Predicting Analgesic Response to Acupuncture - A Practical Approach

预测针灸镇痛反应 - 一种实用方法

基本信息

批准号：
9762627
负责人：
Jiang-Ti Kong
金额：
$ 16.12万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9762627
关键词：
Acupuncture Analgesia Acupuncture Therapy Affect Aftercare American Analgesics Award Behavioral Biological Markers Caring Characteristics Chronic low back pain Clinic Clinical Clinical Research Clinical Trials Cognitive Comorbidity Coping Skills Data Diabetes Mellitus Diagnostic Electroacupuncture Emotional Engineering Ensure Funding Goals Health Care Costs Heart Diseases Individual Inflammation Mediators Intervention K-Series Research Career Programs Knowledge Lasso Literature Malignant Neoplasms Measures Medicine Mentored Patient-Oriented Research Career Development Award Mentors Mission Modeling Modernization Nociception Outcome Pain Pain Research Pain Threshold Pain management Pathway interactions Patient Triage Patients Peripheral Pharmaceutical Preparations Physical therapy Physicians Pilot Projects Placebos Predictive Factor Process Provider Psychological Factors Randomized Regulation Research Research Personnel Resources Risk Self Efficacy Sensory Signal Transduction Spinal Standardization Statistical Models Symptoms Testing Time Training United States National Institutes of Health Validation Visit Work base behavior test career central pain chronic pain chronic pain patient clinical practice conditioned pain modulation conventional therapy coping cost demographics effective therapy evidence base expectation improved instrument mathematical model model building multidisciplinary novel outcome prediction pain catastrophizing pain inhibition pain patient pain processing pain reduction personalized medicine predicting response predictive modeling pressure primary outcome psychologic public health relevance regression trees responders and non-responders response side effect success tool transmission process treatment response

项目摘要

DESCRIPTION (provided by applicant): One hundred million Americans suffer from chronic pain. Acupuncture is more effective than conventional medicine (defined as medication, physician and physiotherapy visits) in several common pain conditions yet we currently do not know who will and will not benefit from acupuncture. Previous attempts on identifying responders to acupuncture have limited success. In this mentored career development award (K23), Dr. Kong proposes to develop an effective clinical tool to predict response to acupuncture using novel predictors. She hypothesizes that specific mechanistic measures associated with the processing of pain (i.e. quantitative sensory testing and key psychological instruments) will distinguish responders from non-responders. She will systematically examine predictors of the ascending and the descending pain pathways in aims 1 and 2 in a clinical study wherein patients with chronic low back pain will be randomized to receive either verum vs sham electroacupuncture. In aim 3, she will build a simple predictive model of response to acupuncture and test this model on patients from a separate, ongoing trial conducted by her mentor Dr. Mackey. The product of the proposed projects will be an effective tool to help providers triage patients either to or away from acupuncture, representing a major advance in the clinical practice of both acupuncture and pain management. During the award period, Dr. Kong will receive advanced training in statistical programming, predictive modeling, and gain knowledge on psychological factors implicated in pain and acupuncture. Dr. Kong has also assembled a team of multidisciplinary, world class mentors who are committed to her success. Dr. Kong's overall career objective is to conduct independently funded clinical research to promote evidence-based integration of acupuncture in modern pain management. Dr. Kong is an accomplished clinician, acupuncturist and engineer. This K23 award will ensure the final avenue/resources to propel her into a successful, independent research career.

描述（由申请人提供）：对于一亿名患有慢性疼痛的美国人来说，针灸对于几种常见的疼痛状况比传统医学（定义为药物、医生和物理治疗）更有效，但我们目前不知道谁会受益，谁不会受益。之前在识别针灸反应者方面的尝试效果有限。在这个指导性职业发展奖（K23）中，Kong 博士建议开发一种有效的临床工具，使用新的预测因子来预测针灸反应。她提出，与疼痛处理相关的具体机制措施（即感觉测试和关键心理工具）将区分定量反应者和无反应者。她最终将在目标 1 和 2 中检查上升和下降疼痛路径的预测因素。在临床研究中，慢性腰痛患者将被随机分配接受真针电针治疗还是假电针治疗。在目标 3 中，她将建立一个简单的针灸反应预测模型，并在患者身上测试该模型。由她的导师 Mackey 博士进行的独立、正在进行的试验将成为帮助提供者对针灸或不接受针灸治疗的患者进行分类的有效工具，代表了针灸和疼痛管理临床实践的重大进步。在获奖期间，孔博士将接受统计编程、预测建模方面的高级培训，并获得有关疼痛和针灸的心理因素的知识。孔博士还组建了一支由多学科、世界一流的导师组成的团队，为她提供帮助。成功。 Kong 博士的总体职业目标是进行独立资助的临床研究，以促进针灸在现代疼痛管理中的循证整合。Kong 博士是一位卓有成就的临床医生、针灸师和工程师，该 K23 奖项将确保推动针灸的最终途径/资源。她进入了成功的、独立的研究生涯。