俯卧位通气相关面部压力性损伤危险因素分析及最佳建模方法选择  

Risk Factors of Prone Position Ventilation-related Facial Pressure Injuries and the Selection of Best Modeling Method

作　　者：袁媛[1] 张亚荣 李振刚 张莉[2] YUAN Yuan;ZHANG Yarong;LI Zhengang;ZHANG Li(Department of Critical Care Medicine,Urumqi,the First Affiliated Hospital of Xinjiang Medical University,Urumqi 830092,China;Department of Nursing,the First Affiliated Hospital of Xinjiang Medical University,Urumqi 830092,China)

机构地区：[1]新疆医科大学第一附属医院重症医学中心,新疆维吾尔自治区乌鲁木齐市830092 [2]新疆医科大学第一附属医院护理部,新疆维吾尔自治区乌鲁木齐市830092

出　　处：《中国全科医学》2024年第8期948-954,共7页Chinese General Practice

基　　金：新疆维吾尔自治区自然科学基金资助项目(2021D01C455)。

摘　　要：背景面部压力性损伤是俯卧位通气患者常见并发症,创面局部暴露可增加全身感染风险,影响俯卧位通气治疗效果,甚至造成局部组织永久性功能损害。探讨其危险因素并构建预测模型对于预防俯卧位通气相关面部压力性损伤具有重要临床意义。目的探讨俯卧位通气相关面部压力性损伤的危险因素及其最佳建模方法。方法选择2020年6月—2023年3月入住新疆医科大学第一附属医院重症医学科的159例接受俯卧位通气的患者为研究对象,根据是否发生面部压力性损伤分为压力性损伤组(n=22)和非压力性损伤组(n=137),收集患者的一般信息、疾病诊断、治疗措施、实验室检查。分别使用逐步Logistic回归模型、全变量Logistic回归模型及Lasso-Logistic回归模型筛选面部压力性损伤危险因素并建立预测模型,应用受试者工作特征曲线下面积(AUC)评价模型区分度;应用赤池信息准则(AIC)、贝叶斯信息准则(BIC)及校准曲线评价模型校准度;应用决策曲线评价模型临床应用价值。通过比较三种Logistic回归模型预测效能和临床应用差异选择最佳建模方法。结果逐步Logistic回归模型结果显示,面部压力性损伤的影响因素为年龄(OR=39.041)、糖尿病(OR=7.256)和单次俯卧位通气时间(OR=6.705);全变量Logistic回归模型结果显示,面部压力性损伤的影响因素为年龄(OR=26.882)、糖尿病(OR=1.770)、ICU住院时间(OR=2.610)和单次俯卧位通气时间(OR=5.340);Lasso-Logistic回归结果显示,面部压力性损伤的影响因素为年龄(OR=38.256)、糖尿病(OR=1.094)、单次俯卧位通气时间(OR=5.738)和Richmond躁动镇静评分(OR=1.179)。Lasso-Logistic回归模型预测俯卧位通气相关面部压力性损伤的AUC、灵敏度和特异度分别为0.855、0.959和0.750,优于逐步和全变量Logistic回归模型;AIC和BIC分别为44.634和55.745,低于逐步和全变量Logistic回归模型;校准曲线显示Lasso-LogiBackground Facial pressure injury is a common complication in patients with prone position ventilation.Local exposure of the trauma can increase the risk of systemic infection,and affect the therapeutic effect of prone position ventilation,and even cause permanent functional damage to local tissues.Exploring the risk factors and constructing a prediction model are of great clinical significance for the prevention of prone position ventilation related facial pressure injuries.Objective To investigate the risk factors for prone position ventilation-related facial pressure injuries and its optimal modeling methods.Methods A total of 159 patients who were admitted to the Department of Critical Care Medicine of the First Affiliated Hospital of Xinjiang Medical University from June 2020 to March 2023 and received prone position ventilation were selected and divided into the pressure injury group(n=22)and non-pressure injury group(n=137)according to whether facial pressure injuries occurred or not.General information,disease diagnosis,therapeutic measures,and laboratory test results were collected.Stepwise Logistic regression,multivariate Logistic regression,and Lasso-Logistic regression were used to screen risk factors for facial pressure injuries and develop predictive models,respectively.The area under receiver operating characteristic curve(AUC)was plotted to evaluate the model discrimination.The Akaike Information Criterion(AIC),Bayesian Information Criterion(BIC),and calibration curve were applied to evaluate the calibration of the model.Decision curves were applied to evaluate the clinical application value of the models.The optimal modeling method was selected by comparing the predictive efficacy and clinical application differences of the three logistic regression models.Results The results of stepwise Logistic regression model showed that the influencing factors of facial pressure injuries were age(OR=39.041),diabetes mellitus(OR=7.256),and duration of a single-prone ventilation session(OR=6.705).The results o

关 键 词：俯卧位通气 压力性溃疡 面部损伤 危险因素 Lasso-Logistic回归模型 LOGISTIC模型 

分 类 号：R473.75[医药卫生—护理学] R632.1[医药卫生—临床医学]