脓毒症合并急性肺栓塞患者院内死亡风险列线图预测模型的开发与验证  

Development and validation of a nomogram prediction model for in-hospital mortality risk in patients with sepsis complicated with acute pulmonary embolism

作　　者：黄丽[1] 王正斌[1] 张岩[1] 岳筱 王硕[1] 高艳霞[1] Huang Li;Wang Zhengbin;Zhang Yan;Yue Xiao;Wang Shuo;Gao Yanxia(Department of Emergency Medicine,the First Affiliated Hospital of Zhengzhou University,Zhengzhou 450052,Henan,China)

机构地区：[1]郑州大学第一附属医院急诊医学科,河南郑州450052

出　　处：《中华危重病急救医学》2025年第2期123-127,共5页Chinese Critical Care Medicine

基　　金：国家自然科学基金(82172180)。

摘　　要：目的探讨影响脓毒症合并急性肺栓塞患者预后的危险因素,构建院内死亡风险列线图预测模型并进行内部验证。方法基于美国重症监护医学信息数据库(MIMIC-Ⅲ、MIMIC-Ⅳ),收集2001至2019年脓毒症合并急性肺栓塞患者的数据,包括基线特征及入重症监护病房(ICU)24 h内的生命体征、疾病评分、实验室检查以及干预措施。以院内死亡为结局事件。采用随机抽样的方式按照7∶3的比例将总样本分为训练集和测试集。利用单因素Cox回归分析所有变量对患者院内死亡风险的影响,以此筛选潜在影响因素,随后通过双向逐步回归法进行逐一筛选,从而构建列线图预测模型,应用共线性检验证明列线图模型中影响因素之间的共线性。用C-指数评价列线图模型、序贯器官衰竭评分(SOFA)、简化肺栓塞严重程度指数(sPESI)的区分度;绘制受试者工作特征曲线(ROC曲线),评估各模型对脓毒症合并急性肺栓塞患者院内死亡的预测价值。结果共纳入562例脓毒症合并急性肺栓塞患者,其中训练集393例,测试集169例。单因素Cox回归分析显示,有30个因素与脓毒症合并急性肺栓塞患者院内死亡有关;通过双向逐步回归法对上述因素进行逐一筛选,最终筛选出性别、合并恶性肿瘤、体温、红细胞分布宽度(RDW)、血尿素氮(BUN)、血钾、凝血酶原时间(PT)、24 h尿量、机械通气、血管活性药物、华法林、脓毒症相关性凝血功能障碍(SIC)共12个变量,共线性检验说明影响因素之间并无较强的共线性〔方差膨胀因子(VIF)均大于10〕。以上述12个变量构建列线图模型,该列线图模型预测脓毒症合并急性肺栓塞患者院内死亡的C-指数及其95%可信区间(95%CI)优于SOFA评分和sPESI〔0.771(0.725~0.816)比0.579(0.519~0.639)、0.608(0.554~0.663)〕,ROC曲线显示,列线图模型的曲线下面积(AUC)及其95%CI高于SOFA评分和sPESI〔0.811(0.766~0.857)比0.630(0.568~0.Objective To explore the risk factors affecting the prognosis of patients with sepsis complicated with acute pulmonary embolism,and to construct and validate a nomogram predictive model for in-hospital mortality risk.Methods Based on the American Medical Information Mart for Intensive Care(MIMIC-Ⅲ,MIMIC-Ⅳ)databases,the data were collected on patients with sepsis complicated with acute pulmonary embolism from 2001 to 2019,including baseline characteristics,and vital signs,disease scores,laboratory tests within 24 hours of admission to the intensive care unit(ICU),and interventions.In-hospital mortality was the outcome event.The total samples were divided into training and testing sets in a 7∶3 ratio by random sampling.Univariate Cox regression analysis was used to verify the impact of all variables on the risk of in-hospital mortality,thereby screen potential influencing factors.Subsequently,a stepwise bi-directional regression method was applied to select factors one by one,leading to the construction of a nomogram prediction model.Collinearity testing was used to demonstrate the absence of strong multicollinearity among the influencing factors in the nomogram prediction model.The discrimination of the nomogram model,sequential organ failure assessment(SOFA),and simplified pulmonary embolism severity index(sPESI)was evaluated using C-index in the test set.Receiver operator characteristic curve(ROC curve)was drawn to evaluate the predictive value of various models for in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism.Results A total of 562 patients with sepsis complicated with acute pulmonary embolism were included,including 393 in the training set and 169 in the testing set.Univariate Cox regression analysis showed that 30 factors associated with in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism.Through stepwise bi-directional regression,12 variables were ultimately selected,including gender,presence of malignant tumors,body tempera

关 键 词：脓毒症 急性肺栓塞 院内死亡 列线图模型 

分 类 号：R563.5[医药卫生—呼吸系统]