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

被引:0
|
作者
Stoecklein, Veit M. [1 ]
Grosu, Sergio [2 ]
Nikolova, Trayana [2 ]
Tonn, Joerg-Christian [1 ]
Zausinger, Stefan [1 ]
Ricke, Jens [2 ]
Schlett, Christopher L. [3 ]
Maurer, Elke [4 ]
Walter, Sven S. [5 ]
Peters, Annette [6 ,7 ,8 ,9 ]
Bamberg, Fabian [3 ]
Rospleszcz, Susanne [7 ,8 ]
Stoecklein, Sophia [2 ]
机构
[1] Ludwig Maximilians Univ Munchen, Univ Hosp, Dept Neurosurg, Marchioninistr 15, D-81377 Munich, Germany
[2] Ludwig Maximilians Univ Munchen, Univ Hosp, Dept Radiol, Munich, Germany
[3] Univ Freiburg, Univ Med Ctr Freiburg, Fac Med, Dept Diagnost & Intervent Radiol, Freiburg, Germany
[4] Eberhard Karls Univ Tuebingen, Dept Trauma & Reconstruct Surg, BG Unfallklin Tuebingen, Tubingen, Germany
[5] Eberhard Karls Univ Tuebingen, Univ Hosp Tuebingen, Dept Diagnost & Intervent Radiol, Tubingen, Germany
[6] Helmholtz Zentrum Munchen, Inst Epidemiol, German Res Ctr Environm Hlth, Neuherberg, Germany
[7] Ludwig Maximilians Univ Munchen, Inst Med Informat Proc Biometry & Epidemiol, Med Fac, Chair Epidemiol, Munich, Germany
[8] German Ctr Cardiovasc Res DZHK, Partner Site Munich Heart Alliance, Munich, Germany
[9] German Ctr Diabet Res DZD, Partner Site Neuherberg, Neuherberg, Germany
来源
JOURNAL OF PAIN | 2024年 / 25卷 / 02期
关键词
Back pain; cohort study; machine learning; spinal MRI; SAGITTAL ALIGNMENT; RISK-FACTORS; POPULATION; DISORDERS; PELVIS; COMORBIDITY; OBESITY; KORA;
D O I
10.1016/j.jpain.2023.09.009
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
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.
引用
收藏
页码:497 / 507
页数:11
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