中国全科医学 ›› 2022, Vol. 25 ›› Issue (24): 2965-2974.DOI: 10.12114/j.issn.1007-9572.2022.0237

所属专题: 心血管最新文章合集

• 论著 • 上一篇    下一篇

急性心肌梗死患者经皮冠状动脉介入治疗后远期主要不良心血管事件预测模型的构建

李琴1,2,3, 檀鑫3, 姜文溪3, 袁梦3, 倪慧3, 王媛3, 杜杰1,2,3,*()   

  1. 1030001 山西省太原市,山西医科大学基础医学院生物化学与分子生物学教研室
    2030001 山西省太原市,山西医科大学分子影像精准诊疗省部共建协同创新中心
    3100029 北京市,首都医科大学附属北京安贞医院 北京市心肺血管疾病研究所血管生物研究室
  • 收稿日期:2022-02-11 修回日期:2022-05-23 出版日期:2022-08-20 发布日期:2022-06-30
  • 通讯作者: 杜杰
  • 李琴,檀鑫,姜文溪,等.急性心肌梗死患者经皮冠状动脉介入治疗后远期主要不良心血管事件预测模型的构建[J].中国全科医学,2022,25(24):2965-2974.[www.chinagp.net]
    作者贡献:李琴、檀鑫、姜文溪、王媛、杜杰进行文章的构思与设计,研究的实施与可行性分析,论文的修订,负责文章的质量控制及审校,对文章整体负责,监督管理;李琴、姜文溪负责研究的统计学处理,结果的分析与解释;李琴、袁梦、倪慧负责数据收集及患者随访;李琴、檀鑫、姜文溪负责撰写论文。
  • 基金资助:
    国家自然科学基金资助项目(81790622,91939303)

Predictive Model for Long-term Major Adverse Cardiovascular Events in Patients with Acute Myocardial Infarction Undergoing Percutaneous Coronary Intervention

Qin LI1,2,3, Xin TAN3, Wenxi JIANG3, Meng YUAN3, Hui NI3, Yuan WANG3, Jie DU1,2,3,*()   

  1. 1Department of Biochemistry and Molecular Biology, Shanxi Medical University, Taiyuan 030001, China
    2Shanxi Medical University-Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Taiyuan 030001, China
    3Beijing Anzhen Hospital, Capital Medical University/Cardiovascular Biology Laboratory, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing 100029, China
  • Received:2022-02-11 Revised:2022-05-23 Published:2022-08-20 Online:2022-06-30
  • Contact: Jie DU
  • About author:
    LI Q, TAN X, JIANG W X, et al. Predictive model for long-term major adverse cardiovascular events in patients with acute myocardial infarction undergoing percutaneous coronary intervention[J]. Chinese General Practice, 2022, 25 (24) : 2965-2974.

摘要: 背景 对急性心肌梗死(AMI)患者进行风险分层对临床决策和预后评估具有重要临床意义,由于AMI患者临床特征及治疗模式正在不断发生改变,现有的风险评分可能并不适用于临床实际情况,因此需要提高AMI患者经皮冠状动脉介入治疗(PCI)后远期主要不良心血管事件的预测准确性以制订患者个性化的管理策略。 目的 构建预测AMI患者PCI后远期主要不良心血管事件的风险模型。 方法 纳入2019年1—7月于首都医科大学附属北京安贞医院接受PCI的AMI患者1 130例,根据纳入、排除标准最终纳入962例患者,收集其一般资料和实验室检查指标。对所有患者进行电话随访,中位随访时间为2.4年,以全因死亡、非致死性心肌梗死、非致死性卒中、恶性心律失常、新发心力衰竭或心力衰竭加重再入院、非计划内的血运重建作为主要不良心血管事件。根据患者随访期间是否发生主要不良心血管事件分为事件组122例和非事件组840例。采用Lasso回归筛选远期主要不良心血管事件的危险因素,多因素Logistic回归分析构建预测模型,并绘制列线图。采用受试者工作特征(ROC)曲线分析模型预测AMI患者PCI后发生远期主要不良心血管事件的效能,使用净重分类改善指标(NRI)和综合判别指数(IDI)对预测模型与全球急性冠状动脉事件注册(GRACE)评分进行比较,评价模型对AMI患者PCI后预后评估的改善效果。 结果 962例AMI患者中122例(12.7%)患者出现远期主要不良心血管事件。Lasso回归筛选出5个预测变量,包括心电图ST段偏移、糖尿病、左心室射血分数(LVEF)、估算肾小球滤过率(eGFR)、血红蛋白(Hb)。通过多因素Logistic回归分析构建的预测模型的回归方程为:logit(P)=3.596-0.023×X1-0.014×X2-0.036×X3+0.726×X4+1.372×X5(X1表示Hb,X2表示eGFR,X3表示LVEF,X4表示糖尿病,X5表示心电图ST段偏移)。心电图ST段偏移、糖尿病、LVEF、Hb是AMI患者PCI后发生远期主要不良心血管事件的影响因素(P<0.05);心电图ST段偏移、糖尿病、eGFR、Hb是ST段抬高型心肌梗死(STEMI)患者PCI后发生远期主要不良心血管事件的影响因素(P<0.05);心电图ST段偏移、糖尿病、Hb是非ST段抬高型心肌梗死(NSTEMI)患者PCI后发生远期主要不良心血管事件的影响因素(P<0.05)。预测模型预测开发队列与验证队列患者PCI后发生远期主要不良心血管事件的ROC曲线下面积(AUC)分别为0.774〔95%CI(0.710,0.834)〕、0.751〔95%CI(0.686,0.815)〕。AMI、STEMI、NSTEMI患者中NRI分别为0.493〔95%CI(0.303,0.682)〕、0.459〔95%CI(0.195,0.724)〕、0.455〔95%CI(0.181,0.728〕,IDI分别为0.055〔95%CI(0.028,0.081)〕、0.042〔95%CI(0.015,0.070〕、0.069〔95%CI(0.022,0.116)〕。3组患者中预测模型的预测效能均优于GRACE评分(P<0.05)。全研究人群队列分析发现预测模型的评价效能优于GRACE评分〔ΔAUC=0.050,P=0.015;IDI=0.055,95%CI(0.028,0.081),P<0.001;NRI=0.493,95%CI(0.303,0.682),P<0.001)〕。 结论 由心电图ST段偏移、糖尿病、LVEF、eGFR、Hb共5个预测变量构建的预测模型可用于评估AMI患者PCI后远期预后,有助于患者早期风险分层。

