早发急性ST段抬高型心肌梗死患者预后风险列线图模型的构建与验证
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1.南京医科大学附属淮安一院;2.南京医科大学附属淮安第一医院心内科

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Establishment and validation of prognostic risk nomogram model among patients with premature ST-segment elevation myocardial infarctionDepartment of Cardiology
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    摘要:

    目的:分析早发ST段抬高型心肌梗死(ST-segment elevated myocardial infarction,STEMI)患者的危险因素,并建立预测早发STEMI患者术后主要心血管不良事件(Major Adverse Cardiac Events,MACE)发生风险的列线图模型。方法:选取2017年1月-2018年12月在我院诊断为早发STEMI并行经皮冠状动脉介入术(percutaneous coronary intervention, PCI)治疗的166例患者为研究对象,并随访24个月,依据MACE的发生情况,分为MACE组(62例)与非MACE组(104例)。利用LASSO回归与Cox回归分析筛选危险因素,并构建列线图预测模型。通过受试者工作特征曲线(receiver operating characteristic curve,ROC),临床决策曲线(decision curve analysis,DCA),校准曲线(calibration curve)等评估模型的效能,并通过Bootstrap法自抽样验证模型的稳定性。结果:LASSO回归与Cox回归结果表明NGAL,肌酐,TIME,LVEF,Artery为早发STEMI患者术后发生MACE的重要危险因素(P<0.05),利用这5个预测指标构建了列线图预测模型。在6,12,24个月,模型的ROC曲线下面积分别为0.95(95% Cl:0.89~1.00),0.94(95% Cl:0.80~0.99),0.87(95% Cl:0.82~0.93),均大于单个危险因素;Calibration校准曲线接近理想曲线;DCA曲线显示列线图预测模型在0.25~1阈值概率范围内表现的更好。结论:本研究根据早发STEMI患者PCI术后发生MACE的危险因素构建了列线图预测模型,经Bootstrap内部验证后,该预测模型具有较好的效能与稳定性,能够较为准确的预测早发STEMI患者PCI术后6,12,24个月MACE的发生风险。有助于针对早发STEMI高危患者进行个体化干预治疗,改善其预后。

    Abstract:

    Objective: To analyze the risk factors of patients with premature ST segment elevation myocardial infarction and to develop a nomogram model to predict the risk of postoperative MACE in patients with premature STEMI. Methods: A total of 166 patients diagnosed with premature STEMI undergoing PCI in our hospital from January 2017 to December 2018 were selected as the research subjects, and they were followed up for 24 months. According to the occurrence of MACE, they were divided into MACE group (62 cases) and non-MACE group (104 cases).Risk factors were screened using lasso regression with Cox regression analysis, and a nomogram prediction model was constructed. The efficacy of the model was evaluated by receiver operating characteristic curve (ROC), clinical decision curve (DCA), calibration curve, etc., and the stability of the model was verified by bootstrap self sampling.Results: The results of LASSO regression and Cox regression showed that NGAL, Creatinine, TIME, LVEF, Artery were significant risk factors for the occurrence of postoperative MACE in patients with premature STEMI (P < 0.05), and the nomogram prediction model was constructed using these 5 predictors. The areas under the ROC curves of the models were 0.95(95% Cl:0.89~1.00),0.94(95% Cl:0.80~0.99),0.87(95% Cl:0.82~0.93) at 6, 12, 24 months, respectively, which were larger than those of the individual risk factors; The calibration curve is close to the ideal curve; The DCA curves showed better performance of the nomogram prediction model in the 0.25 ~ 1 threshold probability range. Conclusion: In this study, a nomogram prediction model was constructed based on the significant risk factors for the occurrence of MACE after PCI in patients with premature STEMI, and after bootstrap internal validation, the prediction model had better efficacy and stability, and could accurately predict the risk of MACE at 6, 12, and 24 months after PCI in patients with premature STEMI. It is helpful to carry out individualized intervention treatment for high-risk patients with premature STEMI and improve their prognoses.

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  • 收稿日期:2022-05-24
  • 最后修改日期:2022-08-09
  • 录用日期:2022-11-10
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