TY - JOUR
T1 - Establishing Central Sensitization-Related Symptom Severity Subgroups
T2 - A Multicountry Study Using the Central Sensitization Inventory
AU - Cuesta-Vargas, Antonio I.
AU - Neblett, Randy
AU - Nijs, Jo
AU - Chiarotto, Alessandro
AU - Kregel, Jeroen
AU - van Wilgen, C. Paul
AU - Pitance, Laurent
AU - Knezevic, Aleksandar
AU - Gatchel, Robert J.
AU - Mayer, Tom G.
AU - Viti, Carlotta
AU - Roldan-Jiménez, Cristina
AU - Testa, Marco
AU - Caumo, Wolnei
AU - Jeremic-Knezevic, Milica
AU - Nishigami, Tomohiko
AU - Feliu-Soler, Albert
AU - Pérez-Aranda, Adrián
AU - Luciano, Juan V.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Central Sensitivity Syndrome
KW - Central Sensitization
KW - Central Sensitization Inventory
KW - Chronic Pain
KW - Hierarchical Cluster Analysis
KW - Latent Profile Analysis
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UR - http://www.scopus.com/inward/citedby.url?scp=85094935145&partnerID=8YFLogxK
U2 - 10.1093/pm/pnaa210
DO - 10.1093/pm/pnaa210
M3 - Article
C2 - 33118603
AN - SCOPUS:85094935145
VL - 21
SP - 2430
EP - 2440
JO - Pain medicine
JF - Pain medicine
SN - 1526-2375
IS - 10
ER -