Background & Aims
Anger is associated with adverse pain outcomes 1,2. Elevated expressions of anger reduce tolerance for experimental pain, increase reported pain intensity and reports of post-surgical pain, and is linked to higher analgesic intake 3,4. The link between anger and pain intensity extends beyond specific conditions 5, impacting individuals with chronic pain who exhibit heightened anger expression, perpetuating a cycle that worsens pain and leads to unfavorable outcomes (e.g., work disability) 1,2,6–10. However, individual differences in experiencing and expressing anger in the context of chronic pain remain unclear, along with their potential consequences on short-term and long-term pain-related symptoms. In the present study, we aimed to identify idiosyncratic anger profiles in chronic pain, using data-driven latent profile analysis (LPA) 11. We hypothesized that profiles will be clinically informative regarding chronic pain severity.
Methods
Data was collected using the learning health system of Stanford’s Collaborative Health Outcomes Information Registry (or CHOIR) that include a large range of chronic pain conditions 12 (n = 735; age = 51.56 ± 16.65; female = 70.34%). Anger was assessed using state-like 13, trait 14, expression 14, and cognitive-related perceived injustice domains of measurement 15, which assesses anger-related appraisals such as unfairness, associated with chronic pain. Severity of chronic pain was assessed using pain intensity 16, the number of body regions in pain, and pain interference, pain behavior, and physical function 13. LPA was computed and evaluated based on global fit indices, followed by ANOVAs and t-tests (Bonferroni corrected) to evaluate profile differences, and their relations to chronic pain severity. Six months after T1, a T2 data collection point was implemented to evaluate the long-term predictability of the latent-profile solution concerning chronic pain severity.
Results
A four-class solution (AIC = 33,618; BIC = 33,816; Entropy = 0.86; LMR = 312.86; 91.33% average classification accuracy) was most parsimonious, generating the following profiles: low-anger-low-injustice (49% of sample), medium-anger-low-injustice (20%), high-anger-high-injustice (7%), and low-anger-high-injustice (24%). Lowanger-low-injustice and medium-anger-low-injustice converged on all measures of chronic pain severity (ps > 0.05), demonstrating less severity compared to high-anger-high-injustice and low-anger-high-injustice profiles (ps > 0.05). Most results remained significant (ps < 0.05) above and beyond demographic factors, anxiety, and depression. Findings highlight unique contributions of anger to chronic pain. The analysis of the 4-profile solution regarding long-term predictability of chronic pain severity at the T2 data collection point is currently in progress. The findings will be included in the poster if it is accepted for presentation.
Conclusions
In summary, the fine-grained phenotyping of anger in chronic pain patients not only underscores the distinctive role of anger, surpassing commonly assessed negative-affect factors like anxiety and depression, but also directs our attention to the intricate and multi-dimensional nature of the anger construct, particularly in treatment contexts. This comprehensive approach contributes to an enhanced understanding of utilizing patient-reported outcomes, both in clinical settings and research, extending beyond anger to potentially inform the assessment of other measures. Finally, the longitudinal analysis has the potential to inform individual differences in long-term care, shedding light on personalized approaches and strategies tailored to meet the unique needs and trajectories of individuals over an extended period. Taken together, this research offers a significant and stimulating contribution to the clinical and research community of the IASP.
References
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Presenting Author
Marine Granjon
Poster Authors
Marine Granjon
PhD
University of Jerusalem
Lead Author
Noel A. Vest
Ph.D.
Boston University School of Public Health
Lead Author
Sean Mackey
Stanford University
Lead Author
Gadi Gilam
Institute of Biomedical and Oral Research, Faculty of Dental Medicine, Hebrew University of Jerusale
Lead Author
Topics
- Mechanisms: Psychosocial and Biopsychosocial