The biologic basis of clinical heterogeneity in juvenile idiopathic arthritis

The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence

Artificial intelligence algorithms, with roots extending into the past but experiencing a resurgence and evolution in recent years due to their superiority over traditional methods and contributions to human capabilities, have begun to make their presence felt in the field of pediatric rheumatology. In the ever-evolving realm of pediatric rheumatology, there have been incremental advancements supported by artificial intelligence in understanding and stratifying diseases, developing biomarkers, refining visual analyses, and facilitating individualized treatment approaches. However, like in many other domains, these strides have yet to gain clinical applicability and validation, and ethical issues remain unresolved. Furthermore, mastering different and novel terminologies appears challenging for clinicians. This review aims to provide a comprehensive overview of the current literature, categorizing algorithms and their applications, thus offering a fresh perspective on the nascent relationship between pediatric rheumatology and artificial intelligence, highlighting both its advancements and constraints.

Histological synovitis score in juvenile idiopathic arthritis and other pediatric synovial inflammatory conditions

OBJECTIVE

The Krenn's scoring is a system for histopathological grading of synovial inflammation, and in adults, useful in etiological grouping. The score has only rarely been used in pediatric samples. We aimed to assess the performance of the score in juvenile idiopathic arthritis and other pediatric synovial inflammatory processes in categorization of the disease and in finding characteristic pathological features.

DESIGN

We collected an unselected series of pediatric (age < 16 years) routine synovial biopsy samples to represent normal synovium and inflammatory conditions. The final diagnosis based on clinical follow-up was determined and classified as normal synovium, and different groups according to etiology. Total of 142 patients were analyzed. According to the score, case was classified to normal, low- or high-grade synovitis.

RESULTS

The synovitis scores in clinically normal synovium were low with 48 % of cases with scores of low-grade synovitis. In structural joint disorders scores varied from normal to low grade synovitis with occasional cases of high grade synovitis. In transient/reactive arthritis scores showed increase, majority clustering to low grade synovitis. In JIA and in bacterial synovitis the scores were higher than in the other groups high grade synovitis being the dominant grade. Extended oligoarthritis showed higher score than persistent oligoarthritis. ROC analysis indicated that JIA could be differentiated from other conditions.

CONCLUSIONS

The Krenn's synovitis score is useful in the etiological classification of pediatric synovial samples, high Krenn's score suggesting JIA. Observed differences between the subcategories of oligoarthritis may be useful in subclassifying these types of JIA.

Peripheral blood regulatory T cells and disease activity, quality of life, and outcomes in children with juvenile idiopathic arthritis

OBJECTIVES

To measure regulatory T cell (Treg) levels in the peripheral blood of children with juvenile idiopathic arthritis (JIA) and analyse the association of this measure with disease activity, quality of life, adjustment of treatment, and hospitalisation.

METHODS

We conducted a two-phase study (cross-sectional and prospective), including consecutive children with a JIA diagnosis according to ILAR criteria. Our independent variables were Tregs, Th1, Th2, and cytokines in peripheral blood, and our dependent variables in the cross-sectional phase were arthritis category, JIA activity, and patient-reported outcomes. To test associations, we used Spearman's correlation coefficient and the Mann-Whitney U test. In the prospective phase, we explored the probability of treatment adjustment and hospitalisation for JIA during follow-up according to Tregs levels at baseline, using Cox proportional regression.

RESULTS

Our sample included 87 participants (median age 11 years, 63.2% girls). Tregs were not associated with most variables of interest. However, we found that higher Tregs concentration was associated with lower erythrocyte sedimentation rate (ESR) and better subjective disease status and course, while higher IL-10 and TGF-β levels were associated with lower ESR, less pain, and better subjective disease status We found no association between Tregs and treatment adjustments or hospitalisation.

CONCLUSIONS

Higher baseline Treg levels in the peripheral blood of children with JIA may be associated with reduced disease activity and better quality of life, though were not informative on the inflammatory progression on the follow-up.

