Page 25 - 2022-24-中国全科医学

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WANG Yuan ，DU Jie 1，2，3*
1.Department of Biochemistry and Molecular Biology，Shanxi Medical University，Taiyuan 030001，China
2.Shanxi Medical University-Collaborative Innovation Center for Molecular Imaging of Precision Medicine，Taiyuan 030001，
China
3.Beijing Anzhen Hospital，Capital Medical University/Cardiovascular Biology Laboratory，Beijing Institute of Heart Lung and
Blood Vessel Diseases，Beijing 100029，China
*
Corresponding author：DU Jie，Professor，Doctoral supervisor；E-mail：jiedu@yahoo.com
【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

急性心肌梗死（acute myocardial infarction，AMI） 预后，但主要不良心血管事件发生风险仍然很高 ［3-4］ 。
是冠心病的严重类型，也是导致冠心病患者死亡、残疾 2017 年欧洲心脏病学会（ESC）《急性 ST 段抬高型心
主要原因 ［1-2］ 。经皮冠状动脉介入治疗（percutaneous 肌梗死指南》 ［5］ 及 2020 年 ESC《非 ST 段抬高型急性
coronary intervention，PCI）是 AMI 患者的主要治疗方 冠脉综合征指南》 ［6］ 均建议应用风险评分对 AMI 患者
法。多项研究证明，尽管 PCI 可改善 AMI 患者的远期 进行风险分层，早期风险分层对于最佳二级预防药物的