Online ISSN : 2187-4697
Print ISSN : 0915-8588
ISSN-L : 0915-8588
河合 愛子山田 恵子濱岡 早枝子千葉 聡子若泉 謙太山口 敬介井関 雅子
ジャーナル フリー

2020 年 35 巻 3 号 p. 141-153


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.

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