Chronic pain conditions present in various forms, yet all feature symptomatic impairments in physical, mental, and social domains. Rather than assessing symptoms as manifestations of illness, we used them to develop a chronic pain classification system. A cohort of real-world treatment-seeking patients completed a multidimensional patient-reported registry as part of a routine initial evaluation in a multidisciplinary academic pain clinic. We applied hierarchical clustering on a training subset of 11,448 patients using nine pain-agnostic symptoms. We then validated a three-cluster solution reflecting a graded scale of severity across all symptoms and eight independent pain-specific measures in additional subsets of 3817 and 1273 patients. Negative affect–related factors were key determinants of cluster assignment. The smallest subset included follow-up assessments that were predicted by baseline cluster assignment. Findings provide a cost-effective classification system that promises to improve clinical care and alleviate suffering by providing putative markers for personalized diagnosis and prognosis.
Bibliographical noteFunding Information:
This study was supported by The Redlich Pain Research Endowment (S.C.M.) and The Feldman Family Foundation Pain Research Fund (S.C.M. and G.G.). The following NIH grants provided further support: R61 NS11865 (S.C.M.), R01 NS109450 (S.C.M.), K24 DA029262 (S.C.M.), P01 AT006651 (S.C.M.), K23 NS104211 (K.A.W.), L30 NS108301 (K.A.W.), and K23 DA047473 (M.S.Z.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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