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Updated: May 17, 2026
Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery
Published on: April 14, 2016
Data-driven clustering of chronic pain profiles using Swedish national registry data: Towards individualized decision
Ilias Thomas1, Roger Nyberg1, Riccardo LoMartire2
1School of Information and Engineering, Dalarna University, Borlänge, Sweden.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
