Redefining comorbidity understanding in rheumatoid arthritis through novel approaches using real-world data

Abstract

Rheumatoid arthritis (RA) is a prevalent chronic disease that is associated with numerous comorbidities. Accurate assessment of these coexisting conditions, as reported by clinicians, is critical for an improved understanding of the impact of the disease and patient care. This perspective aims to evaluate the utility of real-world data (RWD) for enhancing the understanding of comorbidities in RA and to assess its potential in reshaping clinical management. RWD approaches, specifically the use of structured databases or data extracted from electronic health records, offer promising alternatives to overcome the limitations of traditional methodologies. Structured databases provide a systematic approach to data analysis, utilizing diagnosis codes to study large patient cohorts, revealing the prevalence of conditions, and demonstrating the potential for long-term disease trend analysis. Meanwhile, natural language processing (NLP) and artificial intelligence (AI) image analysis can bridge the gap between structured and unstructured data, by extracting meaningful information from unstructured fields such as free text or imaging. NLP has proven effective in the identification of RA patients and research outcomes, while AI image analysis has enabled the discovery of hidden findings in cardiovascular assessments, establishing a basis for the assessment of comorbidities in RA. However, while the benefits of using RWD are substantial, challenges remain. Ensuring comprehensive data capture, managing missing data, and improving data detection are key areas requiring attention. The involvement of clinicians and researchers in rheumatology is crucial in unlocking the potential of RWD studies, offering the promise of significant improvements in disease characterization and patient health outcomes.