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.

阿片类药物使用障碍（OUD）是一个重大的公共卫生问题，影响到270多万人，并导致