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在糖尿病和抑郁症的常规临床数据中使用结构方程建模:观察性队列研究

Amy Ronaldson¹, Mark Freestone¹, Haoyuan Zhang²

¹Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.

|September 19, 2023

概括

结构方程建模 (SEM) 在分析2型糖尿病患者的常规临床数据方面表现有前途. 虽然SEM部分支持抑郁症和较差结果之间的已知关联,但注意到数据质量问题,影响了其充分的效用.

科学领域:

医疗保健服务研究医疗服务研究
生物统计学生物统计学
临床信息学临床信息学

背景情况:

常规临床数据越来越多地可用于健康研究.
结构方程建模 (SEM) 可以从复杂的临床变量中创建研究友好的结构.
对于大规模的常规临床数据,SEM提供了一种潜在的分析方法.

研究的目的:

将SEM应用于伦敦东部的常规临床数据,以建模2型糖尿病的已知关联.
调查抑郁症,糖尿病控制和医疗保健利用之间的关系.
通过建模已知的临床关联来测试 SEM 在常规临床数据中的实用性.

主要方法:

在东伦敦的一大批例行临床数据数据中使用了SEM.
该分析调查了抑郁症,糖尿病控制,糖尿病护理,心理健康治疗和事故和紧急情况 (A&E) 使用之间的关联.
隐性变量及其关联是基于现有的临床知识来定义的.

主要成果:

抑郁症与糖尿病控制的恶化和A&E使用的增加有关.
更糟糕的糖尿病控制意外地与较低的A&E使用有关.
心理健康治疗没有显著影响糖尿病控制,但接受糖尿病护理与更好的控制和其他积极结果有关.

结论:

SEM部分支持使用常规数据对2型糖尿病患者确定的临床关联进行建模.
该研究强调了可能影响SEM实用性的数据质量问题.
进一步改进数据可以加强SEM在常规临床数据集中的应用.

关键词:

这就是PLS-SEM.事故事故事故事故事故急性护理急性护理急性护理临床数据临床数据抑郁抑郁症抑郁症抑郁症是一种糖尿病糖尿病患者糖尿病患者电子健康记录是电子健康记录.紧急情况紧急情况紧急护理紧急护理紧急护理方程建模方程建模路径分析路径分析结构方程建模结构方程建模

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