Abstract
This thesis explores advancements in monitoring and treatment strategies for Juvenile Idiopathic Arthritis (JIA), aiming to improve personalized care.
In the first part, novel monitoring strategies are investigated, including the feasibility of capillary blood sampling at home as an alternative to venous blood draws at the hospital. Results provide insights into the feasibility and challenges associated with self-sampling. The presented laboratory studies provide evidence for the comparability between results from capillary and venous blood samples, supporting the potential for remote monitoring. In addition, a mobile eHealth application, electronic dashboard, and web-based surveys are examined in different studies, demonstrating that these technologies could provide insights in disease status and disease course, and could be used as monitoring tools, especially for patients with stable or inactive disease.
In the second part, research is presented to further improve and tailor JIA treatment. Potential causes for bDMARD therapy failure, including the formation of anti-drug antibodies and low drug levels, are discussed. The thesis also examines biologic therapy withdrawal, showing that stopping specific bDMARDs in JIA patients with clinically inactive disease leads to significant cost reductions. Furthermore, the development of prediction models for methotrexate response is explored, which remains the first-line treatment agent for non-systemic JIA. Methodological concerns of currently available prediction models are highlighted, and new prediction models are developed. While these newly developed models demonstrate moderate performance, further refinement and validation are necessary.
In conclusion, this thesis elaborates on current research challenges in the field of JIA and provides new evidence towards a data-driven and personalized approach to monitoring and treatment strategies. The integration of standardized data collection, digital solutions for JIA patients (including remote laboratory monitoring and eHealth applications), predictive models, cost-effective treatment adjustments, and analytical applications could further enable personalized treatment and monitoring while improving the quality of care for children and adolescents with JIA.
In the first part, novel monitoring strategies are investigated, including the feasibility of capillary blood sampling at home as an alternative to venous blood draws at the hospital. Results provide insights into the feasibility and challenges associated with self-sampling. The presented laboratory studies provide evidence for the comparability between results from capillary and venous blood samples, supporting the potential for remote monitoring. In addition, a mobile eHealth application, electronic dashboard, and web-based surveys are examined in different studies, demonstrating that these technologies could provide insights in disease status and disease course, and could be used as monitoring tools, especially for patients with stable or inactive disease.
In the second part, research is presented to further improve and tailor JIA treatment. Potential causes for bDMARD therapy failure, including the formation of anti-drug antibodies and low drug levels, are discussed. The thesis also examines biologic therapy withdrawal, showing that stopping specific bDMARDs in JIA patients with clinically inactive disease leads to significant cost reductions. Furthermore, the development of prediction models for methotrexate response is explored, which remains the first-line treatment agent for non-systemic JIA. Methodological concerns of currently available prediction models are highlighted, and new prediction models are developed. While these newly developed models demonstrate moderate performance, further refinement and validation are necessary.
In conclusion, this thesis elaborates on current research challenges in the field of JIA and provides new evidence towards a data-driven and personalized approach to monitoring and treatment strategies. The integration of standardized data collection, digital solutions for JIA patients (including remote laboratory monitoring and eHealth applications), predictive models, cost-effective treatment adjustments, and analytical applications could further enable personalized treatment and monitoring while improving the quality of care for children and adolescents with JIA.
| Original language | English |
|---|---|
| Awarding Institution |
|
| Supervisors/Advisors |
|
| Award date | 20 May 2025 |
| Publisher | |
| Print ISBNs | 9789039378717 |
| DOIs | |
| Publication status | Published - 20 May 2025 |
Keywords
- juvenile idiopathic arthritis
- laboratory monitoring
- remote monitoring
- self-sampling
- eHealth
- digital solutions
- immunogenicity
- clinical prediction models
- cost-effective treatment
- standardized data collection