Value of Sleep Metrics in Predicting Opioid-Use Disorder Treatment Outcomes: Leadership and Data Coordinating Center

阿片使用障碍(OUD)是一个主要的公共卫生问题，影响着超过270万人，并导致 2020年，美国有超过9万人死亡，并广泛影响到 患有自闭症的个人以及受影响个人的社区和家庭。治疗 OUD的有效性取决于护理中的留存。不幸的是，一项系统的审查报告说 19个月中阿片使用障碍(Moud)计划的药物保留率中位数为6个月 只有58%的人在学习。睡眠障碍已被确定为治疗磨耗的预测因子，并 通过涉及疼痛、压力和情绪失调的双向通路与OUD相关，并可能 作为未来的干预目标，以改善OUD的结果。然而，人们对此的了解有限。 睡眠和昼夜节律的特殊测量在早期康复期间的预测价值，几乎没有 有关睡眠和昼夜节律参数如何与其他风险因素相互作用影响Moud结局的数据。我们有 组建了一个由睡眠科学家、成瘾药物专家、生物统计学家和临床试验专家组成的团队，并 将利用布里格姆和妇女医院睡眠医学项目的特殊资源 流行病学、睡眠阅读中心和哈佛朝圣者卫生保健研究所生物统计部 领导领导和数据协调中心(LDCC)进行这项多站点研究。LDCC将制定一项 四项研究中用于预测Moud结果的标准化数据收集的通用协议 中心，并将领导、协调和实施这一共同协议的所有方面，提供 全面、快速、创新的数据和项目管理。它将为招聘和数据提供便利 分享和支持对全面睡眠测量的严格收集和分析。除了……之外 由现有预测模型建议的风险因素和治愈倡议的公共数据元素，睡眠将是 由脑电--神经生理学和精神疾病的生物标记物--进行评估，并将在两个 与参加Moud计划的400名患者相隔大约一个月的夜晚。休眠宏和 微架构将使用集中式睡眠评分和高级脑电定量信号来派生 分析。睡眠呼吸紊乱、周期性肢体运动、睡眠-觉醒模式和昼夜节律危险因素 将通过多导睡眠图、有效问卷和多天Fitbit跟踪器进行测量。初级阶段 终点将是研究登记后6个月的治疗保留。次要结果将包括时间 对治疗退出、非法药物使用和非医用阿片类药物使用；阿片类药物渴求；戒断症状； 酒精使用；疼痛强度和干扰；身体功能；睡眠障碍和质量测量在6- 月份。我们将采用集成算法(超级学习器)来开发预测模型，以找到 统计和机器学习算法集合的最佳组合。这一基于数据科学的计划 我们的多学科专业知识将确保实现这项研究的目标。

Opioid-use disorder (OUD) is a major public health problem, affecting over 2.7 million people and resulting in over 90,000 deaths in 2020 in the U.S., as well as broadly impacting the mental and physical health of individuals suffering from an OUD and the communities and families of affected individuals. Treatment effectiveness for OUD depends on retention in care. Unfortunately, a systematic review reported that the median retention rate at 6 months for Medications for Opioid Use Disorder (MOUD) programs across 19 studies was only 58%. Sleep disturbances have been identified as predictors of treatment attrition and are related to OUD through bi-directional pathways involving pain, stress, and emotional dysregulation, and could serve as future intervention targets to improve OUD outcomes. However, there is a limited understanding of the predictive value of specific measures of sleep and circadian rhythm during early recovery, and almost no data on how sleep and circadian parameters interact with other risk factors for MOUD outcomes. We have assembled a team of sleep scientists, addiction medicine specialists, biostatisticians, and clinical trialists and will leverage the exceptional resources of Brigham and Women's Hospital's Program in Sleep Medicine Epidemiology, Sleep Reading Center, and Division of Biostatistics at Harvard Pilgrim Health Care Institute to lead the Leadership and Data Coordinating Center (LDCC) for this multi-site study. The LDCC will develop a Common Protocol for the collection of standardized data for predicting MOUD outcomes across four Research Centers and will lead, coordinate, and implement all aspects of this common protocol, providing comprehensive, responsive, and innovative data and project management. It will facilitate recruitment and data sharing and support the rigorous collection and analyses of comprehensive sleep measurements. In addition to risk factors suggested by existing prediction models and HEAL initiative common data elements, sleep will be assessed by EEG -- a biomarker of neurophysiology and psychiatric diseases -- and will be measured on two nights approximately one month apart from 400 patients enrolled in a MOUD program. Sleep macro- and micro-architecture will be derived using centralized sleep scoring and advanced EEG quantitative signal analysis. Sleep-disordered breathing, periodic limb movements, sleep-wake patterns and circadian risk factors will be measured by polysomnography, validated questionnaires and multiple day Fitbit trackers. The primary endpoint will be treatment retention at 6 months after study enrollment. Secondary outcomes will include time to treatment drop out, and illicit drug use and non-medical opioid use; opioid craving; withdrawal symptoms; alcohol use; pain intensity and interference; physical functioning; sleep disturbance and quality measured at 6- month. We will adopt an ensemble algorithm (the Super Learner) to develop the prediction model that finds the optimal combination of a collection of statistical and machine learning algorithms. This data-science based plan and our multi-disciplinary expertise will ensure that the goals of this study are met.