建立并验证一个预测直接经皮冠状动脉介入治疗（PCI）后急性 ST 段抬高型心肌梗死患者院内死亡率的风险模型。

Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI.

机构信息

Department of Internal Medicine, Hebei Medical University, Shijiazhuang, Hebei, China.

Department of Cardiology, Hebei General Hospital, Shijiazhuang, Hebei, China.

出版信息

BMC Cardiovasc Disord. 2020 Dec 9;20(1):513. doi: 10.1186/s12872-020-01804-7.

BACKGROUND

Currently, how to accurately determine the patient prognosis after a percutaneous coronary intervention (PCI) remains unclear and may vary among populations, hospitals, and datasets. The aim of this study was to establish a prediction model of in-hospital mortality risk after primary PCI in patients with acute ST-elevated myocardial infarction (STEMI).

METHODS

This was a multicenter, observational study of patients with acute STEMI who underwent primary PCI. The outcome was in-hospital mortality. The least absolute shrinkage and selection operator (LASSO) method was used to select the features that were the most significantly associated with the outcome. A regression model was built using the selected variables to select the significant predictors of mortality. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.

RESULTS

Totally, 1169 and 316 patients were enrolled in the training and validation sets, respectively. Fourteen predictors were identified by the LASSO analysis: sex, Killip classification, left main coronary artery disease (LMCAD), grading of thrombus, TIMI classification, slow flow, application of IABP, administration of β-blocker, ACEI/ARB, symptom-to-door time (SDT), symptom-to-balloon time (SBT), syntax score, left ventricular ejection fraction (LVEF), and CK-MB peak. The mortality risk prediction nomogram achieved good discrimination for in-hospital mortality (training set: C-statistic = 0.987; model calibration: P = 0.722; validation set: C-statistic = 0.984, model calibration: P = 0.669). Area under the curve (AUC) values for the training and validation sets are 0.987 (95% CI: 0.981-0.994, P = 0.003) and 0.990 (95% CI: 0.987-0.998, P = 0.007), respectively. DCA shows that the nomogram can achieve good net benefit.

CONCLUSIONS

A novel nomogram was developed and is a simple and accurate tool for predicting the risk of in-hospital mortality in patients with acute STEMI who underwent primary PCI.

背景

目前，如何准确地确定经皮冠状动脉介入治疗（PCI）后的患者预后仍不清楚，且可能因人群、医院和数据集而异。本研究旨在建立急性 ST 段抬高型心肌梗死（STEMI）患者行直接 PCI 后住院死亡率风险的预测模型。

方法

这是一项多中心、观察性研究，纳入了接受直接 PCI 的急性 STEMI 患者。结局为住院死亡率。采用最小绝对收缩和选择算子（LASSO）方法选择与结局最显著相关的特征。使用所选变量构建回归模型，选择死亡率的显著预测因子。使用接受者操作特征（ROC）曲线和决策曲线分析（DCA）评估列线图的性能。

结果

共纳入 1169 例和 316 例患者分别进入训练集和验证集。LASSO 分析确定了 14 个预测因子：性别、Killip 分级、左主干冠状动脉疾病（LMCAD）、血栓分级、TIMI 分级、慢血流、IABP 应用、β受体阻滞剂的应用、ACEI/ARB、症状至门时间（SDT）、症状至球囊时间（SBT）、Syntax 评分、左心室射血分数（LVEF）和 CK-MB 峰值。死亡率风险预测列线图对住院死亡率具有良好的区分度（训练集：C 统计量=0.987；模型校准：P=0.722；验证集：C 统计量=0.984，模型校准：P=0.669）。训练集和验证集的曲线下面积（AUC）值分别为 0.987（95%CI：0.981-0.994，P=0.003）和 0.990（95%CI：0.987-0.998，P=0.007）。DCA 显示，该列线图可获得良好的净收益。