Personalized Models of Depression and Chronic Pain

抑郁症和慢性疼痛的个性化模型

• 批准号: 10474958
• 负责人: Madelyn Frumkin
• 金额: $ 4.24万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10474958
• 关键词: Address; Adult; Alexithymias; Area; Arousal; Blood Pressure; Chronic; Chronic disabling pain; Clinical; Clinical Trials; Data; Depressed mood; Development; Dose; Ecological momentary assessment; Emotional; Equation; Exhibits; Fellowship; Fostering; Goals; Heart Rate; Incidence; Individual; Intervention; Lead; Life; Mental Depression; Mental Health; Mentorship; Methodology; Modeling; Mood Disorders; National Institute of Mental Health; Opiate Addiction; Opioid; Outcome; Overdose; Pain; Pathway interactions; Patient Self-Report; Patients; Physiological; Public Health; Recommendation; Reporting; Research; Research Personnel; Research Training; Risk; Role; Severities; Suicide; Symptoms; Testing; Time; Training; Universities; Variant; Washington; associated symptom; base; career; chronic pain; depression model; depressive symptoms; emotional experience; experience; individualized medicine; multilevel analysis; pain perception; pain symptom; personalized approach; personalized medicine; physical conditioning; physical symptom; primary care setting; prospective; psychologic; psychological symptom; trend

Project Summary Depression is a common and disabling mental health condition that can also have serious implications for physical health. As many as 41% of individuals with depression report disabling chronic pain1. Individuals with depression are likely to be prescribed higher doses of opioids, contributing to greater risk of abuse, overdose, and suicide4–6. Psychological interventions may alleviate both physical and emotional suffering; however, it is currently difficult to identify patients who are most likely to benefit from these approaches2,3. Personalized (i.e., idiographic or N = 1) models of depression and pain could lead to more precise treatment targets for patients with co-occurring symptoms. For example, if increased depressive symptoms predict increased pain in the daily life of Patient A, treatment for depression may be effective in reducing both psychological and physical symptoms. In contrast, if depressive symptoms are predicted by increased pain in the daily life of Patient B, pain-focused interventions may be indicated. In the current study, I will collect ecological momentary assessment data from patients with co-occurring depressive symptoms and chronic pain (N = 75). Each individual will provide approximately 105 data points, allowing for the development of personalized symptom models using dynamic structural equation modeling14. Personalized models will be used to assess the degree to which depression predicts pain and pain predicts depression for different individuals (Aim 1). I hypothesize that prospective relationships between depressed mood and pain will vary on a continuum between individuals, as opposed to the alternative hypothesis that these relationships are present for all or no individuals. If increased depression predicts increased pain severity in daily life for some individuals, this would suggest that personalized models might be useful in tailoring treatment. I will also collect ambulatory blood pressure and heart rate data to assess the degree to which depressed mood increases physiological arousal, which in turn predicts perceived pain (Aim 2). I hypothesize that these relationships will also vary between individuals, with some individuals exhibiting a relationship indicative of physiological arousal as a mechanism whereby depression perpetuates pain. Finally, in order to foster understanding and development of personalized models, I will assess moderators of individual-level relationships (Aim 3). This proposal will provide training in the application of personalized medicine approaches to the assessment of co-occurring depression and pain. My long-term goal is to lead clinical trials assessing the feasibility and utility of tailoring treatment for depression and co-occurring pain based on personalized models. In this proposal, I will take a first step towards developing these models. I will also receive interdisciplinary training in mood disorders, chronic pain, and physiological functioning from my mentorship team at Washington University in St. Louis. This fellowship will serve as the perfect capstone to my graduate studies as I prepare to begin my career as an independent clinical researcher.

项目摘要 抑郁症是一种常见的致残心理健康状况，也可能对 身体健康。多达41%的抑郁症患者报告说慢性疼痛是无效的1。具有以下特征的个人 抑郁症可能会被开出更高剂量的阿片类药物，从而导致更大的滥用、过量服药、 心理干预可能会减轻身体和精神上的痛苦；然而，它是 目前很难确定哪些患者最有可能从这些方法中受益2，3.个性化 (例如，具体的或N=1)抑郁和疼痛的模型可能导致更精确的治疗目标 适用于同时出现症状的患者。例如，如果抑郁症状的增加预示着 在患者A的日常生活中，治疗抑郁症可能在减少心理和心理上都有效 身体症状。相反，如果抑郁症状是通过日常生活中疼痛的增加来预测的 患者B，可能需要以疼痛为重点的干预措施。在目前的研究中，我将收集生态 来自同时出现抑郁症状和慢性疼痛患者的瞬时评估数据 (n=75)。每个人将提供大约105个数据点，以便开发 使用动态结构方程建模的个性化症状模型14。将使用个性化的模型 评估抑郁预测疼痛的程度，疼痛预测不同个体的抑郁 (目标1)。我假设抑郁情绪和疼痛之间的预期关系会因 个体之间的连续体，与这些关系存在的替代假设相反 对于所有人或没有个人。如果抑郁加剧预示着一些人日常生活中疼痛的严重程度增加 对于个人，这将表明个性化模式在量身定做治疗中可能有用。我也会收集 动态血压和心率数据，以评估抑郁情绪增加的程度 生理唤醒，进而预测感知到的疼痛(目标2)。我假设这些关系会 个体之间也不同，有些个体表现出生理唤醒的关系 作为一种抑郁使疼痛永久化的机制。最后，为了增进理解和 为了开发个性化的模型，我将评估个人层面关系的调解人(目标3)。这 提案将提供培训，将个性化医疗方法应用于 评估同时发生的抑郁和疼痛。我的长期目标是领导临床试验，评估 基于个性化模型的抑郁症和共生疼痛个体化治疗的可行性和实用性。 在这份提案中，我将朝着开发这些模型迈出第一步。我还将获得跨学科的 由我在华盛顿的指导团队提供的情绪障碍、慢性疼痛和生理功能方面的培训 圣路易斯大学。这笔奖学金将成为我研究生学习的完美顶峰，因为我正在准备 我作为一名独立的临床研究员开始了我的职业生涯。

项目成果

Feasibility and Acceptability of a Preoperative Multimodal Mobile Health Assessment in Spine Surgery Candidates.

脊柱手术候选人术前多模式移动健康评估的可行性和可接受性。

• DOI: 10.1227/neu.0000000000002245
• 发表时间: 2023
• 期刊: Neurosurgery
• 影响因子: 4.8
• 作者: Greenberg,JacobK;Frumkin,MadelynR;Javeed,Saad;Zhang,JustinK;Dai,Ruixuan;Molina,CamiloA;Pennicooke,BrentonH;Agarwal,Nitin;Santiago,Paul;Goodwin,MatthewL;Jain,Deeptee;Pallotta,Nicholas;Gupta,MunishC;Buchowski,JacobM;Leuth
• 通讯作者: Leuth