Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 May 14:17:1759720X251332224.
doi: 10.1177/1759720X251332224. eCollection 2025.

Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registries

David Castro Corredor  1 , Luis Ángel Calvo Pascual  2 , Eduardo Collantes-Estévez  3 , Clementina López-Medina  4   3
Affiliations

Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registries

David Castro Corredor et al. Ther Adv Musculoskelet Dis. .

Abstract

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.

Keywords: anti-TNF therapy; cross-validated mean accuracy; machine learning; mutual information; rheumatic diseases.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Mutual information test to compute the most relevant variables explaining the activity.
Figure 2.
Figure 2.
Random forest classifier with Bayesian optimization to compute the most relevant variables explaining the activity.
Figure 3.
Figure 3.
Top 20 features based on mean importance across feature selection methods.
Figure 4.
Figure 4.
Pruned decision tree explaining activity levels in patients treated with anti-TNF therapy depending on SF-12 physical component, ASQoL and SF-12 mental component. ASQoL, Ankylosing Spondylitis Quality of Life; TNF, tumour necrosis factor.
Figure 5.
Figure 5.
Confusion matrix of the pruned decision tree.
Figure 6.
Figure 6.
Confusion matrix: decision tree.
See this image and copyright information in PMC

References

    1. Menegatti S, Bianchi E, Rogge L. Anti-TNF therapy in spondyloarthritis and related diseases, impact on the immune system and prediction of treatment responses. Front Immunol 2019; 10: 382. - PMC - PubMed
    1. Pinto AS, Farisogullari B, Machado PM. Predictors of remission in people with axial spondyloarthritis: a systematic literature review. Semin Arthritis Rheum 2022; 56: 152078. - PubMed
    1. Wang R, Dasgupta A, Ward MM. Predicting probability of response to tumor necrosis factor inhibitors for individual patients with ankylosing spondylitis. JAMA Netw Open 2022; 5(3): e222312. - PMC - PubMed
    1. Castro Corredor D, Calvo Pascual LA. Imbalanced machine learning classification models for removal biosimilar drugs and increased activity in patients with rheumatic diseases. PLoS ONE 2023; 18(11): e0291891. - PMC - PubMed
    1. Calvo Pascual LÁ, Castro Corredor D, Garrido Merchán EC. Machine learning classification of vitamin D levels in spondyloarthritis patients. Intell Based Med 2024; 9: 100125.

LinkOut - more resources