Chronic pain patients can be classified into four groups: Clustering-based discriminant analysis of psychometric data from 4665 patients referred to a multidisciplinary pain centre (a SQRP study)

Research output: Contribution to journalArticle


Objective To subgroup chronic pain patients using psychometric data and regress the variables most responsible for subgroup discrimination. Design Cross-sectional, registry-based study. Setting and subjects Chronic pain patients assessed at a multidisciplinary pain centre between 2008 and 2015. Methods Data from the Swedish quality registry for pain rehabilitation (SQRP) were retrieved and analysed by principal component analysis, hierarchical clustering analysis, and partial least squares-discriminant analysis. Results Four subgroups were identified. Group 1 was characterized by low "psychological strain", the best relative situation concerning pain characteristics (intensity and spreading), the lowest frequency of fibromyalgia, as well as by a slightly older age. Group 2 was characterized by high "psychological strain" and by the most negative situation with respect to pain characteristics (intensity and spreading). Group 3 was characterized by high "social distress", the longest pain durations, and a statistically higher frequency of females. The frequency of three neuropathic pain conditions was generally lower in this group. Group 4 was characterized by high psychological strain, low "social distress", and high pain intensity. Conclusions The identification of these four clusters of chronic pain patients could be useful for the development of personalized rehabilitation programs. For example, the identification of a subgroup characterized mainly by high perceived "social distress" raises the question of how to best design interventions for such patients. Differentiating between clinically important subgroups and comparing how these subgroups respond to interventions is arguably an important area for further research.


External organisations
  • Skåne University Hospital
  • Linköping University
  • Quality Stat AB
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Other Health Sciences
Original languageEnglish
Article numbere0192623
JournalPLoS ONE
Issue number2
Publication statusPublished - 2018 Feb 1
Publication categoryResearch