Page 117 - 《南京医科大学学报（自然科学版）》2026年第3期

第46卷第3期 南京医科大学学报（自然科学版）
2026年3月 Journal of Nanjing Medical University（Natural Sciences） ·425 ·

·临床研究·

基于列线图的 VA⁃ECMO 患者临床死亡风险预测模型构建

杨 洋，朱 轶，吴 昊 *
南京医科大学第一附属医院急诊与危重症医学科，江苏 南京 210029

［摘 要］ 目的：探讨接受静脉⁃动脉体外膜肺氧合（venous⁃arterial extracorporeal membrane oxygenation，VA⁃ECMO）治疗的急
性心肌梗死（acute myocardial infarction，AMI）患者院内死亡的危险因素，并构建列线图预测模型。方法：回顾性纳入 2021 年
5 月—2025年6月收治的162例接受 VA⁃ECMO 治疗的AMI患者临床资料。以上机时间为随访起点，随访至出院或死亡（以先
发生者为准），以院内全因死亡为终点事件。采用Cox比例风险回归模型分析各因素与院内死亡风险的相关性。采用最小绝
对收缩与选择算子（least absolute shrinkage and selection operator，LASSO）回归筛选预测变量，并构建多因素Cox回归模型。基
于最终模型构建列线图，用于预测患者院内生存概率。通过一致性指数（C⁃index）评价模型整体区分度，并以28 d作为时间依
赖受试者工作特征（receiver operating characteristic，ROC）曲线分析的固定时间节点，评估模型在短期时间窗口内的预测能力，
采用校准曲线评估模型校准度，并通过临床决策曲线分析（decision curve analysis，DCA）评价模型的临床实用价值。结果：多
因素分析显示，心肌肌钙蛋白T、可溶性生长刺激表达基因 2 蛋白、血红蛋白浓度、凝血酶原时间、血钠浓度和丙氨酸氨基转移
酶与院内死亡风险显著相关，白细胞计数与白蛋白在模型中呈边缘统计学意义。基于上述8项变量构建的列线图模型具有较
高的预测准确性和良好的校准能力，显示出较好的临床应用价值。结论：本研究构建并验证了一个用于评估接受 VA⁃ECMO
治疗的AMI患者院内死亡风险的列线图预测模型，为VA⁃ECMO 患者死亡风险的个体化评估提供了一种简便、可靠的工具，对
临床决策和治疗策略优化具有重要指导意义。
［关键词］ 体外膜肺氧合；急性心肌梗死；死亡风险；列线图
［中图分类号］ R542.22 ［文献标志码］ A ［文章编号］ 1007⁃4368（2026）03⁃425⁃10
doi：10.7655/NYDXBNSN260051

A nomogram⁃based prediction model for clinical mortality risk in VA⁃ECMO patients
YANG Yang，ZHU Yi，WU Hao *
Department of Emergency and Critical Care Medicine，the First Affiliated Hospital of Nanjing Medical University，
Nanjing 210029，China

［Abstract］ Objective：To identify risk factors associated with in⁃hospital death and to develop a nomogram⁃based predictive model
for in⁃hospital mortality in acute myocardial infarction（AMI）patients treated with venous⁃arterial extracorporeal membrane oxygenation
（VA⁃ECMO）. Methods：A total of 162 consecutive patients with AMI who received VA⁃ECMO support between May 2021 and June
2025 were retrospectively enrolled. The time of ECMO initiation was defined as the start of follow⁃up，and patients were followed until
hospital discharge or death，whichever occurred first. In ⁃ hospital all ⁃ cause mortality was defined as the primary endpoint. Cox
proportional hazards regression analysis was performed to evaluate the associations between candidate variables and the risk of in⁃
hospital mortality. Variables were selected using least absolute shrinkage and selection operator（LASSO）regression，and a
multivariable Cox regression model was subsequently constructed. Based on the final model，a nomogram was developed to predict in⁃
hospital survival probability. Model discrimination was assessed using the concordance index（C⁃index）. The 28⁃day time point was
used as a fixed landmark for time⁃dependent receiver operating characteristic（ROC）curve analysis to evaluate short⁃term predictive
performance. Model calibration was evaluated using calibration curves，and clinical utility was assessed using decision curve analysis
（DCA）. Results：Multivariable analysis demonstrated that cardiac troponin T，soluble suppression of tumorigenicity ⁃ 2（sST2），
hemoglobin concentration，prothrombin time，serum sodium level，and alanine aminotransferase were significantly associated with in⁃

［基金项目］ 国家自然科学基金（82272244）
通信作者（Corresponding author），E⁃mail：drwuhao@njmu.edu.cn（ORCID：0009⁃0002⁃9819⁃8619）
∗