Integrating Pragmatic Comparative Effectiveness Research into a Tertiary Pain Management Center

将实用的比较有效性研究纳入三级疼痛管理中心

• 批准号: 10216024
• 负责人: Vafi Salmasi
• 金额: $ 19.35万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10216024
• 关键词: Academic Training; Anticonvulsants; Antidepressive Agents; Anxiety; Characteristics; Clinic; Clinic Visits; Clinical; Clinical Data; Clinical Research; Clinical Trials; Clinical Trials Design; Comparative Effectiveness Research; Data; Data Collection; Decision Making; Decision Support Systems; Development; Equipoise; Funding; Goals; Healthcare; Healthcare Systems; Institution; Judgment; Learning; Logistic Regressions; Machine Learning; Master' s Degree; Medical; Medicine; Mental Depression; Mentors; Mentorship; Modeling; Observational Study; Pain; Pain intensity; Pain interference; Pain management; Patient Care; Patients; Pharmaceutical Preparations; Physical Function; Physicians; Pilot Projects; Pragmatic clinical trial; Precision Health; Predictive Factor; Process; Publications; Randomized; Randomized Controlled Trials; Reporting; Research; Resources; Scientist; Site; Social Functioning; Societies; Surveys; Testing; Training; United States; United States National Institutes of Health; base; career; career development; chronic pain; chronic pain patient; clinical care; clinical epidemiology; clinical pain; clinical practice; comorbidity; comparative effectiveness trial; compare effectiveness; cost; effective therapy; flexibility; improved; individual patient; low socioeconomic status; machine learning algorithm; open source; pain catastrophizing; patient oriented; patient subsets; personalized medicine; point of care; pragmatic trial; predicting response; predictive modeling; psychologic; real world application; research and development; response; statistics; tool; treatment arm; treatment response

Project Summary: Chronic pain is a major healthcare problem with an annual cost of above $600 billion. The quality of data available for treatments of chronic pain is not optimal. Generalizability of explanatory randomized controlled trial data is problematic as these trials exclude up to 90% of patients: leaving out real-world patients with serious medical and psychological comorbidities. Pragmatic trials embedded in patient care compare effectiveness of currently used treatments in real-world application leading to findings that generalize to broader range of patients. The changes in clinical practice and workflow necessary to integrate this type of research within patient care present pragmatic challenges. In this research, my overall objective is to overcome these challenges using an open-source learning health care system – CHOIR – developed by my mentor at the Stanford Pain Management Center. CHOIR is currently used to track patients’ clinical trajectory and treatment response across multiple academic sites resulting in over 25 publications characterizing chronic pain. Through our pilot studies, we have already developed a point-of-care randomization for CHOIR that facilitates integration of research and patient care by allowing the physicians to randomize patients during clinic visits. We have already demonstrated feasibility of the randomization and data collection platform in two ongoing pilot pragmatic clinical trials. We are proposing to better integrate pragmatic research within our clinical practice through conducting a randomized comparative effectiveness trial in 450 patients with chronic pain comparing effectiveness of anti- convulsants and anti-depressants (two most commonly used classes of medications for treatment of chronic pain). We will use the data available in CHOIR as well as the real-world data generated from this clinical trial to build, validate and test a model to predict what clinical characteristics can predict response to either of these classes of medications. The proposed study is the first step to use flexible point-of-care randomization to compare effectiveness of different treatments in different subgroups of patients whenever equipoise exists. Our prediction model will guide decision making process of clinicians choosing between these medications based on clinical characteristics of individual patients. Dr. Salmasi is a physician-scientist with clinical training in Pain Medicine as well as academic training in Clinical Research and Epidemiology. The detailed career development and research plan presented in this application will provide the required resources and mentorship for him to become an independent R01-funded expert in two domains critical to his long-term career goals: (1) advanced clinical trial design and conduct; and (2) advanced statistics and machine learning.

项目摘要： 慢性疼痛是一个主要的医疗保健问题，每年花费超过6000亿美元。现有数据的质量 对于慢性疼痛的治疗并不是最佳的。解释性随机对照试验数据的概括性是 问题在于这些试验排除了高达90%的患者：排除了具有严重医学症状的真实患者。 和心理共病嵌入患者护理的务实试验比较了当前 在实际应用中使用的治疗方法导致了适用于更广泛患者的发现。 临床实践和工作流程的变化，将此类研究纳入患者护理 提出了务实的挑战。在这项研究中，我的总体目标是使用一种 开源学习医疗保健系统- CHOIR -由我在斯坦福大学疼痛管理的导师开发 中心CHOIR目前用于跟踪多个患者的临床轨迹和治疗反应 学术网站导致超过25篇关于慢性疼痛的出版物。通过我们的试点研究， 已经为CHOIR开发了一种护理点随机化，有助于研究和患者的整合 通过允许医生在诊所就诊期间随机分配患者来进行护理。我们已经证明了 在两个正在进行的试点实用临床试验中的随机化和数据收集平台的可行性。 我们建议通过开展一项 在450名慢性疼痛患者中进行的随机比较有效性试验， 惊厥药和抗抑郁药（两种最常用的治疗慢性病的药物） 疼痛）。我们将使用CHOIR中可用的数据以及本临床试验产生的真实世界数据， 建立、验证和测试模型，以预测哪些临床特征可以预测对这些药物的反应 类药物。 这项拟议的研究是第一步，使用灵活的即时随机化来比较 当平衡存在时，在不同的患者亚组中进行不同的治疗。我们的预测模型将指导 临床医生根据患者的临床特征在这些药物之间进行选择的决策过程 个别患者。 博士Salmasi是一位医生，科学家，接受过疼痛医学的临床培训，也接受过临床医学的学术培训。 研究和流行病学。本申请中提供的详细职业发展和研究计划 将提供必要的资源和指导，使他成为一个独立的R 01资助的专家，在两个 对他的长期职业目标至关重要的领域：（1）先进的临床试验设计和实施;（2）先进的 统计学和机器学习。