SPRINT: Signature for Pain Recovery IN Teens

SPRINT：青少年疼痛康复的标志

• 批准号: 10709409
• 负责人: LAURA E SIMONS
• 金额: $ 212.67万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10709409
• 关键词: SPRINT; Signature; Pain; Recovery; Teens

PROJECT SUMMARY Up to 5% of adolescents (~3.5 million in the US alone) suffer from high impact chronic musculoskeletal (MSK) pain, affecting all life domains and posing a significant economic burden. Current treatments for chronic MSK pain are suboptimal and have been tied to the opioid crisis. Only ~50% of adolescents with chronic MSK pain who present for multidisciplinary pain treatment recover, as measured by clinical endpoints of pain severity and functional disability. Discovery of robust markers of the recovery vs. persistence of pain and disability is essential to develop more resourceful and patient-specific treatment strategies and to conceive novel approaches that benefit patients who are refractory. Given that chronic pain is a biopsychosocial process, the discovery and validation of a prognostic and robust signature for pain recovery vs. persistence requires measurements across multiple dimensions in the same patient cohort in combination with a suitable ‘big data’ computational analysis pipeline for the extraction of reliable and cross-validated results from a multilayered and complex dataset. We are well positioned to execute the study aims with: (1) A highly skilled and experienced team of scientists and clinicians from Stanford University, University of Toronto/Hospital for Sick Children, and Cincinnati Children’s Hospital Medical Center; (2) A standardized specimen collection, processing, storage, and distribution system, leveraging Stanford Biobank’s platform, BioCatalyst, to aggregate the sample inventory with clinical annotations for an accessible, virtual biobank, within the Signature of Pain Recovery IN Teens (SPRINT) Biobank and Analysis Core (SBAC); (3) Cutting-edge preliminary data implicating novel candidates for neuroimaging, immune, quantitative sensory, and psychological markers for discovery; and (4) Expertise in machine learning approaches to extract reliable and prognostic bio-signatures from a large and complex data set. We expect that the results from this project will facilitate risk stratification in patients with chronic MSK, a more resourceful selection of patients who are likely to respond for undergoing current multidisciplinary pain treatment approaches, and new insight into biological and behavioral processes that may be exploited to develop novel strategies profiting those who are refractory. For the R61/Discovery Phase Aim individuals will be thoroughly characterized via biological (i.e. brain structure and function, immune, sensory profiles), psychological state, and clinical endpoint (i.e., pain intensity, disability) data. Unbiased machine learning algorithms will identify a multivariate model comprised of the most prognostic biological, psychological, and clinical endpoints. The model will classify adolescents with and without resolving chronic MSK pain after a state-of-the art multidisciplinary pain treatment intervention. R33/Validation Phase Aim will validate the biological signature derived in the R61 study. This signature will be useful for a range of adolescent-based clinical trials in which identification of the highest risk individuals is necessary, providing a clinically actionable intervention algorithm.

项目总结 高达5%的青少年(仅在美国就有约350万)患有高冲击性慢性肌肉骨骼(MSK) 痛苦，影响到生活的所有领域，并造成重大的经济负担。慢性MSK的现有治疗方法 疼痛是次优的，并与阿片类药物危机有关。只有约50%的青少年患有慢性MSK疼痛 参加多学科疼痛治疗的患者恢复情况，以疼痛严重程度的临床终点和 功能性残疾。发现恢复期与持续性疼痛和残疾的强健标志是 必须开发更多足智多谋和针对患者的治疗策略，并构思新的 使顽固性患者受益的治疗方法。鉴于慢性疼痛是一个生物、心理和社会过程， 发现和验证疼痛恢复与持续性的预测和可靠的签名需要 在同一患者队列中对多个维度进行测量，并结合合适的大数据 计算分析流水线，用于从多层和 复杂的数据集。我们具备以下条件来执行研究目标：(1)高度熟练和经验丰富的 来自斯坦福大学、多伦多大学/患病儿童医院的科学家和临床医生团队，以及 辛辛那提儿童医院医学中心；(2)标准化的标本收集、处理、存储和 分销系统，利用Stanford Biobank的平台Biocatalyst，将样本库存与 在青少年疼痛恢复签名内建立可访问的虚拟生物库的临床注释(SPRINT) 生物库和分析核心(SBAC)；(3)涉及新候选对象的尖端初步数据 神经成像、免疫、定量感觉和心理标记物的发现；和(4)专业知识 从大量复杂数据中提取可靠和可预测的生物签名的机器学习方法 准备好了。我们期望这个项目的结果将有助于慢性MSK患者的风险分层。 更足智多谋地选择可能对目前正在经历的多学科疼痛做出反应的患者 治疗方法，以及对可能被利用来开发的生物和行为过程的新见解 新的战略使那些顽固的人受益。对于R61/发现阶段，目标个人将是 通过生物学(即大脑结构和功能、免疫、感觉特征)、心理 状态和临床终点(即疼痛强度、残疾)数据。无偏机器学习算法将识别 一个由最具预后的生物学、心理学和临床终点组成的多变量模型。这个 模型将对患有和不缓解慢性MSK疼痛的青少年进行分类 多学科疼痛治疗干预。R33/验证阶段目标将验证生物签名 在R61研究中得出。此签名将对一系列基于青少年的临床试验有用，在这些试验中 识别最高风险个体是必要的，提供临床上可操作的干预算法。

项目成果

Signature for Pain Recovery IN Teens (SPRINT): protocol for a multisite prospective signature study in chronic musculoskeletal pain.

• DOI: 10.1136/bmjopen-2022-061548
• 发表时间: 2022-06-08
• 期刊: BMJ OPEN
• 影响因子: 2.9
• 作者: Simons, Laura;Moayedi, Massieh;Coghill, Robert C.;Stinson, Jennifer;Angst, Martin S.;Aghaeepour, Nima;Gaudilliere, Brice;King, Christopher D.;Lopez-Sola, Marina;Hoeppli, Marie-Eve;Biggs, Emma;Ganio, Ed;Williams, Sara E.;Goldschneider, Kenneth R.;Campbell, Fiona;Ruskin, Danielle;Krane, Elliot J.;Walker, Suellen;Rush, Gillian;Heirich, Marissa
• 通讯作者: Heirich, Marissa