IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0192623.html
   My bibliography  Save this article

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)

Author

Listed:
  • Emmanuel Bäckryd
  • Elisabeth B Persson
  • Annelie Inghilesi Larsson
  • Marcelo Rivano Fischer
  • Björn Gerdle

Abstract

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.

Suggested Citation

  • Emmanuel Bäckryd & Elisabeth B Persson & Annelie Inghilesi Larsson & Marcelo Rivano Fischer & Björn Gerdle, 2018. "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)," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-19, February.
  • Handle: RePEc:plo:pone00:0192623
    DOI: 10.1371/journal.pone.0192623
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0192623
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0192623&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0192623?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0192623. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.