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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 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 To assess the clinical validity and factor structure of the Fear-Avoidance Components Scale (FACS), a new fear-avoidance (FA) measure. Methods: In this study, 426 chronic musculoskeletal pain disorder (CMPD) patients were admitted to a functional restoration program (FRP). They were categorized into five FACS severity levels, from Subclinical to Extreme, at admission, and again at discharge. Associations with objective lifting performance and other patient-reported psychosocial measures were determined at admission and discharge, and objective work outcomes for this predominantly disabled cohort, were assessed one-year later. Results Those patients in the Severe and Extreme FACS severity groups at admission were more likely to “drop out” of treatment than those in the lower severity groups (P=0.05). At both admission and discharge, the FACS severity groups were highly and inversely correlated with objective lifting performance and patient-reported FA-related psychosocial variables, including kinesiophobia, pain intensity, depressive symptoms, perceived disability, perceived injustice, and insomnia (Ps<0.001). All variables showed improvement at FRP discharge. Patients in the Extreme FACS severity group at discharge were less likely to return to, or retain, work one-year later (P≤0.02). The factor analysis identified a two-factor solution. Discussion Strong associations were found among FACS scores and other patient-reported psychosocial and objective lifting performance variables at both admission and discharge. High discharge FACS scores were associated with worse work outcomes one-year after discharge. The FACS appears to be a valid and clinically useful measure for predicting attendance, physical performance, distress and relevant work outcomes in FRP treatment of CMPD patients.