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

影响摘要 慢性疼痛影响着5000万美国成年人，严重干扰了2500多万人的工作和生活， 每年花费6350亿美元用于医疗和由此造成的生产力损失。而一些非- 药理互补的疼痛管理方法，如正念减压法 (MBSR)在减轻一些患者的疼痛方面有效，但另一些患者则没有反应。临床医生缺乏工具来 准确可靠地预测哪些患者将对补充治疗有反应。在种族和民族方面 在互补性干预措施的研究和实践中，不同人群的代表性也很低 尽管慢性疼痛和相关不良后果的风险增加。回应RFA-NS-22-050 (UG3/UH3)，基于影响-综合正念的慢性下腰痛预测方法 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MBSR对慢性下腰痛(CLBP)的反应的标志物。这项研究的目标是 患有慢性下腰痛的多样化高危人群(总计350人)。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 MBSR治疗慢性下腰痛的临床试验(n=50)和应用纵向生物心理社会资料和相关临床资料的ML建模 试验数据集，以确定cLBP对MBSR反应的候选预测和监测标记物 扩大UH3阶段的试验范围。从UG3阶段(目标1)过渡到更大临床阶段的里程碑 UH3阶段的试验(AIMS 2+3)将包括：(1)最后确定的数据收集和初步分析方案 MBSR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，用于确定预测和监测疟疾生物-心理-社会标志物 CLBP对MBSR的反应，以及(3)基于ML的候选生物心理社会预测的初步验证 以及使用统计和交叉验证方法监测cLBP对MBSR的反应的标记。目标 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 300例。AIM 3(UH3)将使用在AIM 2中收集的数据进行ML建模，以识别和验证准确性 慢性腰痛对MBSR疗效的生物、心理、社会预测和监测标记物。为了完成我们的目标， 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 在成功招募和吸引不同人群参与临床工作方面拥有广泛的专业知识的健康联盟 治疗疼痛的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。这项拟议的项目最终将加强临床决策- CLBP的制作和靶向治疗。

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.