Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, Oxfordshire, UK.
BMJ Open. 2023 Apr 5;13(4):e072832. doi: 10.1136/bmjopen-2023-072832.
Sciatica is a common condition and is associated with higher levels of pain, disability, poorer quality of life, and increased use of health resources compared with low back pain alone. Although many patients recover, a third develop persistent sciatica symptoms. It remains unclear, why some patients develop persistent sciatica as none of the traditionally considered clinical parameters (eg, symptom severity, routine MRI) are consistent prognostic factors.The FORECAST study (factors predicting the transition from acute to persistent pain in people with 'sciatica') will take a different approach by exploring mechanism-based subgroups in patients with sciatica and investigate whether a mechanism-based approach can identify factors that predict pain persistence in patients with sciatica.
We will perform a prospective longitudinal cohort study including 180 people with acute/subacute sciatica. N=168 healthy participants will provide normative data. A detailed set of variables will be assessed within 3 months after sciatica onset. This will include self-reported sensory and psychosocial profiles, quantitative sensory testing, blood inflammatory markers and advanced neuroimaging. We will determine outcome with the Sciatica Bothersomeness Index and a Numerical Pain Rating Scale for leg pain severity at 3 and 12 months.We will use principal component analysis followed by clustering methods to identify subgroups. Univariate associations and machine learning methods optimised for high dimensional small data sets will be used to identify the most powerful predictors and model selection/accuracy.The results will provide crucial information about the pathophysiological drivers of sciatica symptoms and may identify prognostic factors of pain persistence.
The FORECAST study has received ethical approval (South Central Oxford C, 18/SC/0263). The dissemination strategy will be guided by our patient and public engagement activities and will include peer-reviewed publications, conference presentations, social media and podcasts.
ISRCTN18170726; Pre-results.
与单纯腰痛相比,坐骨神经痛是一种常见的病症,与更高水平的疼痛、残疾、较差的生活质量和更多地使用卫生资源相关。尽管许多患者会康复,但三分之一的患者会出现持续的坐骨神经痛症状。目前尚不清楚为什么一些患者会出现持续的坐骨神经痛,因为传统上认为的临床参数(例如症状严重程度、常规 MRI)都不是一致的预后因素。FORECAST 研究(预测坐骨神经痛患者从急性疼痛向持续性疼痛转变的因素)将采用一种不同的方法,即探索坐骨神经痛患者的基于机制的亚组,并研究基于机制的方法是否可以确定可预测坐骨神经痛患者疼痛持续存在的因素。
我们将进行一项前瞻性纵向队列研究,纳入 180 名急性/亚急性坐骨神经痛患者。N=168 名健康参与者将提供正常数据。在坐骨神经痛发作后 3 个月内,将评估一系列详细的变量。这将包括自我报告的感觉和心理社会特征、定量感觉测试、血液炎症标志物和高级神经影像学。我们将使用坐骨神经痛困扰指数和腿部疼痛严重程度的数字疼痛评分量表,在 3 个月和 12 个月时确定结局。我们将使用主成分分析,然后是聚类方法来识别亚组。将使用单变量关联和针对高维小数据集优化的机器学习方法来识别最有力的预测因素和模型选择/准确性。研究结果将提供关于坐骨神经痛症状的病理生理驱动因素的重要信息,并可能确定疼痛持续存在的预后因素。
FORECAST 研究已获得伦理批准(南中部牛津 C,18/SC/0263)。传播策略将由我们的患者和公众参与活动指导,并包括同行评议的出版物、会议演讲、社交媒体和播客。
ISRCTN84622554。预结果。