Establishing Central Sensitization–Related Symptom Severity Subgroups: A Multicountry Study Using the Central Sensitization Inventory

• Published in: Pain medicine (Malden, Mass.) Mass.), 2020-10, Vol.21 (10), p.2430-2440
• Main Authors: Cuesta-Vargas, Antonio I, Neblett, Randy, Nijs, Jo, Chiarotto, Alessandro, Kregel, Jeroen, van Wilgen, C Paul, Pitance, Laurent, Knezevic, Aleksandar, Gatchel, Robert J, Mayer, Tom G, Viti, Carlotta, Roldan-Jiménez, Cristina, Testa, Marco, Caumo, Wolnei, Jeremic-Knezevic, Milica, Nishigami, Tomohiko, Feliu-Soler, Albert, Pérez-Aranda, Adrián, Luciano, Juan V
• Format: Article
• Language: English
• Subjects: Central Nervous System Sensitization; Chronic illnesses; Chronic pain; Chronic Pain - diagnosis; Cluster analysis; Humans; Neurological disorders; Pain; Patients; Surveys
• ISSN: 1526-2375; 1526-4637
• DOI: 10.1093/pm/pnaa210

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.