Abstract
Chronic low back pain (cLBP) is a complex, multifaceted disorder where biological, psychological, and social factors affect its onset and trajectory. Consequently, cLBP encompasses many different disease variants, with multiple patient-specific mechanisms. The goal of NIH Back Pain Consortium (BACPAC) Research Program is to develop understanding of cLBP mechanisms and to develop algorithms that optimally match specific treatments to individual patients. To accomplish this, one research activity of BACPAC is to develop theoretical models for chronic low back pain based on the current state of knowledge in the scientific community, and to interrogate the relationships implied by the theoretical models using data generated by or available to BACPAC. The models consider biopsychosocial perspectives, and encompass both peripheral (i.e. low back) and central (i.e. spinal and supra-spinal) factors as well as proposed mechanisms of action of cLBP treatments. However, absent explanations, models/algorithms may fall short of regulatory requirements and clinician expectations, and ultimately may not be embraced by physicians and patients. To address this, BACPAC is developing a clinical utility roadmap (CUR) to clarify how models will be used in practice for selecting optimal treatments, monitoring response to treatment, and reducing health care utilization. This presentation will review the goals of BACPAC and how theoretical models and CUR are being used to support computational knowledge networks to integrate data from deeply phenotyped cLBP patients.
