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

睡眠指标在预测阿片类药物使用障碍治疗结果中的价值：领导力和数据协调中心

• 批准号: 10783610
• 负责人: Shaun M Purcell
• 金额: $ 64.01万
• 依托单位: HARVARD PILGRIM HEALTH CARE, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10783610
• 关键词: Adherence; Adopted; Adverse event; Affect; Alcohol consumption; Algorithms; Architecture; Area; Biological Markers; Biometry; Caring; Case Report Form; Certification; Cessation of life; Circadian Rhythms; Clinical; Clinical Research; Collaborations; Collection; Common Data Element; Communication; Communities; Data; Data Analyses; Data Collection; Data Coordinating Center; Data Files; Data Management Resources; Data Science; Data Set; Detection; Development; Documentation; Drops; Electroencephalography; Electronics; Eligibility Determination; Enrollment; Ensure; Epidemiologist; Epidemiology; Family; Future; Generations; Goals; Healthcare; Helping to End Addiction Long-term; Hospitals; Individual; Institutional Review Boards; Intervention; Laboratories; Lead; Leadership; Manuals; Manuscripts; Measures; Medicine; Mental Health; Mental disorders; Metadata; Mission; Monitor; Multicenter Studies; Online Systems; Opiate Addiction; Opioid; Outcome; Pain; Pain intensity; Pain interference; Pathway interactions; Patient Recruitments; Pattern; Periodicals; Personal Satisfaction; Persons; Phase; Phenotype; Physical Function; Physical activity; Polysomnography; Prediction of Response to Therapy; Predictive Value; Preparation; Procedures; Protocols documentation; Public Health; Quality Control; Questionnaires; Reading; Recovery; Report (document); Reporting; Research; Research Training; Resolution; Resources; Risk Factors; Safety; Scientist; Secure; Services; Signal Transduction; Site; Sleep; Sleep Apnea Syndromes; Sleep Architecture; Sleep disturbances; Specialist; Standardization; Statistical Data Interpretation; Stress; Summary Reports; Support System; System; Testing; Time; Training; Treatment Effectiveness; Treatment outcome; United States National Institutes of Health; Validation; Withdrawal Symptom; Woman; Work; Writing; addiction; circadian; craving; data ecosystem; data integration; data management; data quality; data sharing; data standards; design; distributed data; emotion dysregulation; experience; fitbit; illicit drug use; improved; innovation; limb movement; machine learning algorithm; medication for opioid use disorder; member; multidisciplinary; neurophysiology; non rapid eye movement; opioid misuse; opioid use disorder; participant enrollment; participant retention; physical conditioning; predictive modeling; primary endpoint; programs; quality assurance; recruit; retention rate; secondary outcome; skills; sleep health; sleep quality; standard measure; statistical and machine learning; systematic review; treatment adherence; treatment response; web site; web-accessible

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.

阿片类药物疾病（OUD）是一个主要的公共卫生问题，影响了270万人，并导致 2020年在美国，超过90,000人死亡，并广泛影响 患有OUD的个人以及受影响个人的社区和家庭。治疗 OUD的有效性取决于保留护理。不幸的是，一项系统的评论报告了 阿片类药物使用障碍药物（MOUD）计划的6个月的中位保留率19 研究只有58％。睡眠障碍已被确定为治疗流失的预测因素，并且是 通过涉及疼痛，压力和情绪失调的双向途径与OUD有关，并且可以 作为未来的干预目标，以改善OUD结果。但是，对 早期恢复期间，特定的睡眠和昼夜节律特定度量的预测价值，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的数据。我们有 组建了一支由睡眠科学家，成瘾医学专家，生物统计学家和临床试验者组成的团队 将利用杨百翰和妇女医院的睡眠医学计划的卓越资源 哈佛大学朝圣者医疗保健研究所的流行病学，睡眠阅读中心和生物统计学司 领导领导和数据协调中心（LDCC）进行这项多站点研究。 LDCC将开发 用于收集标准化数据的常见协议，以预测四项研究的MOUD结果 中心并将领导，协调和实施该共同协议的所有方面，提供 全面，响应和创新的数据和项目管理。它将促进招聘和数据 共享和支持严格的综合睡眠测量收集和分析。此外 现有预测模型和治愈计划所建议的风险因素常见数据元素，睡眠将为 由EEG评估 - 神经生理学和精神病的生物标志物 - 将在两种中进行测量 夜晚约有400名参加MOUD计划的患者大约一个月。睡眠宏和 微观结构将使用集中的睡眠评分和高级脑电图定量信号得出 分析。睡眠呼吸的呼吸，周期性的肢体运动，睡眠效果模式和昼夜节律风险因素 将通过多摄影术，经过验证的问卷和多天的FITBIT跟踪器来衡量。主要 终点将在研究入学后6个月后保留治疗。次要结果将包括时间 治疗辍学，非法药物使用和非医疗阿片类药物的使用；阿片类药物渴望；戒断症状； 饮酒；疼痛强度和干扰；身体功能；睡眠障碍和质量在6-- 月。我们将采用合奏算法（超级学习者）来开发发现的预测模型 统计和机器学习算法集合的最佳组合。这个基于数据科学的计划 而且我们的多学科专业知识将确保实现这项研究的目标。