Up to 5% of adolescents (\~3.5 million in the US alone) suffer from high impact chronic musculoskeletal (MSK) pain, affecting all life domains and posing a significant economic burden. Current treatments for chronic MSK pain are suboptimal and have been tied to the opioid crisis. Only \~50% of adolescents with chronic MSK pain who present for multidisciplinary pain treatment recover, as measured by clinical endpoints of pain severity and functional disability. Discovery of robust markers of the recovery vs. persistence of pain and disability is essential to develop more resourceful and patient-specific treatment strategies and to conceive novel approaches that benefit patients who are refractory. Given that chronic pain is a biopsychosocial process, the discovery and validation of a prognostic and robust signature for pain recovery vs. persistence requires measurements across multiple dimensions in the same patient cohort in combination with a suitable 'big data' computational analysis pipeline for the extraction of reliable and cross-validated results from a multilayered and complex dataset. The research team is well positioned to execute the study aims with: (1) A highly skilled and experienced team of scientists and clinicians from Stanford University, University of Toronto/Hospital for Sick Children, and Cincinnati Children's Hospital Medical Center; (2) A standardized specimen collection, processing, storage, and distribution system, leveraging Stanford Biobank's platform, BioCatalyst, to aggregate the sample inventory with clinical annotations for an accessible, virtual biobank, within the Signature of Pain Recovery IN Teens (SPRINT) Biobank and Analysis Core (SBAC); (3) Cutting-edge preliminary data implicating novel candidates for neuroimaging, immune, quantitative sensory, and psychological markers for discovery; and (4) Expertise in machine learning approaches to extract reliable and prognostic bio-signatures from a large and complex data set. The research team expects that the results from this project will facilitate risk stratification in patients with chronic MSK, a more resourceful selection of patients who are likely to respond for undergoing current multidisciplinary pain treatment approaches, and new insight into biological and behavioral processes that may be exploited to develop novel strategies profiting those who are refractory.

For the R61/Discovery Phase Aim individuals will be thoroughly characterized via biological (i.e. brain structure and function, immune, sensory profiles), psychological state, and clinical endpoint (i.e., pain intensity, disability) data. Unbiased machine learning algorithms will identify a multivariate model comprised of the most prognostic biological, psychological, and clinical endpoints. The model will classify adolescents with and without resolving chronic MSK pain after a state-of-the art multidisciplinary pain treatment intervention. R33/Validation Phase Aim will validate the biological signature derived in the R61 study. This signature will be useful for a range of adolescent-based clinical trials in which identification of the highest risk individuals is necessary, providing a clinically actionable intervention algorithm.