Given our Center’s longstanding interests in funding rigorous research of complementary and integrative health approaches for pain, we are enthusiastic about a potential new translational research initiative that will address the need for effective and personalized therapies for chronic low back pain – the NIH Back Pain Research Consortium (BACPAC).
The National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) has published a Request for Information (RFI) to seek input from researchers, health care professionals, patient advocates and health advocacy organizations, scientific or professional organizations, federal agencies, and other interested members of the public for the BACPAC Research Program. You may submit your comments and suggestions through an online form until October 18, 2018.
The NIH is interested in receiving input on topics such as (but not limited to):
• Characteristics and availability of current ongoing and planned back pain cohorts, including opportunities and obstacles to combining data from existing and new cohorts;
• Availability and feasibility of clinical, laboratory-based and other approaches for deep phenotyping of patients with back pain;
• Availability and feasibility of developing new and improved patient-based back pain algorithms that predict long term outcomes and inform selection of most efficacious treatment for the individual patient;
• Research designs, including clinical trials, that might be appropriate to accomplish the development of data-driven algorithms for individualized treatment plans for those with chronic low back pain;
• Opportunities for development and testing of early stage technologies for diagnosis of low back pain;
• Availability and preliminary information about non-addictive pain medications, drugs, biologics, devices, complementary, biopsychosocial and other interventions that can be tested in Phase 2 trials in the next 2-3-year time frame;
• Opportunities and approaches to integrate data from biologic, molecular, imaging, biomechanical and other types of data into a dynamic model of back pain.