构建一种基于急诊常用指标的新型预测模型，用于评估急诊就诊并接受经皮冠状动脉介入术（PCI）的ST段抬高型心肌梗死（STEMI）患者的院内死亡风险。

回顾性分析2020年12月26日至2021年12月25日期间在韩国延世大学医学院急诊科和深圳市第四人民医院急诊科（2021年1月1日至2021年12月31日期间）接受PCI治疗的842例STEMI患者临床资料。延世大学503例作为训练集、251例作为内部验证集，深圳市第四人民医院88例作为外部验证集。采用LASSO回归和非条件Logistic逐步回归方法筛选预测变量，建立预测模型并以列线图形式呈现。通过ROC曲线、校准曲线和临床决策曲线评价模型的区分度、校准度及临床应用价值，并进行内部和外部验证。

延世大学医学院最终纳入754例接受PCI术的STEMI患者，34例发生了院内死亡。深圳市第四人民医院纳入88例患者，院内死亡6人。经LASSO回归和非条件Logistic逐步回归方法，筛选出左室射血分数、收缩压、高密度脂蛋白胆固醇、肌酸激酶同工酶、脑尿钠肽、年龄、心率7个预测因素，构建STEMI患者PCI术后院内死亡的预测模型：logit（P）=-2.85810-0.10535×左室射血分数+0.00551×肌酸激酶同工酶-0.00002×脑尿钠肽+0.07765×年龄-0.01181×收缩压-0.03742×高密度脂蛋白胆固醇+0.01314×心率。该模型在内部验证集和外部验证集的ROC曲线下面积（AUC）分别为0.88和0.83，显示出良好的预测性能。校准曲线提示其院内死亡预测值与实测值有较好的一致性（HL检验，P=0.648），临床决策曲线则表明模型具有良好的校准度和临床应用价值。

STEMI患者PCI术后院内死亡预测模型基于急诊科易获取的临床指标，具有良好的区分度、校准度和临床应用价值。

To develop a novel predictive model based on commonly used emergency indicators for assessing the risk of in-hospital mortality among ST-segment elevation myocardial infarction (STEMI) patients who underwent percutaneous coronary intervention (PCI) at the emergency department.

A retrospective analysis was conducted based on the clinical data of 842 STEMI patients who received PCI treatment (from Dec. 26, 2020 to Dec. 25, 2021) at the emergency departments of Yonsei University College of Medicine and the Fourth People's Hospital of Shenzhen (From Jan. 1, 2021 to Dec. 31, 2021). 503 cases from Yonsei University were used as the training set, 251 cases as the internal validation set, and 88 cases from the Fourth People's Hospital of Shenzhen as the external validation set. LASSO regression and unconditional logistic stepwise regression were employed to screen predictive variables, to establish a prediction model, and to present it in the form of nomograms. The model's discrimination, calibration, and clinical applicability were evaluated using ROC curves, calibration curves, and clinical decision curves, respectively, followed by internal and external validation.

A total of 842 STEMI patients after PCI were included, with 40 cases of in-hospital deaths. Using LASSO regression and unconditional logistic stepwise regression methods, seven predictive factors were identified: left ventricular ejection fraction, systolic blood pressure, high-density lipoprotein cholesterol, creatine kinase isoenzyme, brain natriuretic peptide, age, and heart rate. A prediction model of in-hospital mortality after PCI in STEMI patients on basis of these factors were constructed as follows: logit (P)=-2.85810-0.10535×left ventricular ejection fraction+0.00551×creatine kinase isoenzyme-0.00002×brain natriuretic peptide+0.07765×age-0.01181×systolic blood pressure-0.03742×high-density lipoprotein cholesterol+ 0.01314×heart rate. The model demonstrated good predictive performance with ROC curve areas (AUC) of 0.88,0.83 for internal and external validation sets, respectively. Calibration curves indicated good agreement between predicted and observed in-hospital mortality values (HL test, P=0.648), and clinical decision curves showed that the model had good calibration and clinical applicability.

Based on easily accessible clinical indicators from the emergency department, the novel in-hospital mortality prediction model for STEMI patients after PCI demonstrates good discrimination, calibration, and clinical applicability.