Back Pain Consortium Research Program

Overview

The Research Need

Chronic low back pain is one of the most common forms of chronic pain among adults worldwide; according to the Global Burden of Disease Study 2010, it ranked highest in terms of years lived with disability among hundreds of conditions. National Health Interview Survey data indicate that 20 percent of adults in the United States reported “frequent” back pain and 28 percent experienced low back pain that lasted one or more days during the previous three months. Current chronic low back pain treatment options are ineffective, which has led to an increased use of opioids.

About the Program

The Back Pain Consortium (BACPAC) Research Program is a translational, patient-centered effort to address the need for effective and personalized therapies for chronic low back pain. It will examine biomedical mechanisms within a biopsychosocial context by using interdisciplinary methods and exploring innovative technologies. 

The BACPAC Research Program will develop an integrated model of chronic low back pain by:

  • Using deep phenotyping to characterize people with chronic low back pain to improve understanding of the complex mechanisms underlying the condition
  • Identifying novel pathways and targets for intervention for the development of new therapeutic options to reduce pain and improve function
  • Developing precise diagnostic and treatment algorithms and then testing and refining them in clinical trials using new interventions and/or combination therapies so health care providers can tailor therapies to patients
  • Combining data from translational research and Phase 2 clinical trials to deliver an integrated model of back pain
  • Collaborating with the Early Phase Pain Investigation Clinical Network (EPPIC-Net) to test novel chronic low back pain interventions during clinical trials

Program Details

Through the Helping to End Addiction Long-termSM Initiative, or NIH HEAL InitiativeSM, NIH awarded 13 grants, totaling approximately $150 million, to form the BACPAC Research Program. The collaborative research program is composed of mechanistic research centers and technology sites combining translational research and Phase 2 clinical trials to deliver an integrated model of chronic low back pain. 

The Data Integration, Algorithm Development and Operations Management Center (DAC) will coordinate the BACPAC Research Program’s activities, ensure communication and accountability, and manage a registry of patient-reported outcomes. The DAC will also provide support for the design, development, implementation, monitoring, and analysis of clinical research that the program conducts. Using data from the BACPAC Research Program’s clinical studies, the DAC will develop patient-centered algorithms to predict optimized therapeutic interventions and lead to an integrated model of chronic lower back pain.

The Mechanistic Research Centers (MRCs) will conduct translational and clinical research projects that address critical gaps in chronic lower back pain characteristics and treatment. The MRCs will generate data from deep patient phenotyping, develop patient-based algorithms, and identify new targets for intervention.

Phase 2 clinical trials will test the safety and efficacy of complementary and alternative medicine approaches, non-addictive drugs, biologics, and devices that will relieve chronic lower back pain and improve physical function. The novel therapies tested in these trials will employ a biopsychosocial perspective on chronic low back pain.

The Technology Research Sites will develop, test, and distribute analytic tools, technologies, and methods to address chronic lower back pain. The BACPAC Research Program’s Phase 2 clinical trials or other collaborative clinical projects may further test new tools and methods.

BACPAC interfaces with EPPIC-Net to study how people with different sub-phenotypes of back pain respond to specific interventions.

Research Examples

The four components of the BACPAC Research Program will have specific tasks to develop novel and effective interventions for chronic lower back pain.

The DAC will:

  • Promote communications through public and private websites and social media to inform investigators, participants, and the public about the BACPAC Research Program’s activities.
  • Develop a web-based data management and integration system for data entry and storage.
  • Provide administrative support and coordination across the BACPAC Research Program for efforts such as regulatory compliance and document preparation.
  • Provide system-level analysis for BACPAC-generated, multidimensional datasets to produce an integrated model of low back pain.

The MRCs will:

  • Establish an Informatics Core that will manage data, develop new methodologies and algorithms for data analysis, and share data with other researchers in the BACPAC Research Program.
  • Recruit and retain participants for clinical studies.
  • Characterize chronic low back pain through clinical tests, such as quantitative sensory testing, brain imaging, and biomechanical testing of the spine.
  • Understand the diverse contributors to chronic lower back pain, such as behavioral and psychosocial factors.
  • Test various therapies and personalized care options for treating chronic lower back pain.

Phase 2 clinical trials will:

  • Test whether therapeutic virtual reality can improve chronic lower back pain symptoms and reduce opioid use.
  • Use antidepressants and fear-avoidance-based physical therapy to treat chronic lower back pain in participants with depression or anxiety disorders.
  • Administer pain-management behavioral therapies to participants with chronic lower back pain after lumbar spinal fusion surgery.

The Technology Research Sites will:

  • Test newly designed wearable technology to monitor and reduce chronic lower back pain.
  • Optimize current imaging techniques, such as magnetic resonance imaging and positron emission tomography scans, to establish measurable characteristics associated with chronic lower back pain.
  • Use participant data, such as spinal motion metrics and participant-reported outcomes, to create personalized treatment strategies.

BACPAC Data Integration, Algorithm Development and Operations Management Center

  • University of North Carolina at Chapel Hill – North Carolina

BACPAC Mechanistic Research Centers

  • University of California, San Francisco – California
  • University of Michigan at Ann Arbor – Michigan
  • University of Pittsburgh at Pittsburgh – Pennsylvania

BACPAC Phase 2 Clinical Trials

  • Cedars-Sinai Medical Center – California
  • University of Pittsburgh at Pittsburgh – Pennsylvania

BACPAC Technology Research Sites

  • Brigham Young University – Utah
  • Harvard University – Massachusetts
  • Massachusetts General Hospital – Massachusetts
  • Ohio State University – Ohio
  • University of California, San Francisco – California
  • University of Utah – Utah

Funded Projects

2019
Back Pain Consortium (BACPAC) Research Program Data Integration, Algorithm Development and Operations Management Center
Sep 26, 2019
2019
Development, Evaluation and Translation of Robotic Apparel for Alleviating Low Back Pain
Sep 26, 2019
2019
Focused Ultrasound Neuromodulation of Dorsal Root Ganglion for Noninvasive Mitigation of Low Back Pain
Sep 26, 2019
2019
HEALing LB3P: Profiling Biomechanical, Biological and Behavioral phenotypes
Sep 26, 2019
2019
Wearable nanocomposite sensor system for diagnosing mechanical sources of low back pain and guiding rehabilitation
Nov 19, 2019

Request for Information (RFI) on the NIH Back Pain Consortium (BACPAC) Research Program

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National Institutes of Health, Office of Extramural Research. Grants & Funding. NIH's central resource for grants and funding information.

Request for Information (RFI) on the NIH Back Pain Consortium (BACPAC) Research Program

This highly collaborative research program will be composed of mechanistic research centers and technology sites that will conduct translational research and phase 2 clinical trials to deliver an integrated model of back pain and patient-based algorithms to facilitate the identification of treatments tailored to the individual patient.

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