Transcriptomic signatures of classical monocytes reveal pro-inflammatory modules and heterogeneity in polyarticular juvenile idiopathic arthritis

Introduction

Polyarticular juvenile idiopathic arthritis (pJIA) is a childhood-onset autoimmune disease. Immune cells contribute to persistent inflammation observed in pJIA. Despite the crucial role of monocytes in arthritis, the precise involvement of classical monocytes in the pathogenesis of pJIA remains uncertain. Here, we aimed to uncover the transcriptomic patterns of classical monocytes in pJIA, focusing on their involvement in disease mechanism and heterogeneity.

Methods

A total of 17 healthy subjects and 18 premenopausal women with pJIA according to ILAR criteria were included. Classical monocytes were isolated, and RNA sequencing was performed. Differential expression analysis was used to compare pJIA patients and healthy control group. Differentially expressed genes (DEGs) were identified, and gene set enrichment analysis (GSEA) was performed. Using unsupervised learning approach, patients were clustered in two groups based on their similarities at transcriptomic level. Subsequently, these clusters underwent a comparative analysis to reveal differences at the transcriptomic level.

Results

We identified 440 DEGs in pJIA patients of which 360 were upregulated and 80 downregulated. GSEA highlighted TNF-α and IFN-γ response. Importantly, this analysis not only detected genes targeted by pJIA therapy but also identified new modulators of immuno-inflammation. PLAUR, IL1B, IL6, CDKN1A, PIM1, and ICAM1 were pointed as drivers of chronic hyperinflammation. Unsupervised learning approach revealed two clusters within pJIA, each exhibiting varying inflammation levels.

Conclusion

These findings indicate the pivotal role of immuno-inflammation driven by classical monocytes in pJIA and reveals the existence of two subclusters within pJIA, regardless the positivity of rheumatoid factor and anti-CCP, paving the way to precision medicine.

Patterns and determinants of response to novel therapies in juvenile and adult-onset polyarthritis

Biologic and targeted synthetic DMARDs (b/tsDMARDs) have revolutionized the management of multiple rheumatic inflammatory conditions. Among these, polyarticular JIA (pJIA) and RA display similarities in terms of disease pathophysiology and response pattern to b/tsDMARDs. Indeed, the therapeutic efficacy of novel targeted drugs is variable among individual patients, in both RA and pJIA. The mechanisms and determinants of this heterogeneous response are diverse and complex, such that the development of true 'precision'-medicine strategies has proven highly challenging. In this review, we will discuss pathophysiological, patient-specific, drug-specific and environmental factors contributing to individual therapeutic response in pJIA in comparison with what is known in RA. Although some biomarkers have been identified that stratify with respect to the likelihood of either therapeutic response or non-response, few have proved useful in clinical practice so far, likely due to the complexity of treatment-response mechanisms. Consequently, we propose a pragmatic, patient-centred and clinically based approach, i.e. personalized instead of biomarker-based precision medicine in JIA.

Applications of artificial intelligence in clinical management, research and health administration: imaging perspectives with a focus on hemophilia

INTRODUCTION

Joints of persons with hemophilia are frequently affected by repetitive hemarthrosis. In this paper concepts, perks and quirks of the use of artificial intelligence (AI), machine learning (ML) and deep learning are reviewed within clinical and research contexts of hemophilia and other blood-induced disorders' patient care, targeted to the imaging diagnosis of hemophilic joints, under the perspective of different stakeholders (radiologists, hematologists, nurses, physiotherapists, technologists, researchers, managers and patients/caregivers).

AREAS COVERED

Rubrics that determine the suitability of the utilization of AI in blood-induced disorders' patient care, including diagnosis and follow-up of patients are discussed, focusing on features in which AI can replace or augment the role of radiology in the clinical management and in research of patients. Insights on features in the design and conduct of AI projects in which the human intervention remains critical are provided.

