Terminologies and definitions used to classify patients with osteoarthritis: a scoping review

Objectives:

Osteoarthritis (OA), a prevalent and disabling condition, significantly burdens individuals and healthcare systems worldwide. It is characterized by joint pain, stiffness, and structural changes in cartilage, bone, and synovium. The clinical manifestations of OA vary widely, reflecting complex interactions among genetic, metabolic, biomechanical, and environmental factors. Despite progress in identifying OA clinical phenotypes, inconsistent terminology, including “phenotypes,” “subtypes,” and “subgroups,” hinders effective communication and research translation. This review aims to synthesize existing literature on clinical OA phenotypes, terminology, and definitions and propose a research agenda. 

Method: 

This scoping review followed PRISMA-ScR guidelines, focusing on publications from 2010 to 2023 investigating clinical phenotypes in adult OA patients. Searches were conducted in MEDLINE, SCOPUS, and EBSCOhost using combinations of terms related to clinical phenotypes in OA. Studies were screened, duplicates removed, and relevant data were charted and analyzed by two independent reviewers. 

Results: 

From 196 identified studies, 50 were included in the final analysis. Eight clinical phenotypes were categorized, including inflammatory, biomechanical, metabolic, and pain-sensitization. minimal joint disease, psychologically driven, menopause, severe radiographic. Most studies focused on knee OA, with limited exploration of hand, midfoot, and hip OA. Phenotype-based management strategies demonstrated potential for improving treatment outcomes and guiding research. 

Conclusion: 

Standardizing terminology and leveraging phenotype-based frameworks hold promise for advancing personalized OA care and research. Future efforts should focus on validating criteria, developing accessible diagnostic tools, and addressing understudied OA phenotypes. This work highlights the value of tailoring interventions to specific OA phenotypes for improved patient outcomes.