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.

Public Health Relevance

The ability to predict pain reduction by acupuncture will improve the management of patients with chronic pain (whose number exceeds that of diabetes, heart disease and cancer combined) by offering an effective treatment (that is free of the side effects of medications) to the responders of acupuncture and by shorting the time to find an effective therapy to the non-responders. The project is therefore relevant to NIH's mission because it reduces pain and suffering and it saves healthcare cost.

Agency
National Institute of Health (NIH)
Institute
National Center for Complementary & Alternative Medicine (NCCAM)
Type
Mentored Patient-Oriented Research Career Development Award (K23)
Project #
5K23AT008477-04
Application #
9543945
Study Section
Special Emphasis Panel (ZAT1)
Program Officer
Chen, Wen G
Project Start
2015-09-01
Project End
2020-08-31
Budget Start
2018-09-01
Budget End
2019-08-31
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Stanford University
Department
Anesthesiology
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
Mackey, Ian G; Dixon, Eric A; Johnson, Kevin et al. (2017) Dynamic Quantitative Sensory Testing to Characterize Central Pain Processing. J Vis Exp :
MacPherson, Hugh; Hammerschlag, Richard; Coeytaux, Remy R et al. (2016) Unanticipated Insights into Biomedicine from the Study of Acupuncture. J Altern Complement Med 22:101-7