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: 10398940
• 负责人: Vafi Salmasi
• 金额: $ 19.21万
• 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/10398940
• 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 management; 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.

项目总结： 慢性疼痛是一个主要的医疗保健问题，每年的成本超过6000亿美元。可用数据的质量 对于慢性疼痛的治疗并不是最理想的。解释性随机对照试验数据的概括性为 由于这些试验排除了高达90%的患者，这是有问题的：忽略了现实世界中患有严重疾病的患者 以及心理上的共病。嵌入患者护理的务实试验比较目前 在实际应用中使用的治疗方法导致的结果适用于更广泛的患者。 将这类研究整合到病人护理中所需的临床实践和工作流程的变化 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 由我在斯坦福疼痛管理中心的导师开发的开源学习医疗保健系统--唱诗班 中心。合唱团目前被用来跟踪患者的临床轨迹和治疗反应 学术网站导致了超过25种描述慢性疼痛的出版物。通过我们的初步研究，我们已经 已经为合唱团开发了便于研究和患者集成的护理点随机化 通过允许医生在诊所就诊期间随机对患者进行护理。我们已经演示过了 随机化和数据收集平台在两个正在进行的试点实用临床试验中的可行性。 我们建议更好地将务实研究与临床实践相结合，方法是开展 450例慢性疼痛患者的随机对照疗效试验 惊厥药和抗抑郁药(治疗慢性阻塞性肺病最常用的两类药物 疼痛)。我们将使用合唱团提供的数据以及从这项临床试验中产生的真实数据来 建立、验证和测试一个模型，以预测哪些临床特征可以预测对其中任何一个的反应 药物的种类。 这项拟议的研究是使用灵活的护理点随机化来比较以下各项有效性的第一步 在均衡存在的情况下，针对不同的患者亚组进行不同的治疗。我们的预测模型将指导 临床医生根据临床特点进行药物选择的决策过程 个别病人。 萨尔马西博士是一名内科科学家，曾接受过疼痛医学临床培训和临床学术培训 研究和流行病学。此申请表中提供的详细职业发展和研究计划 将为他提供所需的资源和指导，使他在两年内成为R01资助的独立专家 对他的长期职业目标至关重要的领域：(1)高级临床试验设计和实施；以及(2)高级 统计和机器学习。