Identifying patient subgroups in the heterogeneous chronic pain population using cluster analysis

Rijsdijk, M; Smits, HM; Azizoglu, HR; Brugman, S; van de Burgt, Y; Charldorp, TCV; Gelder, DJV; Grauw, JCD; Lange, EAV; Meye, FJ; Strick, M; Walravens, HWA; Winkens, LHH; Huygen, FJPM; Drylewicz, J; Willemen, HLDM

Abstract

Chronic pain is an ill-defined disease with complex biopsychosocial aspects, posing treatment challenges. We hypothesized that treatment failure results, at least partly, from limited understanding of diverse patient subgroups. We aimed to identify subgroups using psychological variables, allowing for more tailored interventions. In a retrospective cohort study, we extracted patient-reported data from two Dutch tertiary multidisciplinary outpatient pain clinics (2018-2023) for unsupervised hierarchical clustering. Clusters were defined by anxiety, depression, pain catastrophizing, and kinesiophobia. Sociodemographics, pain characteristics, diagnosis, lifestyle, health-related quality of life and treatment efficacy were compared among clusters. A prediction model was built utilizing a minimum set of questions to reliably assess cluster allocation. Among 5466 patients with chronic pain, three clusters emerged. Cluster 1 (n=750) was characterized by high psychological burden, low health- related quality of life, lower educational levels and employment rates, and more smoking. Cluster 2 (n=1795) showed low psychological burden, intermediate health-related quality of life, higher educational levels and employment rates, and more alcohol consumption. Cluster 3 (n=2909) showed intermediate features. Pain reduction following treatment was least in cluster 1 (28.6% after capsaicin patch, 18.2% after multidisciplinary treatment), compared to >50% for both treatments in clusters 2 and 3. A model incorporating 15 psychometric questions reliably predicted cluster allocation. In conclusion, our study identified distinct chronic pain patient clusters through 15 psychological questions, revealing one cluster with notably poorer response to conventional treatment. Our prediction model, integrated in a web-based tool, may help clinicians improve treatment by allowing patient-subgroup targeted therapy according to cluster allocation. Perspective: Hierarchical clustering of chronic pain patients identified three subgroups with similar pain intensity and diagnoses but distinct psychosocial traits. One group with higher psychological burden showed poorer treatment outcomes. A web-based tool using this model could help clinicians tailor therapies by matching interventions to specific patient subgroups for improved outcomes.

Citation

Rijsdijk, M., Smits, H., Azizoglu, H., Brugman, S., van de Burgt, Y., Charldorp, T., Gelder, D., Grauw, J., Lange, E., Meye, F., Strick, M., Walravens, H., Winkens, L., Huygen, F., Drylewicz, J., & Willemen, H. (2025). Identifying patient subgroups in the heterogeneous chronic pain population using cluster analysis. The Journal of Pain, 28, https://doi.org/10.1016/j.jpain.2025.104792

• Journal Article Type: Article
• Acceptance Date: Jan 18, 2025
• Online Publication Date: Jan 22, 2025
• Publication Date: 2025
• Deposit Date: Mar 14, 2025
• Publicly Available Date: Mar 14, 2025
• Journal: The Journal of Pain
• Print ISSN: 1526-5900
• Electronic ISSN: 1526-5900
• Publisher: Elsevier
• Peer Reviewed: Not Peer Reviewed
• Volume: 28
• DOI: https://doi.org/10.1016/j.jpain.2025.104792
• Keywords: Chronic pain; Phenotyping; Cluster analysis; Patient-reported measures; Patient stratification; DIAGNOSTIC QUESTIONNAIRE; CATASTROPHIZING SCALE; UNITED-STATES; BACK-PAIN; VALIDATION; EDUCATION; ADULTS; HEALTH