Pain phenotypes in endometriosis: a population-based study using latent class analysis

Objective To identify phenotypes of pain in patients with endometriosis and to investigate their associations with predictors and quality of life (QoL). Design Population-based study. Setting A referral university center in Quebec City, Canada. Population or Sample A total of 352 patients aged 18‒50 years and diagnosed with endometriosis. Methods Latent class analysis (LCA) was used to identify pain phenotypes. To assess the associations, the three-step approach of LCA was applied. Main Outcome Measures Pain phenotypes, predictors of pain phenotypes, QoL. Results A total of 352 patients were included in the analyses. The diagnosis of endometriosis was either based on histology (N=135), imaging (N=106) or clinical presentation (N=111). The optimal model identified two distinct and homogeneous phenotypes of patients with endometriosis. The two groups had distinct clinical presentations, one with more severe and frequent pain symptoms and poorer quality of life (54%); the other with mild and less frequent pain symptoms (46%). Predictors of a high pain phenotype were a previous treatment failure, use of pain killers, a family history of endometriosis, a low annual family income, and pain comorbidities such as painful bladder, fibromyalgia, migraines, low back pain, irritable bowel syndrome, anxiety, and depression or mood disorders. The presence of endometrioma was predictive of the low pain phenotype. Phenotype membership was associated with distinct quality of life profiles (p<0.001). Conclusion Patients with endometriosis and pelvic pain can be grouped into two distinct and homogeneous phenotypes. Phenotypes membership correlates with quality of life and can be predicted with the patients’ characteristics. These findings will need to be validated in other populations and may inform the development of more specialized or personalized interventions based on the pain phenotypes.