Divergent treatment responses in chronic pain: Identifying subgroups of patients through cluster analysis.

Background: Chronic pain is an ill-defined disease with complex biopsychosocial aspects, posing treatment challenges. We hypothesize that treatment failure results, at least partly, from limited understanding of diverse patient subgroups. We aim to identify subgroups through psychometric data, allowing for more tailored interventions. Methods: For this 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 (HRQoL) and treatment efficacy were compared among clusters. A prediction model was built utilizing a minimum set of questions to reliably assess cluster allocation. Results: Among 5,454 patients with chronic pain, three clusters emerged. Cluster 1 (n=750) was characterized by high psychological burden, low HRQoL, lower educational levels and employment rates, and more smoking. Cluster 2 (n=1,795) showed low psychological burden, intermediate HRQoL, higher educational levels and employment rates, and more alcohol consumption. Cluster 3 (n=2,909) 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% in clusters 2 and 3. A model incorporating 15 psychometric questions reliably predicted cluster allocation. In conclusion, our study identifies distinct chronic pain patient clusters through 15 psychometric questions, revealing one cluster with notably poorer response to conventional treatment. Our prediction model may help clinicians improve treatment by allowing patient-subgroup targeted therapy according to cluster allocation.