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.

影响摘要慢性疼痛会影响5000万美国成年人，严重干扰了超过2500万的工作和寿命每年花费6350亿美元用于医疗和导致生产率的损失。虽然有些非 - 药理学补充疼痛管理方法，例如基于正念的压力减轻（MBSR）有效减轻某些患者的疼痛，其他患者没有反应。临床医生缺乏工具准确，可靠地预测哪些患者将对完整的治疗做出反应。种族和种族在完成干预措施的研究和实践中，潜水员人群的人数也不足尽管增加了慢性疼痛和相关不良后果的风险。响应RFA-NS-22-050 （UG3/UH3），影响 - 基于正念的整合正念预测方法使用机器学习方法（AI的子场）来识别生物心理学预测的治疗建议并监视对MBSR的响应标记，以减少慢性下背部疼痛（CLBP）。这项研究将针对潜水员，高风险人群患有CLBP（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治疗的CLBP治疗的潜水员患者收集。 AIM 1（UG3）将涉及 MBSR的CLBP（n = 50）和ML模型的临床试验，并具有纵向生物心理社会数据和相关临床试验试用数据集以识别候选人的预测和监视对MBSR的响应的CLBP的标记在UH3阶段扩展试验。从UG3阶段（目标1）过渡到较大临床的里程碑 UH3阶段的试验（目标2+3）将包括：（1）最终数据收集和主要分析协议 MBSR的CLBP临床试验，（2）通过被动数据收集程序的成功和实验 ML模型培训和测试，以鉴定预测和监测的生物心理社会标记 CLBP对MBSR的响应，以及（3）基于ML ML的生物心理预测的初步验证并使用统计和交叉验证方法监测对CLBP的响应标记。目的 2（UH3）将扩大在AIM 1中启动的临床试验，以从较大样本中收集生物心理社会数据（n = 300）。 AIM 3（UH3）将与AIM 2中收集的数据进行ML建模，以识别和验证准确 CLBP对MBSR反应的生物心理社会预测和监测标记。为了完成我们的目标，波士顿大学，马萨诸塞大学陈医学院和剑桥的临床科学家卫生联盟具有广泛的专业知识，以成功招募和吸引潜水员人群参与临床正念干预措施的疼痛试验将与生物医学，数据科学家和机器学习合作伍斯特理工学院的研究人员。这个拟议的项目最终将增强临床决策 - 对CLBP进行靶向治疗。