A network analysis of pain intensity and pain-related measures of physical, emotional, and social functioning in US military service members with chronic pain.

A network analysis of pain intensity and pain-related measures of physical, emotional, and social functioning in US military service members with chronic pain.
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对患有慢性疼痛的美国军人的疼痛强度和与疼痛相关的身体、情感和社会功能测量进行网络分析。

DOI:
10.1093/pm/pnad148
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发表时间:
2024
期刊:
Pain medicine (Malden, Mass.)
影响因子:
--
通讯作者:
Doorenbos,ArdithZ
Doorenbos,ArdithZ
中科院分区:
--
文献类型:
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作者:
Wi,Dahee;Park,Chang;Ransom,JeffreyC;Flynn,DianeM;Doorenbos,ArdithZ

文献摘要

相似文献

本研究的目的是应用网络分析方法,以更好地了解疼痛相关措施之间的关系与慢性pain.MethodsWe的人从一个横断面样本的4614名现役军人与慢性疼痛的数据进行了分析,涉及到1军事跨学科疼痛管理中心在2014年和2021年之间。使用患者报告的结果测量信息系统的措施和其他疼痛相关的措施相结合,我们应用的“EBICglasso”算法来创建正则化的偏相关网络,将确定最有影响力的measures.ResultsPain干扰,抑郁和焦虑在这些网络中的最高强度。疼痛灾难化在疼痛与其他疼痛相关健康指标之间的关联中发挥了重要作用。Bootstrap分析表明,网络是非常稳定的和边缘的权重准确估计在2个分析(有和没有疼痛catastrophizing)。重要的发现强调了疼痛干扰,抑郁和焦虑之间的关联强度,这表明如果要治疗疼痛,抑郁和焦虑也必须得到解决。特别重要的是疼痛灾难化在疼痛和其他症状之间的关系中的作用,这表明疼痛灾难化是治疗慢性疼痛的关键症状。
ObjectiveThe purpose of this study was to apply network analysis methodology to better understand the relationships between pain-related measures among people with chronic pain.MethodsWe analyzed data from a cross-sectional sample of 4614 active duty service members with chronic pain referred to 1 military interdisciplinary pain management center between 2014 and 2021. Using a combination of Patient-Reported Outcomes Measurement Information System measures and other pain-related measures, we applied the “EBICglasso” algorithm to create regularized partial correlation networks that would identify the most influential measures.ResultsPain interference, depression, and anxiety had the highest strength in these networks. Pain catastrophizing played an important role in the association between pain and other pain-related health measures. Bootstrap analyses showed that the networks were very stable and the edge weights accurately estimated in 2 analyses (with and without pain catastrophizing).ConclusionsOur findings offer new insights into the relationships between symptoms using network analysis. Important findings highlight the strength of association between pain interference, depression and anxiety, which suggests that if pain is to be treated depression and anxiety must also be addressed. What was of specific importance was the role that pain catastrophizing had in the relationship between pain and other symptoms suggesting that pain catastrophizing is a key symptom on which to focus for treatment of chronic pain.