EXPERT OPINION

The author discusses research concepts in radiogenomics, and challenges of the utilization of AI in different healthcare fields such as patient safety, data sharing and privacy regulations, workforce education and future jobs' shortage. Finally, the author proposes alternatives and potential solutions to mitigate challenges in successfully deploying ML algorithms into clinical practice.

Intraarticular steroids as DMARD-sparing agents for juvenile idiopathic arthritis flares: Analysis of the Childhood Arthritis and Rheumatology Research Alliance Registry

BACKGROUND

Children with juvenile idiopathic arthritis (JIA) who achieve a drug free remission often experience a flare of their disease requiring either intraarticular steroids (IAS) or systemic treatment with disease modifying anti-rheumatic drugs (DMARDs). IAS offer an opportunity to recapture disease control and avoid exposure to side effects from systemic immunosuppression. We examined a cohort of patients treated with IAS after drug free remission and report the probability of restarting systemic treatment within 12 months.

METHODS

We analyzed a cohort of patients from the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry who received IAS for a flare after a period of drug free remission. Historical factors and clinical characteristics and of the patients including data obtained at the time of treatment were analyzed.

RESULTS

We identified 46 patients who met the inclusion criteria. Of those with follow up data available 49% had restarted systemic treatment 6 months after IAS injection and 70% had restarted systemic treatment at 12 months. The proportion of patients with prior use of a biologic DMARD was the only factor that differed between patients who restarted systemic treatment those who did not, both at 6 months (79% vs 35%, p < 0.01) and 12 months (81% vs 33%, p < 0.05).

CONCLUSION

While IAS are an option for all patients who flare after drug free remission, it may not prevent the need to restart systemic treatment. Prior use of a biologic DMARD may predict lack of success for IAS. Those who previously received methotrexate only, on the other hand, are excellent candidates for IAS.

Synovial fluid mesenchymal progenitor cells from patients with juvenile idiopathic arthritis demonstrate limited self-renewal and chondrogenesis

Juvenile idiopathic arthritis (JIA) is a heterogeneous group of inflammatory diseases affecting joints with a prevalence of one in a thousand children. There is a growing body of literature examining the use of mesenchymal stem/progenitor cells (MPCs) for the treatment of adult and childhood arthritis, however, we still lack a clear understanding of how these MPC populations are impacted by arthritic disease states and how this could influence treatment efficacy. In the current study we examined the immunophenotyping, self-renewal ability and chondrogenic capacity (in vitro and in vivo) of synovial derived MPCs from normal, JIA and RA joints. Synovial MPCs from JIA patients demonstrated reduced self-renewal ability and chondrogenic differentiation capacity. Furthermore, they did not induce cartilage regeneration when xenotransplanted in a mouse cartilage injury model. Synovial MPCs from JIA patients are functionally compromised compared to MPCs from normal and/or RA joints. The molecular mechanisms behind this loss of function remain elusive. Further study is required to see if these cells can be re-functionalized and used in cell therapy strategies for these JIA patients, or if allogenic approaches should be considered.

Latent classes of early response trajectories to biologics initiation in juvenile idiopathic arthritis: an analysis of four trials

AIMS

1) To delineate latent classes of treatment response to biologics in juvenile idiopathic arthritis (JIA) patients in the first 16 weeks after initiation. 2) To identify predictors of early disease response.

METHODS

The study population was drawn from four biologics trials in polyarticular course JIA: Etanercept 2000, Abatacept 2008, TRial of Early Aggressive Therapy (TREAT) 2012 and Tocilizumab 2014. The outcome was active joint counts (AJC). Semiparametric latent class trajectory analysis was applied to identify latent classes of response to treatment; AJC was transformed for this modelling. We tested baseline disease and treatment characteristics for their abilities to predict class membership of response.

RESULTS

There were 480 participants, 74% females. At baseline, 26% were rheumatoid factor positive. 67% were on methotrexate at enrollment. Three latent class solution provided the best fit. Baseline AJC was the sole best predictor of class membership. Participants classified by their highest membership probabilities into high baseline AJC (> 30) and slow response (26.5%), low baseline AJC (< 10), early and sustained response (29.7%), and moderate baseline AJC progressive response (43.8%). Participants were classified into the latent classes with a mean class membership posterior probability of 0.97. Those on methotrexate at baseline were less likely to belong to high baseline AJC class.

