Suppr超能文献

通过对应分析对饮食失调患者的亚型进行分类。

Classification of subtypes of patients with eating disorders by correspondence analysis.

作者信息

Martín Josune, Anton-Ladislao Ane, Padierna Ángel, Berjano Belén, Quintana José María

机构信息

Department of Research, Galdakao-Usansolo Hospital, Galdakao 48960, Spain.

Health Services Research on Chronic Diseases Network - REDISSEC, Galdakao 48960, Spain.

出版信息

World J Psychiatry. 2021 Jul 19;11(7):375-387. doi: 10.5498/wjp.v11.i7.375.

Abstract

BACKGROUND

Grouping eating disorders (ED) patients into subtypes could help improve the establishment of more effective diagnostic and treatment strategies.

AIM

To identify clinically meaningful subgroups among subjects with ED using multiple correspondence analysis (MCA).

METHODS

A prospective cohort study was conducted of all outpatients diagnosed for an ED at an Eating Disorders Outpatient Clinic to characterize groups of patients with ED into subtypes according to sociodemographic and psychosocial impairment data, and to validate the results using several illustrative variables. In all, 176 (72.13%) patients completed five questionnaires (clinical impairment assessment, eating attitudes test-12, ED-short form health-related quality of life, metacognitions questionnaire, Penn State Worry Questionnaire) and sociodemographic data. ED patient groups were defined using MCA and cluster analysis. Results were validated using key outcomes of subtypes of ED.

RESULTS

Four ED subgroups were identified based on the sociodemographic and psychosocial impairment data.

CONCLUSION

ED patients were differentiated into well-defined outcome groups according to specific clusters of compensating behaviours.

摘要

背景

将饮食失调(ED)患者分组为不同亚型有助于制定更有效的诊断和治疗策略。

目的

使用多重对应分析(MCA)在患有ED的受试者中识别具有临床意义的亚组。

方法

对一家饮食失调门诊诊所所有被诊断为ED的门诊患者进行前瞻性队列研究,根据社会人口统计学和心理社会损害数据将ED患者分组为不同亚型,并使用几个说明性变量验证结果。共有176名(72.13%)患者完成了五份问卷(临床损害评估、饮食态度测试-12、ED-简短形式健康相关生活质量、元认知问卷、宾夕法尼亚州立大学忧虑问卷)和社会人口统计学数据。使用MCA和聚类分析定义ED患者组。结果通过ED亚型的关键结果进行验证。

结果

根据社会人口统计学和心理社会损害数据确定了四个ED亚组。

结论

根据特定的代偿行为集群,ED患者被分为明确的结局组。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e339/8311511/2ef18c0e9448/WJP-11-375-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验