重症机械通气患者脱机失败的风险预测列线图模型构建与验证  

Construction and Validation of a Nomogram Model for Predicting the Risk of Offline Failure in Patients with Severe Mechanical Ventilation

作　　者：赵文婷 周大文 杨晓梅[1] 郑红艳 ZHAO Wenting;ZHOU Dawen;YANG Xiaomei;ZHENG Hongyan(Department of Respiratory and Critical Care Medicine,Huai'an Second People's Hospital,Huaian 223002,China;Department of Neurosurgery,Huai'an Second People's Hospital,Huaian 223002,China)

机构地区：[1]江苏省淮安市第二人民医院呼吸与危重症医学科,223002 [2]江苏省淮安市第二人民医院神经外科,223002

出　　处：《实用心脑肺血管病杂志》2023年第8期59-65,共7页Practical Journal of Cardiac Cerebral Pneumal and Vascular Disease

摘　　要：目的 构建并验证重症机械通气患者脱机失败的风险预测列线图模型。方法 采用便利抽样法,选取2020年5月至2022年5月在淮安市第二人民医院重症医学科进行机械通气的患者670例为研究对象。按照7∶3的比例将患者分为建模组(n=469)及验证组(n=201)。根据脱机结果将建模组进一步分为失败亚组(n=88)和成功亚组(n=381)。自行设计基线资料调查表并统计患者基线资料,采用多因素Logistic回归分析探讨建模组重症机械通气患者脱机失败的影响因素;基于多因素Logistic回归分析结果,采用R软件构建重症机械通气患者脱机失败的风险预测列线图模型;采用Hosmer-Lemeshow检验及校准曲线评估该列线图模型的校准度,采用ROC曲线评估该列线图模型的区分度。结果 失败亚组机械通气时间≥7 d、入院24 h内最低急性生理学与慢性健康状况评分系统Ⅱ(APACHEⅡ)评分≥15分、入院24 h内最低脓毒症相关性器官功能衰竭评价(SOFA)评分≥6分者占比及通气后动脉血二氧化碳分压(PaCO_(2))、呼吸机相关性膈肌功能障碍(VIDD)发生率高于成功亚组,脱机前24 h内血清白蛋白低于成功亚组(P<0.05)。多因素Logistic回归分析结果显示,机械通气时间、入院24 h内最低APACHEⅡ评分、入院24 h内最低SOFA评分、通气后PaCO_(2)、VIDD是建模组重症机械通气患者脱机失败的影响因素(P<0.05)。基于多因素Logistic回归分析结果,构建重症机械通气患者脱机失败的风险预测列线图模型。Hosmer-Lemeshow检验及校准曲线分析结果显示,该列线图模型预测建模组、验证组重症机械通气患者脱机失败的发生率分别与本组重症机械通气患者脱机失败的实际发生率比较,差异无统计学意义(χ^(2)=7.650,P=0.468;χ^(2)=7.465,P=0.487)。ROC曲线分析结果显示,该列线图模型预测建模组、验证组重症机械通气患者脱机失败的曲线下面积分别为0.870[95%CI(0.836,0.903)Objective To construct and validate a nomogram model for predicting the risk of offline failure in patients with severe mechanical ventilation.Methods A total of 670 patients with mechanical ventilation admitted to the Department of Intensive Care Medicine of Huai'an Second People's Hospital from May 2020 to May 2022 were selected as the research objects by convenience sampling method.Patients were divided into modeling group(n=469)and validation group(n=201)in a ratio of 7∶3.According to the offline results,the modeling group was further divided into failure subgroup(n=88)and success subgroup(n=381).The baseline data questionnaire was designed and the baseline data of patients were collected.Multivariate Logistic regression analysis was used to explore the influencing factors of offline failure in patients with severe mechanical ventilation in the modeling group.Based on the results of multivariate Logistic regression analysis,R software was used to establish the nomogram model for predicting the risk of offline failure in patients with severe mechanical ventilation.Hosmer-Lemeshow test and calibration curve were used to evaluate the calibration degree of the nomogram model.ROC curve was used to evaluate the differentiation of the nomogram model.Results The proportion of mechanical ventilation duration≥7 d,the lowest acute physiology and chronic health evaluationⅡ(APACHEⅡ)score≥15 points within 24 h of admission,the lowest sepsis-related organ failure assessment(SOFA)score≥6 points within 24 h of admission,partial pressure of arterial carbon dioxide(PaCO_(2))after ventilation and the incidence of ventilator-induced diaphragmatic dysfunction(VIDD)in the failure subgroup were higher than those in the successful subgroup,and the serum albumin within 24 h before offline was lower than that in the successful subgroup(P<0.05).Multivariate Logistic regression analysis showed that mechanical ventilation duration,the lowest APACHEⅡscore within 24 h of admission,the lowest SOFA score within 24 h of admission

关 键 词：机械通气 脱机失败 影响因素分析 列线图 预测 

分 类 号：R605.973[医药卫生—急诊医学]