ArticlePloS one2018
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).
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Unravelling the Connection Between Reduced Mobility and Mild Cognitive Impairment in Chronic Pain Patients Through Cluster Analysis.Advances in experimental medicine and biology · 2026Article
- Patterns of Pain in Middle-Aged and Older Adults in China: A Latent Class Analysis.Pain and therapy · 2025Article
- A qualitative interview study exploring the experiences of pain specialists on prescribing opioids for chronic non-cancer pain.Scientific reports · 2025Article
- Statistical Validation of Unsupervised Clustering for Adolescent TMD: A Cross-Sectional Study.Oral diseases · 2025Article
- Pain Phenotypes in Endometriosis: A Population-Based Study Using Latent Class Analysis.BJOG : an international journal of obstetrics and gynaecology · 2025Article
- Identifying and characterizing clinical subgroups in individuals with endometriosis.Frontiers in pain research (Lausanne, Switzerland) · 2025Article
- Putting pain into words: the psalms of lament as an aid for the alexithymic.Frontiers in pain research (Lausanne, Switzerland) · 2025Article
- Eleven neurology-related proteins measured in serum are positively correlated to the severity of diabetic neuropathy.Scientific reports · 2024Observational
- Longitudinal multisite study of the chronobiological control of chronic pain: the CircaHealth CircaPain study protocol.BMJ open · 2024Article
- Multidisciplinary strategies to treat painful mononeuropathies in the upper extremity: from lab to bedside.The Journal of hand surgery, European volume · 2024Review
- Prognostic subgroups of chronic pain patients using latent variable mixture modeling within a supervised machine learning framework.Scientific reports · 2024Article
- The Adaptive, Pain Sensitive, and Global Symptoms Clusters: Evidence from a Patient-Based Study.JDR clinical and translational research · 2024Article
- Acceptance and Fear-Avoidance Mediate Outcomes of Interdisciplinary Pain Rehabilitation Programs at 12-Month Follow-Up: A Clinical Registry-Based Longitudinal Cohort Study from the Swedish Quality Registry for Pain Rehabilitation (SQRP).Journal of pain research · 2024Article
- The new chronic pain MG30 category and diagnostic specificity in quality registries-problems and suggested solutions with special reference to Swedish Quality Registry for Pain rehabilitation (SQRP).Frontiers in pain research (Lausanne, Switzerland) · 2024Article
- Unsupervised learning for prognostic validity in patients with chronic pain in transdisciplinary pain care.Scientific reports · 2023Article
- Effects of interdisciplinary pain rehabilitation programs on neuropathic and non-neuropathic chronic pain conditions - a registry-based cohort study from Swedish Quality Registry for Pain Rehabilitation (SQRP).BMC musculoskeletal disorders · 2023Article
- Pain intensity and psychological distress show different associations with interference and lack of life control: A clinical registry-based cohort study of >40,000 chronic pain patients from SQRP.Frontiers in pain research (Lausanne, Switzerland) · 2023Article
- Applying the Rapid OPPERA Algorithm to Predict Persistent Pain Outcomes Among a Cohort of Women Undergoing Breast Cancer Surgery.The journal of pain · 2022Observational
- Profiles of Risk and Resilience in Chronic Pain: Loneliness, Social Support, Mindfulness, and Optimism Coming out of the First Pandemic Year.Pain medicine (Malden, Mass.) · 2022Observational
- Chronic back pain sub-grouped via psychosocial, brain and physical factors using machine learning.Scientific reports · 2022Article
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5 authors.
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Abstract
objectiveTo subgroup chronic pain patients using psychometric data and regress the variables most responsible for subgroup discrimination.
designCross-sectional, registry-based study. SETTING AND SUBJECTS: Chronic pain patients assessed at a multidisciplinary pain centre between 2008 and 2015.
methodsData 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.
resultsFour 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.
conclusionsThe 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.
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