Data-driven identification of distinct pain drawing patterns and their association with clinical and psychological factors: a study of 21,123 patients with spinal pain

Natalie Hong Siu Chang; Casper Nim; Steen Harsted; James J Young; Søren O'Neill

doi:10.1097/j.pain.0000000000003261

Data-driven identification of distinct pain drawing patterns and their association with clinical and psychological factors: a study of 21,123 patients with spinal pain

Pain. 2024 May 15. doi: 10.1097/j.pain.0000000000003261. Online ahead of print.

Authors

Natalie Hong Siu Chang^{1

2}, Casper Nim^{1

2

3}, Steen Harsted^{1

3}, James J Young^{3

4}, Søren O'Neill^{1

2}

Affiliations

¹ Medical Spinal Research Unit, Spine Centre of Southern Denmark, University Hospital of Southern Denmark, Middelfart, Denmark.
² Department of Regional Health Research, University of Southern Denmark, Odense, Denmark.
³ Center for Muscle and Joint Health, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark.
⁴ Schroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, Canada.

PMID: 38743560
DOI: 10.1097/j.pain.0000000000003261

Abstract

The variability in pain drawing styles and analysis methods has raised concerns about the reliability of pain drawings as a screening tool for nonpain symptoms. In this study, a data-driven approach to pain drawing analysis has been used to enhance the reliability. The aim was to identify distinct clusters of pain patterns by using latent class analysis (LCA) on 46 predefined anatomical areas of a freehand digital pain drawing. Clusters were described in the clinical domains of activity limitation, pain intensity, and psychological factors. A total of 21,123 individuals were included from 2 subgroups by primary pain complaint (low back pain (LBP) [n = 15,465]) or midback/neck pain (MBPNP) [n = 5658]). Five clusters were identified for the LBP subgroup: LBP and radiating pain (19.9%), radiating pain (25.8%), local LBP (24.8%), LBP and whole leg pain (18.7%), and widespread pain (10.8%). Four clusters were identified for the MBPNP subgroup: MBPNP bilateral posterior (19.9%), MBPNP unilateral posterior + anterior (23.6%), MBPNP unilateral posterior (45.4%), and widespread pain (11.1%). The clusters derived by LCA corresponded to common, specific, and recognizable clinical presentations. Statistically significant differences were found between these clusters in every self-reported health domain. Similarly, for both LBP and MBPNP, pain drawings involving more extensive pain areas were associated with higher activity limitation, more intense pain, and more psychological distress. This study presents a versatile data-driven approach for analyzing pain drawings to assist in managing spinal pain.