Sleep deficiency and opioid use/misuse in adolescents following surgery

项目总结 处方阿片类药物滥用是美国青少年公共卫生的重大负担。阿片类药物 滥用往往始于处方的阿片类药物，手术是青少年 第一个用于治疗急性疼痛的处方阿片类药物。然而，人们对接下来的关键时期知之甚少。 青少年开始处方阿片类药物滥用或可改变的行为机制的手术 为这一进程作出贡献。这些是我们知识中的关键差距，阻碍了我们识别 青少年滥用阿片类药物的风险增加，并制定旨在减少处方的干预措施 阿片类药物滥用。睡眠不足(包括睡眠剥夺、无昼夜睡眠、睡眠障碍和睡眠不良 睡眠质量)是处方阿片类药物滥用的重要近端风险因素。睡眠经常被打乱 围手术期，这是许多青少年接触到他们的第一个阿片类药物处方的时期。的确， 在我们自己的初步研究中，我们发现睡眠不足在手术前和手术中都存在。 术后即刻与阿片类药物的使用增加有关。然而，这项初步研究并没有 允许我们描述与阿片类药物使用最密切相关的睡眠方面，而不允许我们评估 睡眠-阿片类药物使用关系的潜在机制，如疼痛和心理因素。 此外，迫切需要数据来确定睡眠不足如何预测 在其他假定因素的背景下，如物质史，阿片类药物滥用行为的发展 使用、疼痛强度、心理社会因素(如抑郁)、同伴和家庭因素。鉴于睡眠不足是 它是可修改的，是旨在减少青少年滥用阿片类药物的研究的关键重点 行为。因此，本项目的目标是：1)测试睡眠不足、疼痛、 心理因素和运动损伤手术后阿片类药物的使用，以及2)发展和验证 多变量预测模型用于确定青少年滥用处方阿片类药物的风险 术后24个月。为了达到这些目标，我们提出了一项N=400的前瞻性观察性研究 青少年(10-19岁)在运动损伤手术后首次接受阿片类药物治疗。手术前， 参与者将接受全面的多模式睡眠评估(调查和活动监测)，以 测量睡眠不足。参与者还将报告既往药物使用、疼痛强度、心理社会、 同龄人和家庭因素。然后，青少年将在手术后的前14天内使用生态学 即时评估，以捕获有关睡眠、疼痛、心理因素和阿片类药物使用的实时每日数据。 我们将使用创新的电子药物监测方法来准确测量阿片类药物的使用 (总剂量和持续时间)手术后。3个月、6个月、12个月的随访评估 几个月，24个月将跟踪阿片类药物滥用随着时间的推移而发展。我们将应用现代机器学习 开发和验证预测青少年处方阿片滥用的模型的算法。

PROJECT SUMMARY Prescription opioid misuse is a significant burden on adolescent public health in the United States. Opioid misuse often starts with prescribed opioids, with surgery representing a key pathway by which adolescents are first prescribed opioids for the management of acute pain. Yet, little is known about the critical period following surgery during which adolescents initiate prescription opioid misuse or the modifiable behavioral mechanisms contributing to this process. These are critical gaps in our knowledge impeding our ability to identify adolescents at increased risk for opioid misuse and to develop interventions aimed at reducing prescription opioid misuse. Sleep deficiency (including sleep deprivation, noncircadian sleep, sleep disorders, and poor sleep quality) is an important proximal risk factor for prescription opioid misuse. Sleep is often disturbed during the perioperative period, a time when many adolescents are exposed to their first opioid prescription. Indeed, in our own preliminary study, we found that sleep deficiency present both before surgery and during the immediate postsurgical period was associated with increased opioid use. However, this pilot study did not allow us to characterize aspects of sleep most strongly related to opioid use and did not allow us to evaluate mechanisms, such as pain and psychological factors, underlying the sleep – opioid use relationship. Furthermore, data are urgently needed to determine how sleep deficiency prospectively predicts the development of opioid misuse behaviors in the context of other putative factors, such as a history of substance use, pain intensity, psychosocial (e.g., depression), peer, and family factors. Given that sleep deficiency is modifiable, it is a critical focus of research aimed at reducing the development of adolescent opioid misuse behaviors. Therefore, this project aims to 1) test the direct and mediation pathways of sleep deficiency, pain, psychological factors, and opioid use following sports-injury surgery, and 2) develop and validate a multivariable prediction model to identify adolescents at increased risk of prescription opioid misuse over the 24 months following surgery. To address these aims, we propose a prospective, observational study of N= 400 adolescents (10-19 years) who receive their first ever opioid following sports injury surgery. Presurgery, participants will undergo comprehensive multimodal sleep assessments (surveys and actigraphy monitoring) to measure sleep deficiency. Participants will also report on previous substance use, pain intensity, psychosocial, peer, and family factors. Adolescents will then be followed over the first 14 days after surgery using ecological momentary assessment to capture real-time daily data on sleep, pain, psychological factors, and opioid use. We will use an innovative electronic medication monitoring methodology to accurately measure opioid use (total number of doses and duration) following surgery. Follow-up assessments at 3-months, 6-months, 12- months, and 24-months will track opioid misuse developing over time. We will apply modern machine learning algorithms to develop and validate models predicting adolescent prescription opioid misuse.