Advances in Pediatric Rheumatology: Focus on Juvenile Idiopathic Arthritis

A special issue of Children (ISSN 2227-9067). This special issue belongs to the section "Pediatric Allergy and Immunology".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 297

Special Issue Editor


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Guest Editor
2nd Department of Paediatrics, Semmelweis University, Budapest, Hungary
Interests: pediatric rheumatology; juvenile idiopathic arthritis; juvenile systemic sclerosis

Special Issue Information

Dear Colleagues,

Recent developments in pediatric rheumatology have significantly advanced the understanding and treatment of juvenile idiopathic arthritis (JIA), and biomarkers are crucial in guiding diagnosis and therapeutic decisions. Notable biomarkers such as MRP8/14 (S100A8/A9) have emerged as valuable tools to monitor disease activity, particularly in systemic JIA (sJIA). Elevated levels of MRP8/14 can predict flares and are closely related to inflammation in active disease. Similarly, interleukin-18 (IL-18) has shown promise as a biomarker, especially in systemic JIA, where high levels of IL-18 correlate with disease severity and macrophage activation syndrome (MAS), a life-threatening complication of JIA.

In terms of complications, uveitis, a common extra-articular manifestation in JIA, has seen improved management through early biologic interventions. Adalimumab, a TNF inhibitor, has been particularly effective in controlling JIA-associated uveitis resistant to conventional treatments such as methotrexate. Early and aggressive use of these biologics, often in combination with methotrexate, has dramatically reduced the risk of long-term ocular complications such as cataracts and glaucoma. Second-line biologics, such as abatacept and tocilizumab, have provided additional options for patients with refractory uveitis, improving overall visual outcomes.

Early biologic therapies, including TNF inhibitors (etanercept, adalimumab), IL-1 blockers (anakinra, canakinumab), and IL-6 inhibitors (tocilizumab), continue to be transformative, mainly when initiated early in disease progression. This approach has led to better disease control, reduced long-term joint damage, and improved quality of life for patients. The introduction of JAK inhibitors, such as tofacitinib, has provided additional therapeutic options for patients who do not respond to traditional biologics, offering more personalized treatment strategies.

Furthermore, newer therapies, such as IL-17 inhibitors (secukinumab), have improved the treatment of juvenile spondyloarthropathies (JSPA). These drugs specifically target the inflammatory pathways involved in arthritis related to enthesitis, improving disease outcomes for patients with more resistant forms of JIA.

In general, advances in biomarkers such as MRP8/14 and IL-18, combined with early interventions and novel treatments, have significantly changed the landscape of JIA management. These developments enable precise disease monitoring, personalized therapies, and better long-term outcomes for pediatric patients.

Dr. Tamas Constantin
Guest Editor

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Keywords

  • juvenile idiopathic arthritis
  • pediatric rheumatology
  • Uveitis
  • MRP8/14 (S100A8/A9)
  • interleukin-18 (IL-18)
  • TNF inhibitors (etanercept, adalimumab)
  • IL-1 blockers (anakinra, canakinumab)
  • IL-6 inhibitors (tocilizumab)
  • JAK inhibitors
  • IL-17 inhibitors (secukinumab)

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Published Papers (1 paper)

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Research

15 pages, 760 KiB  
Article
Forecasting Achievement of Inactive Disease in Juvenile Idiopathic Arthritis with Artificial Intelligence
by Ana I. Rebollo-Giménez, Francesca Ridella, Silvia Maria Orsi, Elena Aldera, Marco Burrone, Valentina Natoli, Silvia Rosina, Alessandro Consolaro, Esperanza Naredo, Angelo Ravelli and Davide Cangelosi
Children 2025, 12(6), 741; https://doi.org/10.3390/children12060741 (registering DOI) - 7 Jun 2025
Abstract
Objective: to seek for predictors of inactive disease (ID) in juvenile idiopathic arthritis (JIA) with artificial intelligence. Methods: The clinical charts of patients seen within 6 months after disease onset between 2007 and 2019 and with follow-up visits at 6, 12, 18, and [...] Read more.
Objective: to seek for predictors of inactive disease (ID) in juvenile idiopathic arthritis (JIA) with artificial intelligence. Methods: The clinical charts of patients seen within 6 months after disease onset between 2007 and 2019 and with follow-up visits at 6, 12, 18, and 24 months were reviewed retrospectively. Sixty-eight potential predictors were recorded at each visit. The primary endpoint was ID at 24 months by 2004 Wallace criteria. Data obtained from diverse combinations of visits were examined to identify the best forecasting model. After pre-processing, the cohort was divided into training (50%) and testing (50%) cohorts. Multivariate time series forecasting, coupled with the Random Forest method, was used to train the machine learning (ML) forecasting model. Predictive performance was assessed through the Matthews correlation coefficient (MCC). Results: A total of 414 patients were included. The best performance in predicting ID at 24 months in the training cohort was provided by the 0–12 months interval (MCC = 0.68). In the testing cohort, the same ML model confirmed a high forecasting performance (MCC = 0.65). Assessment of feature importance and impact analysis showed that the most relevant predictor of ID was the physician’s global assessment (PhGA), followed by the count of active joints (AJC). Conclusions: PhGA and AJC values over the first 12 months were the strongest predictors of ID at 24 months. This finding highlights the importance of regular quantitative assessment of disease activity by the caring physician in monitoring the course of the patient toward achievement of complete disease quiescence. Full article
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