Antonio I Cuesta-Vargas1,2, Randy Neblett3, Jo Nijs4,5, Alessandro Chiarotto6,7, Jeroen Kregel8,9, C Paul van Wilgen4,5,10, Laurent Pitance11,12, Aleksandar Knezevic13,14, Robert J Gatchel9,15, Tom G Mayer16, Carlotta Viti17,18,19, Cristina Roldan-Jiménez1, Marco Testa20, Wolnei Caumo21,22,23, Milica Jeremic-Knezevic13, Tomohiko Nishigami24, Albert Feliu-Soler25,26, Adrián Pérez-Aranda25,26, Juan V Luciano25,26. 1. Department of Physiotherapy of the Faculty of Health Science at the, University of Malaga, (IBIMA), Malaga, Spain. 2. Faculty of Health at the Queensland University of Technology, Brisbane, Australia. 3. PRIDE Research Foundation, Dallas, Texas, USA. 4. Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education & Physiotherapy, Pain in Motion International Research Group, Vrije Universiteit Brussel, Brussels, Belgium. 5. Department of Physical Medicine and Physiotherapy, University Hospital Brussels, Brussels, Belgium. 6. Department of Health Sciences, Amsterdam Movement Sciences Research Institute, VU University, Amsterdam, the Netherlands. 7. Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands. 8. Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium. 9. Pain in Motion International Research Group, Vrije Universiteit, Brussels, Belgium. 10. Transcare, Transdisciplinary Pain Center, the Netherlands. 11. Neuro Musculoskeletal Lab, Institute of Clinical Research (IREC), Université Catholique de Louvain, Brussels, Belgium. 12. Department of Oral and Maxillofacial Surgery, Cliniques Universitaires Saint-Luc, Brussels, Belgium. 13. Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia. 14. Medical Rehabilitation Clinic, Clinical Centre of Vojvodina, Novi Sad, Serbia. 15. Department of Psychology, College of Science, University of Texas, Arlington, Texas, USA. 16. Department of Orthopedic Surgery, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, USA. 17. FACEit, Italian Association of Integrated Therapy for Cervico-Cranio-Facial Pain and Dysfunction, Barlassina, Italy. 18. Department of Biomedical Sciences, University of Padova, Padova, Italy. 19. Studio Fisioterapico Viti, Bologna, Italy. 20. Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Campus of Savona, Savona, Italy. 21. Post-Graduate Program in Medical Sciences, School of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil. 22. Pain and Palliative Care Service at Hospital de Clínicas de Porto Alegre (HCPA), Laboratory of Pain and Neuromodulation at UFRGS, Porto Alegre, Brazil. 23. Pain and Anesthesia in Surgery Department, School of Medicine, UFRGS, Porto Alegre, Brazil. 24. Department of Physical Therapy, Faculty of Health and Welfare, Prefectural University of Hiroshima, Hiroshima, Japan. 25. Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain. 26. Teaching, Research & Innovation Unit, Parc Sanitari Sant Joan de Déu, St. Boi de Llobregat, Spain.
Abstract
OBJECTIVES: The goal of this study was to identify central sensitization-related symptom severity subgroups in a large multicountry sample composed of patients with chronic pain and pain-free individuals using the Central Sensitization Inventory (CSI). METHODS: A large, pooled international (N = 8 countries) sample of chronic pain patients plus healthy subjects (total N = 2,620) was randomly divided into two subsamples for cross-validation purposes. First, a hierarchical cluster analysis (HCA) was performed using CSI item-level data as clustering variables (test sample; N = 1,312). Second, a latent profile analysis (LPA) was conducted to confirm the optimal number of CSI clusters (validation sample; N = 1,308). Finally, to promote implementation in real-world clinical practice, we built a free online Central Sensitization Inventory Symptom Severity Calculator. RESULTS: In both HCA (N = 1,219 valid cases) and LPA (N = 1,245 valid cases) analyses, a three-cluster and three-profile solution, respectively, emerged as the most statistically optimal and clinically meaningful. Clusters were labeled as follows: (i) Low Level of CS-Related Symptom Severity, (ii) Medium Level of CS-Related Symptom Severity, and (iii) High Level of CS-Related Symptom Severity. CONCLUSIONS: Our results indicated that a three-cluster solution clearly captured the heterogeneity of the CSI data. The calculator might provide an efficient way of classifying subjects into the cluster groups. Future studies should analyze the extent to which the CSI cluster classification correlates with other patient-reported and objective signs and symptoms of CS in patients with chronic pain, their associations with clinical outcomes, health-related costs, biomarkers, (etc.), and responsiveness to treatment.
OBJECTIVES: The goal of this study was to identify central sensitization-related symptom severity subgroups in a large multicountry sample composed of patients with chronic pain and pain-free individuals using the Central Sensitization Inventory (CSI). METHODS: A large, pooled international (N = 8 countries) sample of chronic painpatients plus healthy subjects (total N = 2,620) was randomly divided into two subsamples for cross-validation purposes. First, a hierarchical cluster analysis (HCA) was performed using CSI item-level data as clustering variables (test sample; N = 1,312). Second, a latent profile analysis (LPA) was conducted to confirm the optimal number of CSI clusters (validation sample; N = 1,308). Finally, to promote implementation in real-world clinical practice, we built a free online Central Sensitization Inventory Symptom Severity Calculator. RESULTS: In both HCA (N = 1,219 valid cases) and LPA (N = 1,245 valid cases) analyses, a three-cluster and three-profile solution, respectively, emerged as the most statistically optimal and clinically meaningful. Clusters were labeled as follows: (i) Low Level of CS-Related Symptom Severity, (ii) Medium Level of CS-Related Symptom Severity, and (iii) High Level of CS-Related Symptom Severity. CONCLUSIONS: Our results indicated that a three-cluster solution clearly captured the heterogeneity of the CSI data. The calculator might provide an efficient way of classifying subjects into the cluster groups. Future studies should analyze the extent to which the CSI cluster classification correlates with other patient-reported and objective signs and symptoms of CS in patients with chronic pain, their associations with clinical outcomes, health-related costs, biomarkers, (etc.), and responsiveness to treatment.
Authors: Xiao Jing Wang; Jon O Ebbert; Elizabeth A Gilman; Jordan K Rosedahl; Priya Ramar; Lindsey M Philpot Journal: J Prim Care Community Health Date: 2021 Jan-Dec