Development of a Central Sensitization Inventory short form using data from twenty-three countries

The Central Sensitization Inventory (CSI) assesses central sensitization-related symptoms, but its 25-item length may limit its use in large-scale research or routine clinical practice. Therefore, the aim was to develop a short version of the CSI, using a large international sample of 7862 participants across 23 countries. A multi-step Rasch analysis was applied to the 25-item version of the CSI using a training-validation paradigm to identify the subset of items that best fit a unidimensional model. Then, an expert committee reviewed the face-content validity of each item. A 7-item solution was ultimately developed (Chi-Square Value = 794.728, df = 20; p-value < 0.001; CFI = 0.96; TLI = 0.96; RMSEA = 0.09, 90%CI [0.09-0.10]; SRMR= 0.05). Internal consistency was adequate for both the CSI-25 (α = 0.93; ω = 0.93) and CSI-7 (α = 0.85; ω = 0.85). Both versions demonstrated strong discriminative validity in identifying subgroups with presumed different levels of central sensitization-related symptoms. As expected, CSI-25 and CSI-7 scores increased progressively from healthy control participants (who scored lowest) to single-site non-spinal chronic pain, chronic spinal pain, multi-site chronic pain, and fibromyalgia (who scored highest). CSI-7 Receiver Operating Characteristic curves showed excellent sensitivity and specificity, particularly in differentiating fibromyalgia from healthy control participants (area under the curve = 0.98; sensitivity of 92% and specificity of 93%). CSI-7 severity levels were empirically derived to aid clinical interpretation. To sum up, the CSI-7 offers an efficient, unidimensional, and internally consistent alternative to the CSI-25 for international use. PERSPECTIVE: The CSI-7 offers a brief, clinically useful screening tool for identifying central sensitization-related symptoms across diverse pain conditions. Derived from international data, it retains the psychometric strength of the full CSI while reducing patient burden, supporting efficient assessment in busy clinical and research settings.