社区高血压患者收缩压波动轨迹及其影响因素的研究  

Trajectory of systolic blood pressure fluctuation and its influencing factors in community-dwelling patients with hypertension

作　　者：聂朦 邬娜[2] 焦惠艳 袁志权 李成英 吴龙[2] 许月瑶 杨蕾 王煜 伍永红 钟理 李亚斐[2] 杨敬源[1] NIE Meng;WU Na;JIAO Huiyan;YUAN Zhiquan;LI Chengying;WU Long;XU Yueyao;YANG Lei;WANG Yu;WU Yonghong;ZHONG Li;LI Yafei;YANG Jingyuan(School of Public Health,Key Laboratory of Environmental Pollution Monitoring and Disease Control of Ministry of Education,Guizhou Medical University,Guiyang,Guizhou Province,561113;Department of Epidemiology,Faculty of Military Preventive Medicine,Army Medical University(Third Military Medical University),Chongqing,400038;Shuangbei Community Health Service Center of Shapingba District,Chongqing,400032;Jiulongpo District Center for Disease Control and Prevention,Chongqing,400039;Cardiovascular Disease Center,the Third Affiliated Hospital of Chongqing Medical University,Chongqing,401120,China)

机构地区：[1]贵州医科大学公共卫生与健康学院环境污染与疾病监控教育部重点实验室,贵阳561113 [2]陆军军医大学(第三军医大学)军事预防医学系军队流行病学教研室,重庆400038 [3]沙坪坝区双碑社区卫生服务中心,重庆400032 [4]九龙坡区疾病预防控制中心,重庆400039 [5]重庆医科大学附属第三医院心血管疾病中心,重庆401120

出　　处：《陆军军医大学学报》2024年第12期1457-1466,F0003,共11页Journal of Army Medical University

基　　金：国家自然科学基金(82073649)。

摘　　要：目的分析和构建社区高血压人群收缩压(systolic blood pressure,SBP)变化的轨迹模型,并分析不同SBP轨迹的影响因素。方法本研究基于社区回顾性队列,运用潜类别轨迹模型(latent class trajectory modelling,LCTM)分析社区高血压人群SBP的变化模式,识别、构建SBP的纵向变化轨迹;运用无序多分类logistic回归分析不同SBP轨迹的影响因素,根据先验知识使用“有向无环图”识别和调整不同的混杂因素。结果共793名高血压患者被纳入分析,LCTM拟合的社区高血压患者SBP轨迹最优分组为3组,分别为低水平平稳组(n=561,70.74%)、下降组(n=170,21.44%)和上升组(n=62,7.82%);年龄、锻炼频率、随访方式、摄盐情况、遵医行为、有无转诊在不同SBP轨迹亚组中分布存在统计学差异(P<0.05);无序多分类logistic回归分析结果显示,以低水平平稳组为对照,“男性”、“门诊随访”的患者被分类到下降组的可能性较高,OR及95%CI分别为1.436(1.016~2.030)、1.702(1.202~2.410);而“年龄≥65岁”,“不锻炼或偶尔锻炼”,摄盐情况为“中”和“重”度的人群,被分类到上升组的可能性更高,OR及95%CI依次为1.949(1.145~3.317)、2.284(1.305~3.998)、2.433(1.272~4.654)、4.540(1.291~15.963)。结论社区高血压人群收缩压变化轨迹可分为3组,即“低水平平稳组”、“下降组”和“上升组”;性别、年龄、摄盐情况、锻炼频率、随访方式可能是收缩压轨迹的影响因素。Objective To analyze and construct systolic blood pressure(SBP)fluctuation trajectory in a community population with hypertension and to analyze the factors influencing different trajectories.Methods This is a community-based retrospective cohort study.A latent class trajectory model was used to identify and construct longitudinal trajectories of blood pressure change.Multinomial logistic regression analysis was performed to identify the associated factors of blood pressure trajectories by adjusting for different confounders.Potential confounding factors were identified using a directed acyclic graph based on a priori knowledge.Results A total of 793 patients with hypertension were enrolled in the analysis.They were divided into 3 groups by LCTM-fitted systolic blood pressure trajectories,namely stable low-level group(n=561,70.74%),declining group(n=170,21.44%)and rising group(n=62,7.82%).Significant differences were observed among the 3 trajectories groups in terms of age,frequency of exercise,ways of follow-up,salt intake,compliance behavior,and referral(P<0.05).Compared to the stable low-level group and adjusting for corresponding confounding factors,the male patients and the patients with“outpatient follow-up”were more likely to be classified into“declining group”,with OR and 95%CI of 1.436(1.016~2.030)and 1.702(1.202~2.410),respectively.The participants aged≥65 years,who did not exercise or occasionally exercised,and had moderate and severe salt intake,were more likely to be classified into the“rising group”(OR=1.949,2.284,2.433,4.540,95%CI:1.145~3.317,1.305~3.998,1.272~4.654,1.291~15.963).Conclusion SBP trajectories in community-dwelling hypertensive population can be divided into stable low-level,declining and rising groups.Gender,age,salt intake,exercise frequency,and follow-up methods may be influencing factors for SBP blood pressure trajectory.

关 键 词：社区人群 高血压 潜类别轨迹模型 收缩压轨迹 影响因素 

分 类 号：R181.22[医药卫生—流行病学] R544.1[医药卫生—公共卫生与预防医学]