Association between cluster analysis for multiple measures and International Classification of Diseases 11th revision as classification of chronic pain patients

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  • Kawai Aiko
    Department of Anesthesiology and Pain Medicine, Juntendo University Faculty of Medicine
  • Yamada Keiko
    Department of Anesthesiology and Pain Medicine, Juntendo University Faculty of Medicine Department of Psychology, McGill University, Montreal, Quebec
  • Hamaoka Saeko
    Department of Anesthesiology and Pain Medicine, Juntendo University Faculty of Medicine
  • Chiba Satoko
    Department of Anesthesiology and Pain Medicine, Juntendo University Faculty of Medicine
  • Wakaizumi Kentaro
    Shirley Ryan AbilityLab Northwestern University, Feinberg School of Medicine, Department of Physical Medicineand Rehabilitation, Chicago, IL
  • Yamaguchi Keisuke
    Department of Anesthesiology and Pain Medicine, Juntendo University Faculty of Medicine Department of Anesthesiology and Pain Medicine, Juntendo,Juntendo Tokyo Koto Geriatric Medical Center
  • Iseki Masako
    Department of Anesthesiology and Pain Medicine, Juntendo University Faculty of Medicine

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Other Title
  • 慢性疼痛患者分類として質問票スコアにクラスター分析を用いた手法と，国際疾病分類第11版（ICD–11）に基づく分類の関連

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<p>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.</p>

Journal

  • PAIN RESEARCH

    PAIN RESEARCH 35 (3), 141-153, 2020-09-30

    JAPANESE ASSOCIATION FOR STUDY OF PAIN

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