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Predicting anti-TNF treatment response in rheumatoid arthritis: An artificial intelligence-driven model using

Juan Luis Valdivieso Shephard1, Enrique Josue Alvarez Robles2, Carmen Cámara Hijón3

  • 1Immunology Unit, Hospital Universitario La Paz-Idipaz, 28046, Madrid, Spain.

Heliyon
|January 1, 2024
PubMed
Abstract

Insights

Researchers developed a predictive model using cytokines like IL-6 and IL-2 to identify rheumatoid arthritis (RA) patients likely to respond to anti-TNF drugs, improving treatment selection.

Area of Science:

  • Immunology
  • Rheumatology
  • Artificial Intelligence in Medicine

Background:

  • Rheumatoid arthritis (RA) treatment response varies significantly among patients.
  • Current anti-tumor necrosis factor (anti-TNF) therapies lack predictive markers for individual patient response.
  • This variability leads to treatment delays and increased healthcare costs.

Purpose of the Study:

  • To assess the feasibility of developing a predictive tool for anti-TNF therapy response in RA patients.
  • To utilize patient characteristics, clinical parameters, and cytokine profiles with artificial intelligence.

Main Methods:

  • Analysis of 38 RA patients from the RA-Paz cohort.
  • Measurement of baseline cytokine profiles (IL-1, IL-12, IL-10, IL-2, IL-4, IFNg, TNFa, IL-6) and clinical parameters (CRP, DAS-ESR).
  • Development and validation of logistic regression models using 5-fold cross-validation.

Main Results:

  • Statistically significant differences observed in IL-6, IL-2, CRP, and DAS-ESR between responders and non-responders.
  • The predictive model demonstrated acceptable performance with a ROC-AUC range of 0.804167 to 0.891667.
  • The findings suggest the potential for a clinical classification tool.

Conclusions:

  • A predictive model combining IL-6, IL-2, CRP, and DAS-ESR can effectively discriminate between RA patients who will remit and those who will not respond to anti-TNF therapy.
  • Further validation in larger cohorts is necessary to confirm these promising findings.
  • This approach could personalize RA treatment strategies.
Keywords:
Anti-TNF response predictionArtificial intelligenceCytokinesInterleukin 2Interleukin 6Rheumatoid arthritis

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