关键词: 心肌梗死, 急性心肌梗死, 经皮冠状动脉介入治疗, 主要不良心血管事件, 预测, 风险评估

Abstract:

Background

Risk stratification for acute myocardial infarction (AMI) is important for clinical decision-making and prognosis evaluation. As changes have been found in clinical characteristics and management of AMI, the current existing clinical risk score for AMI may be inapplicable to clinical practice. To effectively implement strategies of individualized management for AMI patients, it is necessary to improve the prediction accuracy of long-term major adverse cardiovascular events (MACEs) in AMI after percutaneous coronary intervention (PCI) .

Objective

To develop a predictive model for long-term MACEs in AMI patients after PCI.

Methods

Among the 1 130 AMI patients treated with PCI in Beijing Anzhen Hospital from January 1 to July 31, 2019, 962 eligible cases were enrolled, and their clinical data and laboratory examination indices were collected. Follow-up of the patients was performed via telephone interviews at a median of 2.4 years. The primary endpoint was a composite of all-cause mortality, non-fatal myocardial infarction, non-fatal stroke, malignant arrhythmia, new heart failure or readmission due to exacerbated heart failure, and unplanned revascularization. Patients were divided into event (122 cases) and non-event (840 cases) groups according to the prevalence of MACEs during the follow-up period. Lasso regression was conducted to identify candidate risk factors of long-term MACEs. Multivariate Logistic regression analysis was used to construct the prediction model and the nomograms. The receiver operating characteristic curve was used to evaluate the discrimination ability of the prediction model. The efficacy of the predictive model was assessed by comparing with that of the Global Registry of Acute Coronary Events (GRACE) score in terms of the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) .

Results

The prevalence of MACEs was 12.7% (122/962) . Five predictive variables were identified by Lasso regression, which included ST-segment deviation, diabetes history, hemoglobin (Hb) , left ventricular ejection fraction (LVEF) , and estimated glomerular filtration rate (eGFR) . The algorithm of the prediction model developed using multivariate Logistic regression was: logit (P) =3.596-0.023×X1-0.014×X2-0.036×X3+0.726×X4+1.372×X5 (X1-X5 indicate Hb, eGFR, LVEF, diabetes, and ST-segment deviation, respectively) . ST-segment deviation, diabetes, LVEF, and Hb were associated with MACEs in AMI patients after PCI (P<0.05) . ST-segment deviation, diabetes, eGFR and Hb were associated with MACEs in ST-segment elevation myocardial infarction (STEMI) patients after PCI (P<0.05) . ST-segment deviation, diabetes, and Hb were associated with MACEs in non-STEMI patients after PCI (P<0.05) . The prediction model exhibited an area under the curve (AUC) of 0.774〔95%CI (0.710, 0.834) 〕 for the training cohort, and an AUC of 0.751〔95%CI (0.686, 0.815) 〕for the testing cohort. The NRI estimated by the predictive model in AMI, STEMI, and non-STEMI patients was 0.493〔95%CI (0.303, 0.682) 〕, 0.459〔95%CI (0.195, 0.724) 〕, and 0.455〔95%CI (0.181, 0.728〕, respectively. The IDI estimated by the predictive model in AMI, STEMI, and non-STEMI patients was 0.055〔95%CI (0.028, 0.081) 〕, 0.042〔95%CI (0.015, 0.070〕, and 0.069〔95%CI (0.022, 0.116) 〕, respectively. The predictive efficiency of the predictive model in the three groups was significantly better than that of the GRACE score (P<0.05) . The predictive model was significantly better than the GRACE score in all participants 〔ΔAUC=0.050, P=0.015; IDI=0.055, 95%CI (0.028, 0.081) , P<0.001; NRI=0.493, 95%CI (0.303, 0.682) , P<0.001) 〕.

Conclusion

Our predictive model containing five factors (ST-segment deviation, diabetes, LVEF, eGFR and Hb) may be useful for early risk stratification and long-term prognosis prediction in patients with AMI after PCI.

Key words: Myocardial infarction, Acute myocardial infarction, Percutaneous coronary intervention, Major adverse cardiovascular events, Forecasting, Risk assessment