Factors predicting the transition from acute to persistent pain in people with 'sciatica': the FORECAST longitudinal prognostic factor cohort study protocol.
Factors predicting the transition from acute to persistent pain in people with 'sciatica': the FORECAST longitudinal prognostic factor cohort study protocol.
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DOI：
10.1136/bmjopen-2023-072832
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发表时间：
2023-04-05
期刊：
影响因子：
2.9
通讯作者：
Baskozos, Georgios
中科院分区：
文献类型：
--
作者：
Schmid, Annina B.;Ridgway, Lucy;Hailey, Louise;Tachrount, Mohamed;Probert, Fay;Martin, Kathryn R.;Scott, Whitney;Crombez, Geert;Price, Christine;Robinson, Claire;Koushesh, Soraya;Ather, Sarim;Tampin, Brigitte;Barbero, Marco;Nanz, Daniel;Clare, Stuart;Fairbank, Jeremy;Baskozos, Georgios
坐骨神经痛是一种常见的疾病，与单纯的腰痛相比，坐骨神经痛与更高程度的疼痛、残疾、更差的生活质量和更多的卫生资源使用有关。虽然许多患者康复，但三分之一的患者会出现持续性坐骨神经痛症状。目前尚不清楚，为什么有些患者会出现持续性坐骨神经痛，因为传统上认为临床参数（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”患者从急性疼痛转变为持续性疼痛的因素）将采取不同的方法，探索坐骨神经痛患者基于机制的亚组，并研究基于机制的方法是否可以识别预测坐骨神经痛患者疼痛持续性的因素。我们将进行一项前瞻性纵向队列研究，包括180例急性/亚急性坐骨神经痛患者。N=168例健康受试者将提供规范性数据。将在坐骨神经痛发作后3个月内评估一组详细的变量。这将包括自我报告的感觉和心理社会概况，定量感觉测试，血液炎症标志物和先进的神经成像。我们将在3个月和12个月时使用坐骨神经痛困扰指数和数字疼痛评定量表确定腿部疼痛严重程度的结局。我们将使用主成分分析，然后聚类方法来确定亚组。针对高维小数据集优化的单变量关联和机器学习方法将用于识别最强大的预测因子和模型选择/准确性。结果将提供有关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性的预后因素。FORECAST研究已获得伦理批准（South Central Oxford C，18/SC/0263）。传播战略将以我们的患者和公众参与活动为指导，包括同行评议的出版物、会议演示、社交媒体和播客。ISRCTN 18170726;预结果。
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.
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