IMPACT: Integrative Mindfulness-Based Predictive Approach for Chronic low back pain Treatment

影响：基于正念的综合预测方法治疗慢性腰痛

• 批准号: 10794463
• 负责人: Emmanuel Agu
• 金额: $ 164.32万
• 依托单位: WORCESTER POLYTECHNIC INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10794463
• 关键词: Address; Adult; Affect; American College of Physicians; Anxiety; Artificial Intelligence; Biological; Biological Markers; Boston; Chronic low back pain; Circadian Rhythms; Clinical Data; Clinical Trials; Collaborations; Complementary therapies; Complex; Data; Data Analyses; Data Collection; Data Reporting; Data Scientist; Data Set; Decision Making; Epidemic; Evaluation; Funding; Health Alliance; Heart Rate; Heroin; Individual; Institutional Review Boards; Intervention; Intervention Studies; Life; Machine Learning; Massachusetts; Medical; Mental Depression; Methodology; Methods; Modality; Monitor; Morones; Motor Activity; Opioid; Pain; Pain Management Method; Pain Research; Pain management; Participant; Patient Self-Report; Patients; Pattern; Performance; Persons; Phase; Physical activity; Pilot Projects; Population Heterogeneity; Pragmatic clinical trial; Procedures; Recommendation; Research; Research Personnel; Risk; Sampling; Scientist; Sleep; Social support; Specificity; Testing; Training; United States National Institutes of Health; Universities; Validation; Work; adverse outcome; biomedical scientist; biopsychosocial; biopsychosocial factor; candidate identification; candidate validation; chronic musculoskeletal pain; chronic pain; chronic pain management; chronic pain patient; clinical decision-making; clinical trial protocol; cost; diverse data; effective intervention; effective therapy; emotion regulation; ethnic diversity; fitbit; health disparity; heart rate variability; high risk population; individual response; machine learning algorithm; machine learning method; machine learning model; medical schools; mindfulness; mindfulness intervention; mindfulness-based stress reduction; multimodal data; opioid abuse; opioid overdose; pain outcome; pain patient; pain reduction; patient response; predicting response; predictive modeling; productivity loss; psychologic; psychosocial; racial diversity; recruit; response; response biomarker; secondary analysis; social; success; targeted treatment; tool; treatment response; wearable device

IMPACT Abstract Chronic pain impacts 50 million U.S. adults, severely interferes with the work and life of over 25 million, and costs $635 billion annually for medical treatment and resultant loss of productivity. While some non- pharmacological complementary pain management methods, such as Mindfulness-Based Stress Reduction (MBSR), are effective at reducing the pain of some patients, others do not respond. Clinicians lack the tools to accurately and reliably predict which patients will respond to complementary treatments. Racially and ethnically diverse populations are also underrepresented in both research and practice of complementary interventions despite increased risk for chronic pain and related adverse outcomes. In response to RFA-NS-22-050 (UG3/UH3), IMPACT – Integrative Mindfulness-Based Predictive Approach for Chronic low back pain Treatment proposes using machine learning methods (a subfield of AI) to identify biopsychosocial predictive and monitoring markers of the ’ response to MBSR for chronic low back pain (cLBP). This research will target a diverse, high risk population suffering from cLBP (total n=350). Comprehensive biopsychosocial data (locomotor activity, sleep, circadian rhythms, heart rate variability, depression, anxiety, pain outcomes, and social support) will be collected from diverse patients treated with MBSR for cLBP. Aim 1 (UG3) will involve the initiation of a clinical trial of MBSR for cLBP (n=50) and ML modeling with longitudinal biopsychosocial data and related clinical trial datasets to identify candidate predictive and monitoring markers of the response to MBSR for cLBP prior to expanding the trial in the UH3 phase. Milestones for transition from the UG3 phase (Aim 1) to the larger clinical trial of the UH3 phase (Aims 2+3) will include: (1) finalized data collection and primary analysis protocols for the clinical trial of MBSR for cLBP, (2) success with passive data collection procedures and experimentation with ML model training and testing for the identification of predictive and monitoring biopsychosocial markers of the response to MBSR for cLBP, and (3) preliminary validation of candidate ML-based biopsychosocial predictive and monitoring markers of the response to MBSR for cLBP using statistical and cross-validation methods. Aim 2 (UH3) will expand the clinical trial initiated in Aim 1 to collect biopsychosocial data from a larger sample (n=300). Aim 3 (UH3) will involve ML modeling with data collected in Aim 2 to identify and validate accurate biopsychosocial predictive and monitoring markers of the response to MBSR for cLBP. To complete our aims, clinician scientists from Boston University, University of Massachusetts Chan Medical School, and Cambridge Health Alliance with extensive expertise in successfully recruiting and engaging diverse populations in clinical trials of mindfulness interventions for pain will collaborate with biomedical, data scientists and machine learning researchers from Worcester Polytechnic Institute. This proposed project will ultimately enhance clinical decision- making and targeted treatment of cLBP.

IMPACT摘要 慢性疼痛影响了5000万美国成年人，严重干扰了2500多万人的工作和生活， 并且每年花费6350亿美元用于医疗和由此导致的生产力损失。虽然有些非- 药物辅助疼痛管理方法，如基于正念的减压 正念减压疗法（MBSR）可以有效地减轻一些患者的疼痛，其他人则没有反应。临床医生缺乏工具， 准确可靠地预测哪些患者会对补充治疗产生反应。种族和族裔 不同人群在补充干预措施的研究和实践中的代表性也不足 尽管慢性疼痛和相关不良后果的风险增加。响应RFA-NS-22 - 050 (UG3/UH3），IMPACT-基于正念的慢性腰痛综合预测方法 治疗建议使用机器学习方法（人工智能的一个子领域）来识别生物心理社会预测 以及监测对慢性下背痛（cLBP）的正念减压疗法的反应的标志物。这项研究将针对 患有cLBP的不同的高风险人群（总n = 350）。全面的生物心理社会数据（运动 活动、睡眠、昼夜节律、心率变异性、抑郁、焦虑、疼痛结果和社会支持） 将从接受MBSR治疗cLBP的不同患者中收集。目标1（UG3）将涉及启动一个 MBSR治疗cLBP的临床试验（n = 50）和使用纵向生物心理社会数据和相关临床数据的ML建模 试验数据集，以确定在治疗前对cLBP的MBSR反应的候选预测和监测标志物。 在UH3阶段扩大试验。从UG3期（目标1）过渡到更大的临床试验的里程碑 UH3期试验（目标2 + 3）将包括：（1）最终的数据收集和主要分析方案， 正念减压治疗慢性下腰痛的临床试验，（2）被动数据收集程序的成功和 ML模型训练和测试，用于识别预测和监测生物心理社会标志物， 对MBSR治疗cLBP的反应，以及（3）初步验证候选的基于ML的生物心理社会预测 以及使用统计学和交叉验证方法监测针对cLBP的对MBSR的响应的标志物。目的 2（UH3）将扩大目标1中启动的临床试验，从更大的样本中收集生物心理社会数据 （n = 300）。目标3（UH3）将涉及使用目标2中收集的数据进行ML建模，以识别和验证准确的 对MBSR治疗cLBP的反应的生物心理社会预测和监测标志物。为了完成我们的目标， 来自波士顿大学、马萨诸塞州大学陈医学院和剑桥的临床科学家 健康联盟在成功招募和吸引不同人群参与临床方面拥有广泛的专业知识 疼痛正念干预试验将与生物医学，数据科学家和机器学习合作 伍斯特理工学院的研究人员。该项目将最终提高临床决策- cLBP的制备和靶向治疗。