Thursday, September 22nd
4:30pm-6:00pm EDT


Topical Workshop


Assessment, Diagnosis & Measurement of Pain


715 B

Signature for Pain Recovery IN Teens (SPRINT): Biomarker Signature Detection in a Multivariate Dataset Leveraging Supervised Machine Learning Algorithms

Up to 5% of adolescents suffer from debilitating, chronic musculoskeletal (MSK) pain affecting quality of life, school attendance, mood, and family function, and posing a significant economic burden. Only 40%-60% of adolescents with chronic MSK pain sustain significant improvements in clinical endpoints of pain severity and functional disability. Discovery of robust markers differentiating recovery versus persistence is essential to develop more resource efficient and patient-specific treatment strategies and to conceive novel approaches that benefit patients. Signature for Pain Recovery IN Teens (SPRINT), integrates four major domains: blood (immune markers), psychophysiologic (QST), imaging (brain structure and function), and patient report (demographic, physical, psychological) metrics. These selected metrics have robust associations with pain and function but have been limited by sample size, single metric assessment, and moderate effect sizes. This study represents the largest cohort of adolescents with chronic MSK pain deeply characterized to derive a prognostic biological signature of pain and functional recovery leveraging  a multivariate computational analysis pipeline which includes cross-validation, for the extraction of reliable results from this multilayered and complex dataset.


Dr. Laura Simons

Stanford University