Phenotype-based pain management in RA patients shows improved outcomes in retrospective cohort

IntroductionRheumatoid arthritis (RA) is a chronic inflammatory disease characterized by pain, functional disability, and comorbidities. Pain management in RA is complex due to both inflammatory and non-inflammatory mechanisms. The Nursing Science Precision Health (NSPH) model offers a personalized approach to pain management, integrating symptom measurement, phenotypic analysis, and biomarker data to guide tailored interventions.MethodsThe recorded data of 287 RA patients were retrospectively archived and categorized into three pain phenotypes: inflammatory pain, non-inflammatory pain, and mixed pain. Pain was assessed using the Visual Analog Scale (VAS), and biomarkers were measured at baseline. Psychological factors, including anxiety, depression, and sleep quality, were also evaluated. Patients’ phenotype-specific interventions were extracted from clinical records: pharmacological treatment for inflammatory pain, psychological counseling and mindfulness-based stress reduction for non-inflammatory pain, and combined therapies for mixed pain. Follow-up assessments were conducted at 12 weeks.ResultsSignificant improvements were observed across all pain phenotypes. Inflammatory pain patients showed reductions in CRP, ESR, and VAS pain scores. Non-inflammatory pain patients experienced reductions in anxiety, depression, and VAS scores, with improvements in sleep quality. Mixed pain patients benefited from both pharmacological and psychological interventions. Patient-reported outcomes, including quality of life and functional status, improved significantly, with 82.6% expressing satisfaction with their pain management plan.DiscussionThe NSPH model offers an effective framework for personalized RA pain management, demonstrating that phenotype-based interventions improve pain outcomes, reduce psychosocial distress, and enhance quality of life. This approach holds potential for broader application in chronic pain management and warrants further research to optimize its implementation.