重症儿童高密度脂蛋白动态发展轨迹与临床结局的相关性研究  

Correlation between dynamic high-density lipoprotein trajectories and clinical outcomes in critically ill children

作　　者：付建垒 张学鹏 张庚 熊怀钰 吉毅[2] 陈思源[1] Fu Jianlei;Zhang Xuepeng;Zhang Geng;Xiong Huaiyu;Ji Yi;Chen Siyuan(Department of Critical Care Medicine,West China Hospital,Sichuan University,Chengdu 610041,China;Department of Pediatric Surgery,West China Hospital,Sichuan University,Chengdu 610041,China)

机构地区：[1]四川大学华西医院重症医学科,成都610041 [2]四川大学华西医院小儿外科,成都610041

出　　处：《中华实用儿科临床杂志》2024年第3期161-169,共9页Chinese Journal of Applied Clinical Pediatrics

基　　金：国家自然科学基金(82273556);四川省科技厅重点研发项目(2022YFS0233,2022YFS0225,2022NSFSC1480);四川大学"从0到1"创新研究项目(2022SCUH0033);四川大学华西医院学科卓越发展1·3·5工程交叉学科创新项目(ZYJC21060);四川大学华西医院临床研究孵化项目(2019HXFH056,2020HXFH048,2023HXFH004)。

摘　　要：目的分析重症儿童高密度脂蛋白(HDL)纵向、动态发展轨迹特征,并探讨不同轨迹与临床结局之间的关系。方法回顾性队列研究。回顾性纳入2015年1月1日至2020年10月1日收住四川大学华西医院儿科重症监护室(PICU)的患儿,利用组基轨迹模型(GBTM)分析入PICU后0~6 d的HDL动态发展轨迹,并建立不同HDL轨迹组,住院死亡率使用例数(%)描述,采用χ^(2)检验或Fisher′s确切概率检验比较组间差异,PICU住院时间用M(Q1,Q3)描述,采用Kruskal Wallis统计检验比较组间差异。利用Logistic回归分析以及多元线性回归分析明确HDL轨迹组与临床结局之间的关系。主要临床结局指标为住院死亡率,次要结局指标为PICU住院时间。结果共纳入4384例重症儿童,GBTM分析产生了6个HDL轨迹组。1组(758例),最低水平HDL组;2组(1413例),较低水平HDL组;3组(74例),低-高水平HDL组;4组(621例),中等水平HDL组;5组(1371例),较高水平HDL组;6组(147例),最高水平HDL组。Logistic回归分析,以1组作为参照组,2~6组的住院死亡率明显降低,其比值比(OR)及95%可信区间(CI)分别为OR:0.475,95%CI:0.352~0.641,P<0.001;OR:0.093,95%CI:0.013~0.679,P=0.019;OR:0.322,95%CI:0.208~0.479,P<0.001;OR:0.263,95%CI:0.185~0.374,P<0.001;OR:0.142,95%CI:0.044~0.454,P=0.001。利用多元线性回归分析,以1组为参照组,4~6组PICU住院时间明显缩短,β值及95%CI分别为β:-4.332,95%CI:-5.238~-3.426,P<0.001;β:-3.053,95%CI:-3.809~-2.297,P<0.001;β:-6.281,95%CI:-7.842~-4.721,P<0.001。结论PICU 0~6 d内HDL动态变化轨迹与死亡率具有相关性:持续低水平轨迹与高死亡率相关,而HDL持续高水平及由低水平逐渐上升至高水平的HDL轨迹呈现较低的死亡率。提示HDL轨迹建模有望成为预测重症患儿病情以及预后的指标。Objective To characterize the longitudinal and dynamic high-density lipoprotein(HDL)trajectories in critically ill children and explore their correlation with clinical outcomes.Methods Retrospective cohort study.All critically ill children admitted to the Pediatric Intensive Care Unit(PICU)of West China Hospital,Sichuan University from January 1,2015 to October 1,2020 were included in this retrospective study.Group-based trajectory modeling(GBTM)was applied to characterize the HDL trajectories in days 0-6 post-PICU admission and develop HDL trajectory groups.The in-hospital mortality rate was reported as frequency(%)and then compared by the Chi-square test or Fisher′s exact test between HDL trajectory groups.The length of stay(LOS)in the PICU was described by M(Q1,Q3),and its difference between HDL trajectory groups was evaluated by the Kruskal Wallis test.Logistic regression and multiple linear regression were used to determine the correlation between HDL trajectories and clinical outcomes.The primary outcome was in-hospital mortality rate,and the secondary outcome was LOS in the PICU.Results A total of 4384 critically ill children were ultimately enrolled in the study,and 6 HDL trajectory groups were developed based on GBTM analyses:group 1(758 cases),the lowest HDL group;group 2(1413 cases),the low HDL group;group 3(74 cases),the low-to-high HDL group;group 4(621 cases),the medium HDL group;group 5(1371 cases),the high HDL group;and group 6(147 cases),the highest HDL group.Logistic regression analysis showed that compared with critically ill children in group 1,those belonging to groups 2,3,4,5,and 6 were at lower risks of in-hospital mortality with odds ratio(OR):0.475,95%confidence interval(CI):0.352-0.641,P<0.001;OR:0.093,95%CI:0.013-0.679,P=0.019;OR:0.322,95%CI:0.208-0.479,P<0.001;OR:0.263,95%CI:0.185-0.374,P<0.001,and OR:0.142,95%CI:0.044-0.454,P=0.001,respectively.Multiple linear regression analysis revealed that compared with critically ill children in group 1,those belonging to groups 4,5,and 6 had t

关 键 词：高密度脂蛋白 住院死亡率 儿科重症监护室 重症儿童 组基轨迹模型 

分 类 号：R720.597[医药卫生—急诊医学]