CONCLUSIONS

Three latent classes of responses were detectable in the first 16 weeks of biologics therapy. Those with the highest baseline AJC demonstrated very slow response in this window and were less likely to be on concomitant methotrexate.

TRIALS REGISTRATION

TREAT 2012 (NCT NCT00443430 ) (Wallace et. al, Arthritis Rheum 64:2012-21, 2012), tocilizumab trial 2014 ( NCT00988221 ), abatacept trial 2008 ( NCT00095173 ). Etanercept 2000 from Amgen does not have a trial registration number.

Increased Development of Th1, Th17, and Th1.17 Cells Under T1 Polarizing Conditions in Juvenile Idiopathic Arthritis

In juvenile idiopathic arthritis (JIA) inflammatory T cells and their produced cytokines are drug targets and play a role in disease pathogenesis. Despite their clinical importance, the sources and types of inflammatory T cells involved remain unclear. T cells respond to polarizing factors to initiate types of immunity to fight infections, which include immunity types 1 (T1), 2 (T2), and 3 (T17). Polarizing factors drive CD4⁺ T cells towards T helper (Th) cell subtypes and CD8⁺ T cells towards cytotoxic T cell (Tc) subtypes. T1 and T17 polarization are associated with autoimmunity and production of the cytokines IFNγ and IL-17 respectively. We show that JIA and child healthy control (HC) peripheral blood mononuclear cells are remarkably similar, with the same frequencies of CD4⁺ and CD8⁺ naïve and memory T cell subsets, T cell proliferation, and CD4⁺ and CD8⁺ T cell subsets upon T1, T2, and T17 polarization. Yet, under T1 polarizing conditions JIA cells produced increased IFNγ and inappropriately produced IL-17. Under T17 polarizing conditions JIA T cells produced increased IL-17. Gene expression of IFNγ, IL-17, Tbet, and RORγT by quantitative PCR and RNA sequencing revealed activation of immune responses and inappropriate activation of IL-17 signaling pathways in JIA polarized T1 cells. The polarized JIA T1 cells were comprised of Th and Tc cells, with Th cells producing IFNγ (Th1), IL-17 (Th17), and both IFNγ-IL-17 (Th1.17) and Tc cells producing IFNγ (Tc1). The JIA polarized CD4⁺ T1 cells expressed both Tbet and RORγT, with higher expression of the transcription factors associated with higher frequency of IL-17 producing cells. T1 polarized naïve CD4⁺ cells from JIA also produced more IFNγ and more IL-17 than HC. We show that in JIA T1 polarization inappropriately generates Th1, Th17, and Th1.17 cells. Our data provides a tool for studying the development of heterogeneous inflammatory T cells in JIA under T1 polarizing conditions and for identifying pathogenic immune cells that are important as drug targets and diagnostic markers.

Updated clinical practice treat-to-target guidelines for JIA management: the Egyptian College of Pediatric Rheumatology initiative

Background

These updated guidelines aimed to provide appropriate and convenient guidelines for the treatment of various types of juvenile idiopathic arthritis (JIA).

Using the Delphi technique, this study was conducted to reach expert consensus on a treat-to-target management strategy for JIA. According to the PICO (patient/population, intervention, comparison, and outcomes) approach, the preliminary scientific committee identified a total of 17 key clinical questions. To assemble evidence on the advantages and dangers associated with JIA treatments, an evidence-based, systematic literature review was conducted. Researchers and clinicians with experience in JIA management were identified by the core leadership team. To establish a consensus on the management suggestions for JIA patients, a Delphi approach (2 rounds) was used.

