Data-driven clustering of chronic pain profiles using Swedish national registry data: Towards individualized decision support in interdisciplinary rehabilitation.

Abstract

Chronic pain affects 20-30% of adults and is a leading cause of disability and societal cost. Interdisciplinary, team-based treatment (IDT) is the most comprehensive approach, yet outcomes vary widely, and long-term benefits are, on average, modest. We aimed to develop clinically interpretable patient clusters from routine pre-treatment intake data and to validate them externally using independent national registry indicators, as a foundation for data-driven clinical decision support. We analyzed a nationwide cohort of 90,505 patients entering specialist IDT in Sweden. A theory-informed unsupervised approach was used to cluster biopsychosocial intake features from the Swedish Quality Registry for Pain Rehabilitation using k-means clustering. Internal validation assessed stability and separation, while external validation tested concordance between questionnaire-derived cluster structures and pre-intake sick-leave trajectories and medication prescriptions derived from national registers using the Mantel statistic and logistic regression. Eight distinct clusters were identified, characterized by differing constellations of pain severity, psychological distress, functional status, and pain duration. Registry indicators tracked with cluster burden: higher-severity clusters showed greater sick leave and more medication prescriptions. Concordance between questionnaire-based and registry-based distance matrices was moderate to strong (Mantel r = 0.65; p = 0.0016) and cluster membership was significantly associated with the registry-based features. Three pre-intake sick-leave trajectories (high/stable, medium/stable, and low/increasing) were observed and differed across clusters. Population-scale unsupervised clustering of routine patient-reported data, externally validated with independent national registries and supported by longitudinal sickness-absence patterns, yields clinically interpretable subgroups with strengthened construct validity. This provides a scalable foundation for patient stratification and the development of future clinical decision-support tools to better target and monitor IDT in real-world care.