Therapeutic advances in musculoskeletal disease · 2025
Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registries.
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Castro Corredor David, Calvo Pascual Luis Ángel, Collantes-Estévez Eduardo, López-Medina Clementina
Аннотация
BACKGROUND: The effectiveness of anti-tumour necrosis factor (TNF) therapy in spondyloarthritis is traditionally associated with factors such as age, obesity and disease subtypes. However, less-explored aspects, such as mental health, socioeconomic status and work type may also play a crucial role in determining inflammatory activity and therapeutic response.
OBJECTIVES: To identify the most significant factors explaining inflammatory activity levels in patients treated with anti-TNF therapy and to develop an interpretable machine-learning model with good performance and minimal overfitting.
DESIGN: This is an observational, cross-sectional and multicentre study with socio-demographical and clinical data extracted from the Registry of Spondyloarthritis of Spanish Rheumatology (REGISPONSER) and Ibero-American Registry of Spondyloarthropathies (RESPONDIA) registries.
METHODS: We selected patients receiving anti-TNF therapy and applied five feature selection methods to identify key factors. We evaluated these factors using 182 machine learning models, and, finally, we selected a decision tree model that offered comparable performance with reduced overfitting.
RESULTS: Activity levels appear strongly influenced by quality-of-life indicators, particularly the SF-12 physical and mental components and Ankylosing Spondylitis Quality of Life scores. While factors such as age, weight, years of treatment and age at diagnosis have relevance, they are not necessary to obtain a pruned tree with similar cross-validated mean accuracy.
CONCLUSION: Recognizing the central role of physical and mental well-being in managing disease activity can lead to better therapeutic strategies for chronic disease management.
OBJECTIVES: To identify the most significant factors explaining inflammatory activity levels in patients treated with anti-TNF therapy and to develop an interpretable machine-learning model with good performance and minimal overfitting.
DESIGN: This is an observational, cross-sectional and multicentre study with socio-demographical and clinical data extracted from the Registry of Spondyloarthritis of Spanish Rheumatology (REGISPONSER) and Ibero-American Registry of Spondyloarthropathies (RESPONDIA) registries.
METHODS: We selected patients receiving anti-TNF therapy and applied five feature selection methods to identify key factors. We evaluated these factors using 182 machine learning models, and, finally, we selected a decision tree model that offered comparable performance with reduced overfitting.
RESULTS: Activity levels appear strongly influenced by quality-of-life indicators, particularly the SF-12 physical and mental components and Ankylosing Spondylitis Quality of Life scores. While factors such as age, weight, years of treatment and age at diagnosis have relevance, they are not necessary to obtain a pruned tree with similar cross-validated mean accuracy.
CONCLUSION: Recognizing the central role of physical and mental well-being in managing disease activity can lead to better therapeutic strategies for chronic disease management.