Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registries
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 i...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-05-01
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| Series: | Therapeutic Advances in Musculoskeletal Disease |
| Online Access: | https://doi.org/10.1177/1759720X251332224 |
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| author | David Castro Corredor Luis Ángel Calvo Pascual Eduardo Collantes-Estévez Clementina López-Medina |
| author_facet | David Castro Corredor Luis Ángel Calvo Pascual Eduardo Collantes-Estévez Clementina López-Medina |
| author_sort | David Castro Corredor |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dafc25e46774425db0c033950ea5546f |
| institution | DOAJ |
| issn | 1759-7218 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Therapeutic Advances in Musculoskeletal Disease |
| spelling | doaj-art-dafc25e46774425db0c033950ea5546f2025-08-20T03:10:03ZengSAGE PublishingTherapeutic Advances in Musculoskeletal Disease1759-72182025-05-011710.1177/1759720X251332224Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registriesDavid Castro CorredorLuis Ángel Calvo PascualEduardo Collantes-EstévezClementina López-MedinaBackground: 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.https://doi.org/10.1177/1759720X251332224 |
| spellingShingle | David Castro Corredor Luis Ángel Calvo Pascual Eduardo Collantes-Estévez Clementina López-Medina Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registries Therapeutic Advances in Musculoskeletal Disease |
| title | Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registries |
| title_full | Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registries |
| title_fullStr | Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registries |
| title_full_unstemmed | Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registries |
| title_short | Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registries |
| title_sort | inflammatory activity levels on patients with anti tnf therapy most important factors and a decision tree model based on regisponser and respondia registries |
| url | https://doi.org/10.1177/1759720X251332224 |
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