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A Data-Driven Biopsychosocial Framework Determining the Spreading of Chronic Pain

Tanguay-Sabourin, C.; Fillingim, M.; Parisien, M.; Guglietti, G. V.; Zare, A.; Norman, J.; Da-ano, R.; Perez, J.; Thompson, S. J.; Martel, M. O.; Roy, M.; Diatchenko, L.; Vachon-Presseau, E.

2022-07-22 pain medicine
10.1101/2022.07.22.22277850 medRxiv
Show abstract

Chronic pain conditions are complex syndromes characterized by a mosaic of biological, psychological, and social factors. We derived predictive models for the number of co- existing pain sites in the UK Biobank and identified a common risk score that classified different chronic pain conditions in cross-sectional data, predicted the development of chronic pain in pain-free individuals, and determined the spreading of chronic pain to multiple sites or its recovery nine years later. The features with the strongest prognosis included sleeplessness, feeling fed-up, tiredness, stressful life events, and a BMI > 30. The risk score for pain was associated with an inflammatory blood marker, a polygenic risk score for pain, and a neuroimaging-based marker for sustained pain. The demonstration of a common biopsychosocial risk factor for different clinical pain conditions may help better characterize a general chronic pain syndrome, tailor research protocols, optimize patient randomization in clinical trials, and improve pain management.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.