Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
Centre for Multimodal Sensorimotor and Pain Research, University of Toronto Faculty of Dentistry, Toronto, Ontario, Canada.
BMJ Open. 2022 Jun 8;12(6):e061548. doi: 10.1136/bmjopen-2022-061548.
Current treatments for chronic musculoskeletal (MSK) pain are suboptimal. Discovery of robust prognostic markers separating patients who recover from patients with persistent pain and disability is critical for developing patient-specific treatment strategies and conceiving novel approaches that benefit all patients. Given that chronic pain is a biopsychosocial process, this study aims to discover and validate a robust prognostic signature that measures across multiple dimensions in the same adolescent patient cohort with a computational analysis pipeline. This will facilitate risk stratification in adolescent patients with chronic MSK pain and more resourceful allocation of patients to costly and potentially burdensome multidisciplinary pain treatment approaches.
Here we describe a multi-institutional effort to collect, curate and analyse a high dimensional data set including epidemiological, psychometric, quantitative sensory, brain imaging and biological information collected over the course of 12 months. The aim of this effort is to derive a multivariate model with strong prognostic power regarding the clinical course of adolescent MSK pain and function.
The study complies with the National Institutes of Health policy on the use of a single internal review board (sIRB) for multisite research, with Cincinnati Children's Hospital Medical Center Review Board as the reviewing IRB. Stanford's IRB is a relying IRB within the sIRB. As foreign institutions, the University of Toronto and The Hospital for Sick Children (SickKids) are overseen by their respective ethics boards. All participants provide signed informed consent. We are committed to open-access publication, so that patients, clinicians and scientists have access to the study data and the signature(s) derived. After findings are published, we will upload a limited data set for sharing with other investigators on applicable repositories.
NCT04285112.
目前治疗慢性肌肉骨骼（MSK）疼痛的方法并不理想。发现能够将康复患者与持续疼痛和残疾患者区分开来的强大预后标志物对于制定针对患者的治疗策略和构思使所有患者受益的新方法至关重要。鉴于慢性疼痛是一个生物心理社会过程，本研究旨在通过计算分析管道在具有相同青少年患者队列中发现和验证一种能够跨多个维度测量的强大预后标志物。这将有助于对患有慢性 MSK 疼痛的青少年患者进行风险分层，并更有效地将患者分配到昂贵且可能负担沉重的多学科疼痛治疗方法中。
在这里，我们描述了一项多机构努力，旨在收集、管理和分析一个高维数据集，其中包括在 12 个月的过程中收集的流行病学、心理测量、定量感觉、脑成像和生物学信息。这项工作的目的是得出一个具有很强预后能力的多变量模型，该模型与青少年 MSK 疼痛和功能的临床过程有关。
该研究符合美国国立卫生研究院关于使用单一内部审查委员会（sIRB）进行多地点研究的政策，辛辛那提儿童医院医疗中心审查委员会为审查 IRB。斯坦福大学的 IRB 是 sIRB 中的依赖 IRB。作为外国机构，多伦多大学和 SickKids（ SickKids ）由各自的伦理委员会监督。所有参与者均提供了签署的知情同意书。我们致力于开放获取出版，以便患者、临床医生和科学家能够访问研究数据和衍生的签名。研究结果发表后，我们将上传一个有限的数据集，以供在适用的存储库中与其他研究人员共享。
NCT04285112。
