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

基本信息

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
项目类别：
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

项目摘要

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.

影响摘要慢性疼痛影响着 5000 万美国成年人，严重干扰了超过 2500 万人的工作和生活，每年的医疗费用和由此造成的生产力损失达 6,350 亿美元。药理学补充性疼痛管理方法，例如基于正念的减压 (MBSR) 可以有效减轻某些患者的疼痛，但其他患者则缺乏相应的工具。准确可靠地预测哪些患者会对种族和民族方面的补充治疗产生反应。在补充干预措施的研究和实践中，不同人群的代表性也不足尽管有相关的不良后果，但慢性疼痛的风险却增加了响应 RFA-NS-22-050。 (UG3/UH3)，IMPACT – 基于正念的综合预测方法治疗慢性腰痛使用机器学习方法（人工智能的一个子领域）来识别生物心理社会预测的治疗建议监测慢性腰痛 (cLBP) MBSR 反应的标志物。患有 cLBP 的多样化高风险人群（总计 n=350）。综合生物心理社会数据（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 治疗的 cLBP 的不同患者中收集数据，目标 1 (UG3) 将涉及启动 MBSR 的 cLBP 临床试验 (n=50) 和 ML 建模纵向生物心理社会数据和相关临床试验数据集，以确定 cLBP 之前对 MBSR 反应的候选预测和监测标记扩大 UH3 阶段的试验从 UG3 阶段（目标 1）过渡到更大规模的临床阶段的里程碑。 UH3 阶段（目标 2+3）的试验将包括：(1) 最终确定的数据收集和主要分析方案 MBSR 治疗 cLBP 的临床试验，(2) 被动数据收集程序和实验取得成功机器学习模型训练和测试，用于识别预测和监测生物心理社会标记对 cLBP 的 MBSR 反应，以及 (3) 基于 ML 的候选生物心理社会预测的初步验证使用统计和交叉验证方法监测 cLBP 对 MBSR 反应的标记。 2 (UH3) 将扩大目标 1 中启动的临床试验，以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，以识别和验证准确性。 cLBP 对 MBSR 反应的生物心理社会预测和监测标记。来自波士顿大学、马萨诸塞大学陈医学院和剑桥大学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丰富的专业知识将与生物医学、数据科学家和机器学习合作进行正念干预疼痛的试验伍斯特理工学院的研究人员提出的这个项目最终将增强临床决策。 cLBP的制定和靶向治疗。