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Classifying chronic pain using multidimensional pain-agnostic symptom assessments and clustering analysis
Gadi Gilam1, Eric M Cramer1, Kenneth A Webber1
1Division of Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Science Advances
|September 13, 2021
Summary
This study developed a new chronic pain classification system using patient symptoms. This cost-effective system can improve clinical care and prognosis for chronic pain patients.
Area of Science:
- Pain Medicine
- Clinical Psychology
- Biostatistics
Background:
- Chronic pain significantly impairs physical, mental, and social functioning.
- Current assessment methods may not fully capture the complexity of chronic pain.
- A need exists for a refined classification system for better patient management.
Purpose of the Study:
- To develop and validate a novel classification system for chronic pain.
- To identify distinct patient clusters based on multidimensional symptoms.
- To establish a system that aids in personalized diagnosis and prognosis.
Main Methods:
- Hierarchical clustering applied to a large cohort (11,448 patients) of treatment-seeking individuals.
- Utilized nine pain-agnostic symptoms for initial cluster analysis.
- Validated a three-cluster solution using independent measures in additional patient subsets (3817 and 1273 patients).
Main Results:
- A three-cluster solution was identified, representing a graded severity scale.
- Negative affect-related factors were significant predictors of cluster assignment.
- Baseline cluster assignment predicted follow-up assessments in a subset of patients.
Conclusions:
- A cost-effective chronic pain classification system was developed.
- The system offers potential for improved clinical care and patient prognosis.
- Findings suggest utility as markers for personalized pain management strategies.
