摘要
目的探讨脓毒症患者发生急性呼吸窘迫综合征(ARDS)的独立危险因素,建立预警模型,并基于合成少数类过采样技术(SMOTE)算法对模型进行预测价值验证。方法采用回顾性病例对照研究方法,选择2016年10月至2022年10月济南市人民医院收治的566例脓毒症患者。收集患者的一般资料、基础疾病、感染部位、起始病因、病情严重程度评分、入院时血液指标和动脉血气分析指标、治疗措施、并发症及预后指标。根据患者住院期间是否发生ARDS分组,观察对比两组患者的临床资料;采用单因素和二元多因素Logistic回归分析筛选脓毒症患者住院期间发生ARDS的独立危险因素,并建立回归方程,构建预警模型,同时基于SMOTE算法改进数据集,构建改进数据集的预警模型;绘制受试者工作特征曲线(ROC曲线),对比验证模型的预测效能。结果566例脓毒症患者均纳入最终分析,其中163例在住院期间发生ARDS,403例未发生ARDS。单因素分析显示,两组患者年龄、体质量指数(BMI)、恶性肿瘤、输血史、胰腺及胰周感染、胃肠道感染、起始病因为肺部感染、急性生理学与慢性健康状况评分Ⅱ(APACHEⅡ)、序贯器官衰竭评分(SOFA)、白蛋白(Alb)、血尿素氮(BUN)、机械通气治疗、脓毒性休克比例及重症监护病房(ICU)住院时间差异均有统计学意义。二元多因素Logistic回归分析显示,年龄〔优势比(OR)=3.449,95%可信区间(95%CI)为2.197~5.414,P=0.000〕、起始病因为肺部感染(OR=2.309,95%CI为1.427~3.737,P=0.001)、胰腺及胰周感染(OR=1.937,95%CI为1.236~3.035,P=0.004)、脓毒性休克(OR=3.381,95%CI为1.890~6.047,P=0.000)、SOFA评分(OR=9.311,95%CI为5.831~14.867,P=0.000)为脓毒症患者住院期间发生ARDS的独立危险因素。基于上述危险因素建立预警模型:P1=-4.558+1.238×年龄+0.837×起始病因为肺部感染+0.661×胰腺及胰周感染+1.218×脓毒性休克+2.231×SOFA评分;ROC曲线分析�
Objective To explore the independent risk factors of acute respiratory distress syndrome(ARDS)in patients with sepsis,establish an early warning model,and verify the predictive value of the model based on synthetic minority oversampling technique(SMOTE)algorithm.Methods A retrospective case-control study was conducted.566 patients with sepsis who were admitted to Jinan People's Hospital from October 2016 to October 2022 were enrolled.General information,underlying diseases,infection sites,initial cause,severity scores,blood and arterial blood gas analysis indicators at admission,treatment measures,complications,and prognosis indicators of patients were collected.The patients were grouped according to whether ARDS occurred during hospitalization,and the clinical data between the two groups were observed and compared.Univariate and binary multivariate Logistic regression analysis were used to select the independent risk factors of ARDS during hospitalization in septic patients,and regression equation was established to construct the early warning model.Simultaneously,the dataset was improved using the SMOTE algorithm to build an enhanced warning model.Receiver operator characteristic curve(ROC curve)was drawn to validate the prediction efficiency of the model.Results 566 patients with sepsis were included in the final analysis,of which 163 developed ARDS during hospitalization and 403 did not.Univariate analysis showed that there were statistically significant differences in age,body mass index(BMI),malignant tumor,blood transfusion history,pancreas and peripancreatic infection,gastrointestinal tract infection,pulmonary infection as the initial etiology,acute physiology and chronic health evaluationⅡ(APACHEⅡ)score,sequential organ failure assessment(SOFA)score,albumin(Alb),blood urea nitrogen(BUN),mechanical ventilation therapy,septic shock and length of intensive care unit(ICU)stay between the two groups.Binary multivariate Logistic regression analysis showed that age[odds ratio(OR)=3.449,95%confidence interva
作者
段红伟
李晓静
杨兴菊
王飞
杨逢永
Duan Hongwei;Li Xiaojing;Yang Xingju;Wang Fei;Yang Fengyong(Department of Critical Care Medicine,Jinan People's Hospital(People's Hospital Affiliated to Shandong First Medical University),Jinan 271100,Shandong,China;Department of Nursing,Jinan People's Hospital(People's Hospital Affiliated to Shandong First Medical University),Jinan 271100,Shandong,China)
出处
《中华危重病急救医学》
CAS
CSCD
北大核心
2024年第4期358-363,共6页
Chinese Critical Care Medicine
基金
山东省自然科学基金(ZR2021MH329)
山东省济南市临床医学科技创新计划项目(202134054)。
关键词
脓毒症
急性呼吸窘迫综合征
危险因素
回归方程
合成少数类过采样技术算法
Sepsis
Acute respiratory distress syndrome
Risk factor
Regression equation
Synthetic minority oversampling technique algorithm