Results

An online survey was applied to the expert panel (n = 27), and 26 of them completed both rounds. At the conclusion of round 2, a total of eighteen (18) recommendation items were gathered, which were divided into four sections to address the four key JIA categories. The percentage of those who agreed with the recommendations (ranks 7–9) ranged from 83.2 to 100% (average 86.8%). The phrasing of all 18 clinical standards identified by the scientific committee was agreed upon (i.e. 75% of respondents strongly agreed or agreed). Algorithms have been proposed for the management of JIA polyarthritis, oligoarthritis, and systemic JIA.

Conclusion

A wide and representative panel of experts initiated a consensus about the management of JIA. The created guidelines give a complete approach to the management of JIA for all healthcare professionals involved in its management, as well as a means of monitoring and evaluating these guidelines on a regular basis.

Therapeutic strategies for treating juvenile idiopathic arthritis

Recent development of new medications has changed the juvenile idiopathic arthritis (JIA) treatment goal to inactive disease. With numerous options, how does a clinician choose which medication to use? Treatment options may depend on the clinical classification and a new paradigm considers the JIA subtypes in reference to categories of adult inflammatory arthritis; poligo JIA, spondyloarthritis JIA, and systemic JIA that can help guide a clinician in determining treatment options. Treatment strategies such as consensus treatment plans can provide guidance on treatment escalation. However, a treat-to-target strategy using frequent standardized disease activity measurements, shared decision making with the patient, and treatment escalation to achieve the disease activity target can provide a personalized approach to managing JIA.

Machine Learning in Rheumatic Diseases

With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients' stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.

Sleep in children and adolescents with juvenile idiopathic arthritis: a systematic review and meta-analysis of case-control studies

STUDY OBJECTIVES

Juvenile idiopathic arthritis (JIA) is one of the most common pediatric rheumatic disease. However, sleep alteration associated with this auto-immune disease remain unclear. We aimed in this systematic review and meta-analysis to compare sleep duration, quality, and architecture in JIA subjects with those in their healthy peers.

METHODS

Systematic search performed in PubMed, EMBase, Cochrane, and PsycINFO databases included 19 studies in the qualitative synthesis of which 10 met the inclusion criteria for the meta-analysis.

RESULTS

Pooled results from subjective methods indicated pronounced sleep disturbances and complaints in youth with JIA compared with their healthy counterparts. This was further confirmed by Increased difficulty maintaining sleep (wake after sleep onset; SMD: -0.69; CI: -1.29; -0.09, p =0.02) and a tendency to increased difficulty initiating sleep (sleep onset latency; SMD: -0.29; CI: -0.60; 0.03, p =0.07). There were no remarkable differences in sleep duration or sleep architecture between JIA patients and healthy controls. High heterogeneity was found for several outcomes. This could be explained by the different methods used as well as associated sleep disorders, medication and comorbidities.

CONCLUSIONS

Although included studies were methodologically diverse, the summarized results of our review and meta-analysis bring evidence that children with JIA present more fragmented sleep compared to healthy peers. Thereby, the implementation of strategies to manage and improve sleep in this population are needed and might have a beneficial effect on the symptoms and functions of JIA.

Juvenile idiopathic arthritis: from aetiopathogenesis to therapeutic approaches

Juvenile idiopathic arthritis (JIA) is the most common paediatric rheumatological disorder and is classified by subtype according to International League of Associations for Rheumatology criteria. Depending on the number of joints affected, presence of extra-articular manifestations, systemic symptoms, serology and genetic factors, JIA is divided into oligoarticular, polyarticular, systemic, psoriatic, enthesitis-related and undifferentiated arthritis. This review provides an overview of advances in understanding of JIA pathogenesis focusing on aetiology, histopathology, immunological changes associated with disease activity, and best treatment options. Greater understanding of JIA as a collective of complex inflammatory diseases is discussed within the context of therapeutic interventions, including traditional non-biologic and up-to-date biologic disease-modifying anti-rheumatic drugs. Whilst the advent of advanced therapeutics has improved clinical outcomes, a considerable number of patients remain unresponsive to treatment, emphasising the need for further understanding of disease progression and remission to support stratification of patients to treatment pathways.

