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抑郁和焦虑与背痛的存在密切相关,而脊柱影像学检查结果的影响有限:一项 MRI 队列研究分析。

Strong Association of Depression and Anxiety With the Presence of Back Pain While Impact of Spinal Imaging Findings is Limited: Analysis of an MRI Cohort Study.

机构信息

Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany.

Department of Radiology, University Hospital, LMU Munich, Munich, Germany.

出版信息

J Pain. 2024 Feb;25(2):497-507. doi: 10.1016/j.jpain.2023.09.009. Epub 2023 Sep 22.

Abstract

Development of back pain is multifactorial, and it is not well understood which factors are the main drivers of the disease. We therefore applied a machine-learning approach to an existing large cohort study data set and sought to identify and rank the most important contributors to the presence of back pain amongst the documented parameters of the cohort. Data from 399 participants in the KORA-MRI (Cooperative health research in the region Augsburg-magnetic resonance imaging) (Cooperative Health Research in the Region Augsburg) study was analyzed. The data set included MRI images of the whole body, including the spine, metabolic, sociodemographic, anthropometric, and cardiovascular data. The presence of back pain was one of the documented items in this data set. Applying a machine-learning approach to this preexisting data set, we sought to identify the variables that were most strongly associated with back pain. Mediation analysis was performed to evaluate the underlying mechanisms of the identified associations. We found that depression and anxiety were the 2 most selected predictors for back pain in our model. Additionally, body mass index, spinal canal width and disc generation, medium and heavy physical work as well as cardiovascular factors were among the top 10 most selected predictors. Using mediation analysis, we found that the effects of anxiety and depression on the presence of back pain were mainly direct effects that were not mediated by spinal imaging. In summary, we found that psychological factors were the most important predictors of back pain in our cohort. This supports the notion that back pain should be treated in a personalized multidimensional framework. PERSPECTIVE: This article presents a wholistic approach to the problem of back pain. We found that depression and anxiety were the top predictors of back pain in our cohort. This strengthens the case for a multidimensional treatment approach to back pain, possibly with a special emphasis on psychological factors.

摘要

背痛的发生是多因素的,目前尚不清楚哪些因素是导致该疾病的主要驱动因素。因此,我们应用机器学习方法对现有的大型队列研究数据集进行分析,旨在确定并对队列记录参数中导致背痛的最重要因素进行排序。该分析使用了 KORA-MRI(奥格斯堡磁共振成像合作健康研究)研究中的 399 名参与者的数据。该数据集包括全身的 MRI 图像,包括脊柱、代谢、社会人口统计学、人体测量学和心血管数据。背痛的存在是该数据集中记录的项目之一。我们应用机器学习方法对这个预先存在的数据进行分析,旨在确定与背痛最密切相关的变量。进行中介分析以评估所确定关联的潜在机制。我们发现,抑郁和焦虑是我们模型中与背痛最相关的两个最重要的预测因素。此外,身体质量指数、椎管宽度和椎间盘生成、中重度体力劳动以及心血管因素也位列前 10 个最重要的预测因素。通过中介分析,我们发现焦虑和抑郁对背痛存在的影响主要是直接影响,而不是通过脊柱影像学来介导。总之,我们发现心理因素是我们队列中背痛的最重要预测因素。这支持了背痛应在个性化多维框架中进行治疗的观点。观点:本文提出了一种整体方法来解决背痛问题。我们发现,抑郁和焦虑是我们队列中背痛的首要预测因素。这进一步支持了背痛多维治疗方法的观点,可能特别强调心理因素。

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