Central sensitization (CS) and psychological factors are associated with pain intensity. However, it has remained unclear whether the effects of central sensitivity syndromes and cognitive ⁄ emotional factors differ depending on the severity of pain and the pain quality. Our purposes were to perform subgrouping based on central sensitivity syndromes and pain intensity, and to clarify the difference in central sensitization syndrome and pain intensity between subgroups.

Fifty–nine patients with musculoskeletal pain were included in this cross–sectional study. Pain intensity and psychological problems were assessed with Central sensitization inventory (CSI–9), Short–Form McGill Pain Questionnaire 2 (SFMPQ2), Hospital Anxiety and Depression Scale (HADS), Pain Catastrophizing Scale (PCS–4). The cluster analysis with a ward method was used to divide patients into subgroups based on central sensitization syndrome and pain intensity. In addition, Kruskal–Wallis test, multiple comparison (Bonferroni method), and Fisher’s exact test were performed to compare clinical outcomes between subgroups. The level of significance was set at 5％.

The cluster analysis classified into three subgroups. One subgroup of patients (n=11) was characterized by high level of central sensitivity syndromes, pain intensity and psychological problems. A second subgroup (n=19) was characterized by low level of central sensitivity syndromes, moderate level of pain intensity, high level of psychological problems. The third subgroup (n=29) was characterized by low level of central sensitivity syndromes, pain intensity and psychological problems. That is, one subgroup was mainly affected with central sensitivity syndromes, and another subgroup was affected psychological factors. These results indicated the differences in pain mechanism among subgroups.

Cluster analysis can classify patients with chronic pain using multiple scales, and classification of chronic pain will be adopted in the International Classification of Diseases 11th revision (ICD–11) in 2022. In the present study, we aimed to investigate whether cluster analysis was practical for classifying chronic pain and to determine the association between these two classifications for chronic pain. This study included 229 patients with chronic pain who completed a self–reported questionnaire at the first visit to a pain clinic in a university hospital. Patients were clustered using a two–step cluster analysis (TSCA), a machine learning method, for the scores of nine questionnaires. Thereafter, the proportions of clusters among major and several minor classifications were tested using the analysis of covariance adjusted for age and doctor. The following three clusters were calculated using TSCA: mild, moderate, and severe symptoms. Among the major classifications of chronic pain in ICD–11, the distribution of clusters significantly differed, but the proportions of these three clusters in each chronic pain classification did not differ. Our findings suggested that TSCA for multiple measures may be a better approach for the classification of chronic pain, but its classification is not associated with the classification of chronic pain in ICD–11.