These immune signatures helped identify a shared signature of inflammation found in patients with JIA, as well as in patients with other systemic inflammatory diseases. These immune patterns were accentuated in patients specifically with systemic JIA and patients with JIA who had active disease.
In patients with active systemic JIA, a significant decrease in natural killer (NK) cell percentage was found, suggesting that quantitative changes in the signature immune dysregulation may be used as reliable markers of disease activity. Another major finding provides an algorithm generated through machine learning to distinguish JIA from healthy controls with approximately 90% accuracy.
Implications
Current: Systemic JIA has a pronounced, distinct immune pattern when compared with other JIA subtypes. This research has implications for our understanding of the pathophysiologic disease process of systemic JIA, which has also been shown through genetic profiles.2
Given the distinct pattern seen in patients with systemic JIA, restructuring classification criteria to designate it as a unique disease can be considered. Additionally, children with JIA have an immune pattern distinct from healthy children. This method can serve as a diagnostic tool for more complicated patients with a clinical presentation that poses diagnostic uncertainty.
Future: Future implications for immunophenotyping machine learning include the diagnosis, treatment and prognosis of all rheumatologic conditions. With the increase in potential immunomodulating targeted therapies, along with the classification of disease based on those same immune targets, an exciting possibility of choosing precise individualized treatment plans for our patients exists.
In pediatric rheumatology, we are accustomed to using complicated clinical algorithms to properly diagnose and treat our patients. But is this really the most accurate system? Machine learning and immunophenotyping have the potential to turn the field inside out.
Chances in the Tournament
As the only pediatric team in play, this team is the dark horse. However, the long-term clinical implications of this team are arguably more far-reaching than any other team in the Machine Region—and the entire tournament.
Despite the small number of participants in this study, the exclusion of psoriatic and enthesitis-related JIA, and the lack of attention given to race, ethnicity and environmental factors that could potentially alter immune signatures, we still believe the strengths of this article make it a crucial one. We stand an excellent chance.
Immunophenotyping machine learning has implications for more than just anti-tumor necrosis factor response in RA, like our opponents would argue. Our study shows that its implications stretch far beyond one diagnosis or two therapy choices. In fact, pediatric rheumatologists have just begun to pave the way for better classifying patients in the adult world as well. Could eight subtypes of RA actually exist, and you just don’t know it yet?
