the independent risk factors for poor prognosis in the patients with AMI.The C-index of the nomogram predictive model based on this was 0.791(95%CI:0.486-0.984),and the calibration curve was close to the ideal curve.The decision curve showed that this predictive model could provide the additional clinical net benefit in the aspect of predicting the risk of poor prognosis in the patients with AMI when the risk threshold value was>0.17.Conclusion This study constructs a nomogram predictive model for the risk factors of poor prognosis in the patients with AMI,which is helpful for the medical staff to understand the related factors of poor prognosis in AMI and formulate the personalized countermeasures to improve the prognosis as early as possible.