Different Chronic Disorders That Fall within the Term Juvenile Idiopathic Arthritis

Juvenile idiopathic arthritis (JIA) represents a significant challenge for pediatricians who intend to diagnose and treat this pathology. The classification criteria for JIA subtypes are rigid and often do not fully satisfy the possibilities of classification in the subtype. The objective of this study was to identify clearer criteria for classifying JIA subtypes. The 2019 expert committee meeting (PRINTO) shows the difficulties of this classification and proposes new research directions for the identification of disease subtypes. Four different chronic disorders are used to define JIA in a concise and easy to follow classification system. However, dates from the literature suggest that at least 10% of cases are still difficult to classify. Possibly in the future, different classifications of JIA based on pathophysiological and genetic criteria would be necessary.

Biological classification of childhood arthritis: roadmap to a molecular nomenclature

Chronic inflammatory arthritis in childhood is heterogeneous in presentation and course. Most forms exhibit clinical and genetic similarity to arthritis of adult onset, although at least one phenotype might be restricted to children. Nevertheless, paediatric and adult rheumatologists have historically addressed disease classification separately, yielding a juvenile idiopathic arthritis (JIA) nomenclature that exhibits no terminological overlap with adult-onset arthritis. Accumulating clinical, genetic and mechanistic data reveal the critical limitations of this strategy, necessitating a new approach to defining biological categories within JIA. In this Review, we provide an overview of the current evidence for biological subgroups of arthritis in children, delineate forms that seem contiguous with adult-onset arthritis, and consider integrative genetic and bioinformatic strategies to identify discrete entities within inflammatory arthritis across all ages.

Characterisation, identification, clustering, and classification of disease

The importance of quantifying the distribution and determinants of multimorbidity has prompted novel data-driven classifications of disease. Applications have included improved statistical power and refined prognoses for a range of respiratory, infectious, autoimmune, and neurological diseases, with studies using molecular information, age of disease incidence, and sequences of disease onset ("disease trajectories") to classify disease clusters. Here we consider whether easily measured risk factors such as height and BMI can effectively characterise diseases in UK Biobank data, combining established statistical methods in new but rigorous ways to provide clinically relevant comparisons and clusters of disease. Over 400 common diseases were selected for analysis using clinical and epidemiological criteria, and conventional proportional hazards models were used to estimate associations with 12 established risk factors. Several diseases had strongly sex-dependent associations of disease risk with BMI. Importantly, a large proportion of diseases affecting both sexes could be identified by their risk factors, and equivalent diseases tended to cluster adjacently. These included 10 diseases presently classified as "Symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified". Many clusters are associated with a shared, known pathogenesis, others suggest likely but presently unconfirmed causes. The specificity of associations and shared pathogenesis of many clustered diseases provide a new perspective on the interactions between biological pathways, risk factors, and patterns of disease such as multimorbidity.

Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments heading to precision medicine

INTRODUCTION

Precision or personalized Medicine (PM) is used for the prevention and treatment of diseases by considering a huge amount of information about individuals variables. Due to high volume of information, AI-based computational models are required. A large set of studies conducted to examine the PM approach to improve childhood clinical outcomes. Thus, the main goal of this study was to review the application of health information technology and especially artificial intelligence (AI) methods for the treatment of childhood disease using PM.

METHODS

PubMed, Scopus, Web of Science, and EMBASE databases were searched up to December 18, 2019. Articles that focused on informatics applications for childhood disease PM included in this study. Included papers were classified for qualitative analysis and interpreting results. The results were analyzed using Microsoft Excel 2019.

RESULTS

From 341 citations, 62 papers met our inclusion criteria. The number of published papers that used AI methods to apply for PM in childhood diseases increased from 2010 to 2019. Our results showed that most applied methods were related to machine learning discipline. In terms of clinical scope, the largest number of clinical articles are devoted to oncology. Besides, the analysis showed that genomics was the most PM approach used regarding childhood disease.

