Classifying chronic pain using multidimensional pain-agnostic symptom assessments and clustering analysis

Gadi Gilam*, Eric M. Cramer, Kenneth A. Webber, Maisa S. Ziadni, Ming Chih Kao, Sean C. Mackey

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageAmerican English
Article numbereabj0320
JournalScience advances
Issue number37
StatePublished - 10 Sep 2021
Externally publishedYes

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