医学
焦虑
剧痛
慢性疼痛
前瞻性队列研究
物理疗法
萧条(经济学)
逻辑回归
物理医学与康复
队列
心理学
内科学
精神科
宏观经济学
经济
作者
Luke C. Jenkins,Wei‐Ju Chang,Valentina Buscemi,Matthew Liston,Peter Humburg,Michael K. Nicholas,Thomas Graven‐Nielsen,Paul W. Hodges,James H. McAuley,Siobhan M. Schabrun
出处
期刊:Pain
[Ovid Technologies (Wolters Kluwer)]
日期:2022-05-13
卷期号:164 (1): 14-26
被引量:18
标识
DOI:10.1097/j.pain.0000000000002684
摘要
Abstract Predicting the development of chronic low back pain (LBP) at the time of an acute episode remains challenging. The Understanding persistent Pain Where it ResiDes study aimed to identify neurobiological and psychological risk factors for chronic LBP. Individuals with acute LBP (N = 120) participated in a prospective cohort study with 6-month follow-up. Candidate predictors were selected from the neurobiological (eg, sensorimotor cortical excitability assessed by sensory and motor-evoked potentials and brain-derived neurotrophic factor genotype), psychological (eg, depression and anxiety), symptom-related (eg, LBP history), and demographic domains. Analyses involved multivariable linear regression models with pain intensity or disability degree as continuous variables. Secondary analyses involved a multivariable logistic model with the presence of LBP at 6 months (thresholding pain intensity and disability degree) as a dichotomous variable. Lower sensory cortex and corticomotor excitability, higher baseline pain intensity, higher depression, stress, and pain catastrophizing were the strongest predictors ( R 2 = 0.47) of pain intensity at 6 months. Older age and higher pain catastrophizing were the strongest predictors ( R 2 = 0.30) of disability at 6 months. When the LBP outcome was dichotomised, sensory cortex and corticomotor excitability, brain-derived neurotrophic factor genotype, depression and anxiety, LBP history and baseline pain intensity, discriminated between those who did and did not report LBP at 6 months (C-statistic 0.91). This study identifies novel risk factors for the development of future LBP. Neurobiological risk factors, when added to a multivariable linear regression model, explained a further 15% of the variance in the 6-month pain intensity.
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