摘要
目的:基于logistics回归,应用儿童早期预警评分及无创超声心输出量监测仪血流动力学参数建立预测重症流感儿童发生危重症的诊断预测模型,分析其应用价值.方法:收集2019年1月至2023年3月于成都市第二人民医院、四川大学华西第二医院、成都市青白江医院确诊为重症和(或)危重症流感的284例29 d至4.9岁的患儿临床资料,按照《流行性感冒诊疗方案(2019年版)》病情分度标准将其分为重症组和危重症组,完成受试者入院后儿童早期预警评分(PEWS)、血气乳酸以及无创超声心输出量监测仪各模块参数值,行相关性分析及多因素logistics回归分析.结果:重症组、危重症组两组间PEWS、血气乳酸、每搏输出量(SV)、心输出量(CO)、心脏指数(CI)、外周血管阻力(SVR)及外周血管阻力指数(SVRI)测量值有明显差异,差异均具有统计学意义(t=29.581、12.462、9.595、6.000、2.872、120.664、9.967,P＜0.05);重症和危重症流感患儿PEWS评分与SVR值呈正相关,与SV、CO值呈负相关关系(r=0.330、-0.217、-0.192,P＜0.05);重症和危重症流感患儿血气乳酸水平与SVR呈正相关,与无创心功能SV、CO值呈负相关(r=0.406、-0.318、-0.324,P＜0.05);应用无创超声心输出量检测仪监测参数和血气乳酸建立的诊断模型诊断危重症流感的受试者工作特征(ROC)曲线下面积(AUC)为0.854(95%CI:0.823～0.886),对此模型进行似然比检验赤池信息准则(AIC)为43.13,拟合效果较好、预测危重症流感肺炎效果较高,差异有统计学意义(x2=35.8,P＜0.05).结论:PEWS评分结合血气乳酸水平、无创超声心输出量监测技术,可有效评估重症和(或)危重症流感患儿的病情严重程度,指导临床制定和优化重症和(或)危重症流感肺炎的治疗方案.
Abstract
Objective:Based on Logistics regression,a diagnosis and prediction model for severe influenza of children was established by applying pediatric early warning score(PEWS)and hemodynamic parameters of non-invasive ultrasonic cardiac output monitor,and to analyze the application value of that.Methods:Clinical data of 284 pediatric patients aged from 29 days to 4.9 years who were diagnosed as severe and(or)critical influenza in Chengdu Second People's Hospital,West China Second University Hospital of Sichuan University and Chengdu Qingbaijiang District People's Hospital from January 2019 to March 2023 were collected.They were divided into severe group and critical group according to the criteria of disease classification of<Influenza Diagnosis and Treatment Protocol(2019 edition)>.The correlation analysis and multi-factor Logistics regression analysis were performed after the PEWS,blood gas lactic acid and the parameter values of each module of the monitor for non-invasive ultrasonic cardiac output(CO)were completed.Results:There were significant differences in the measured values of PEWS,lactic acid,stroke output(SV),CO,cardiac index(CI),peripheral vascular resistance(SVR)and peripheral vascular resistance index(SVRI)between the severe group and the critical group,and the differences were statistically significant(t=29.581,12.462,9.595,6.000,2.872,120.664,9.967,P<0.05),respectively.The PEWS scores of pediatric patients with severe and critical influenza was positively correlated with SVR value,and was negatively correlated with SV value and CO value(r=0.330,-0.217,-0.192,P<0.05),respectively.The serum lactic acid level of pediatric patients with severe and critical influenza was positively correlated with SVR,and was negatively correlated with SV value and CO value of non-invasive heart function(r=0.406,-0.318,-0.324,P<0.05),respectively.The area under curve(AUC)value of receiver operating characteristic(ROC)curve of the diagnostic model that was established by monitoring parameters of the monitor for non-invasive ultrasonic CO and serum lactic acid was 0.854(95%CI:0.823-0.886),and the likelihood ratio test of this model indicated the Akaike information criterion(AIC)was 43.13(x2=35.8,P<0.05),which showed that the model had a better fitting effect and a higher predictive effect for severe and critical influenza.Conclusion:The monitoring technique that PEWS score combines with blood gas lactic acid level and non-invasive ultrasonic CO can effectively assess the severity of pediatric patients with severe and(or)critical influenza,which can guide the clinical practice to formulate and optimize the treatment plan for severe and(or)critical influenza pneumonia.
基金项目
四川省科技厅重点研发项目(20ZDYF3165)
成都市科技局技术创新研发项目(2019-YF05-00140-SN)
成都市卫生健康委医学科研项目(2020113)
国家科技期刊平台
NSTL
万方数据