脓毒症患者28d死亡危险因素分析及预测模型的构建与验证  

Analysis of 28 day-mortality risk factors in sepsis patients and construction and validation of predictive model

作　　者：邵慧娟 王炎 张宏伟 周亚鹏 张江明 姚浩旗 刘东 刘冬梅 Shao Huijuan;Wang Yan;Zhang Hongwei;Zhou Yapeng;Zhang Jiangming;Yao Haoqi;Liu Dong;Liu Dongmei(Department of Intensive Care Unit,the 940th Hospital of Joint Logistic Support Force of PLA,Lanzhou 730050,Gansu,China;The First Clinical Medical College,Gansu University of Traditional Chinese Medicine,Lanzhou 730000,Gansu,China;Department of Anesthesiology,the 940th Hospital of Joint Logistic Support Force of PLA,Lanzhou 730050,Gansu,China)

机构地区：[1]解放军联勤保障部队第九四〇医院重症医学科,甘肃兰州730050 [2]甘肃中医药大学第一临床医学院,甘肃兰州730000 [3]解放军联勤保障部队第九四〇医院麻醉科,甘肃兰州730050

出　　处：《中华危重病急救医学》2024年第5期478-484,共7页Chinese Critical Care Medicine

基　　金：甘肃省自然科学基金(21JR11RA005,21JR7RA002);甘肃省兰州市科技计划项目(2023-ZD-180)。

摘　　要：目的构建用于预测脓毒症患者28 d死亡风险的列线图模型并验证其有效性。方法回顾性分析2017年1月至2022年12月解放军联勤保障部队第九四〇医院重症医学科收治的281例脓毒症患者作为研究对象,按照7∶3的比例分为训练集(197例)和验证集(84例)。收集患者一般情况、入住重症监护病房(ICU)24 h内的诊疗情况、实验室检查指标等。依据28 d临床结局将患者分为存活组和死亡组。比较两组患者间各资料的差异,用Lasso回归筛选最优预测变量,用单因素和多因素Logistic回归分析脓毒症患者死亡的影响因素并建立列线图模型,通过受试者工作特征曲线(ROC曲线)、校准曲线、决策曲线分析(DCA)和临床影响曲线(CIC)对模型进行评价。结果281例脓毒症患者28 d死亡82例(29.18%),其中训练集和验证集死亡患者分别为54例(27.41%)和28例(33.33%)。Lasso回归分析、单因素和多因素Logistic回归分析后筛选出5个与脓毒症患者死亡相关的独立预测因子,分别为血管活性药物使用〔优势比(OR)=5.924,95%可信区间(95%CI)为1.244~44.571,P=0.043〕、急性生理学与慢性健康状况评分Ⅱ(APACHEⅡ:OR=1.051,95%CI为1.000~1.107,P=0.050)、合并多器官功能障碍综合征(MODS:OR=17.298,95%CI为5.517~76.985,P<0.001)、中性粒细胞计数(NEU:OR=0.934,95%CI为0.879~0.988,P=0.022)和氧合指数(PaO2/FiO2:OR=0.994,95%CI为0.988~0.998,P=0.017)。以上述独立预测因子构建列线图模型,ROC曲线分析显示,该列线图模型在训练集和验证集的曲线下面积(AUC)分别为0.899(95%CI为0.856~0.943)和0.909(95%CI为0.845~0.972);训练集和验证集的一致性指数(C-index)分别为0.900和0.920,提示区分度较好;Hosmer-Lemeshoe检验均显示P>0.05,说明校准度较好,DCA和CIC证明该模型具有良好的临床效用。结论血管活性药物使用、APACHEⅡ评分、合并MODS、NEU和PaO2/FiO2是脓毒症患者28 d死亡的独立危险因素,基于上述因素构建的列Objective To construct and validate a nomogram model for predicting the risk of 28-day mortality in sepsis patients.Methods A retrospective cohort study was conducted.281 sepsis patients admitted to the department of intensive care unit(ICU)of the 940th Hospital of the Joint Logistics Support Force of PLA from January 2017 to December 2022 were selected as the research subjects.The patients were divided into a training set(197 cases)and a validation set(84 cases)according to a 7∶3 ratio.The general information,clinical treatment measures and laboratory examination results within 24 hours after admission to ICU were collected.Patients were divided into survival group and death group based on 28-day outcomes.The differences in various data were compared between the two groups.The optimal predictive variables were selected using Lasso regression,and univariate and multivariate Logistic regression analyses were performed to identify factors influencing the mortality of sepsis patients and to establish a nomogram model.Receiver operator characteristic curve(ROC curve),calibration curve,decision curve analysis(DCA),and clinical impact curve(CIC)were used to evaluate the nomogram model.Results Out of 281 cases of sepsis,82 cases died with a mortality of 29.18%.The number of patients who died in the training and validation sets was 54 and 28,with a mortality of 27.41%and 33.33%respectively.Lasso regression,univariate and multivariate Logistic regression analysis screened for 5 independent predictors associated with 28-day mortality.There were use of vasoactive drugs[odds ratio(OR)=5.924,95%confidence interval(95%CI)was 1.244-44.571,P=0.043],acute physiology and chronic health evaluationⅡ(APACHEⅡ:OR=1.051,95%CI was 1.000-1.107,P=0.050),combined with multiple organ dysfunction syndrome(MODS:OR=17.298,95%CI was 5.517-76.985,P<0.001),neutrophil count(NEU:OR=0.934,95%CI was 0.879-0.988,P=0.022)and oxygenation index(PaO2/FiO2:OR=0.994,95%CI was 0.988-0.998,P=0.017).A nomogram model was constructed using the independent p

关 键 词：脓毒症 多器官功能障碍综合征 中性粒细胞计数 氧合指数 危险因素 列线图模型 预后 

分 类 号：R459.7[医药卫生—急诊医学]