CONCLUSION

This systematic review examined papers that used AI methods for applying PM approaches in childhood diseases from medical informatics perspectives. Thus, it provided new insight to researchers who are interested in knowing research needs in this field.

Clinical and associated inflammatory biomarker features predictive of short-term outcomes in non-systemic juvenile idiopathic arthritis

OBJECTIVE

To identify early predictors of disease activity at 18 months in JIA using clinical and biomarker profiling.

METHODS

Clinical and biomarker data were collected at JIA diagnosis in a prospective longitudinal inception cohort of 82 children with non-systemic JIA, and their ability to predict an active joint count of 0, a physician global assessment of disease activity of ≤1 cm, and inactive disease by Wallace 2004 criteria 18 months later was assessed. Correlation-based feature selection and ReliefF were used to shortlist predictors and random forest models were trained to predict outcomes.

RESULTS

From the original 112 features, 13 effectively predicted 18-month outcomes. They included age, number of active/effused joints, wrist, ankle and/or knee involvement, ESR, ANA positivity and plasma levels of five inflammatory biomarkers (IL-10, IL-17, IL-12p70, soluble low-density lipoprotein receptor-related protein 1 and vitamin D), at enrolment. The clinical plus biomarker panel predicted active joint count = 0, physician global assessment ≤ 1, and inactive disease after 18 months with 0.79, 0.80 and 0.83 accuracy and 0.84, 0.83, 0.88 area under the curve, respectively. Using clinical features alone resulted in 0.75, 0.72 and 0.80 accuracy, and area under the curve values of 0.81, 0.78 and 0.83, respectively.

CONCLUSION

A panel of five plasma biomarkers combined with clinical features at the time of diagnosis more accurately predicted short-term disease activity in JIA than clinical characteristics alone. If validated in external cohorts, such a panel may guide more rationally conceived, biologically based, personalized treatment strategies in early JIA.

Data-Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women's Rheumatoid Arthritis Sequential Study Registry

Objective

To use unbiased, data‐driven, principal component (PC) and cluster analysis to identify patient phenotypes of rheumatoid arthritis (RA) that might exhibit distinct trajectories of disease progression, response to treatment, and risk for adverse events.

Methods

Patient demographic, socioeconomic, health, and disease characteristics recorded at entry into a large, single‐center, prospective observational registry cohort, the Brigham and Women’s Rheumatoid Arthritis Sequential Study (BRASS), were harmonized using PC analysis to reduce dimensionality and collinearity. The number of PCs was established by eigenvalue >1, cumulative variance, and interpretability. The resulting PCs were used to cluster patients using a K‐means approach. Longitudinal clinical outcomes were compared between the clusters over 2 years.

Results

Analysis of 142 variables from 1,443 patients identified 41 PCs that accounted for 77% of the cumulative variance in the data set. Cluster analysis distinguished 5 patient clusters: 1) less RA disease activity/multimorbidity, shorter RA duration, lower incidence of comorbidities; 2) less RA disease activity/multimorbidity, longer RA duration, more infections, psychiatric comorbidities, health care utilization; 3) moderate RA disease activity/multimorbidity, more neurologic comorbidity; 4) more RA disease activity/multimorbidity, shorter RA duration, more metabolic comorbidity, higher body mass index; 5) more RA disease activity/multimorbidity, longer RA duration, more hepatic, orthopedic comorbidity and RA‐related surgeries. The clusters exhibited differences in clinical outcomes over 2 years of follow‐up.

Conclusion

Data‐driven analysis of the BRASS registry identified 5 distinct phenotypes of RA. These results illustrate the potential of data‐driven patient profiling as a tool to support personalized medicine in RA. Validation in an independent data set is ongoing.

Associations of clinical and inflammatory biomarker clusters with juvenile idiopathic arthritis categories

OBJECTIVE

To identify discrete clusters comprising clinical features and inflammatory biomarkers in children with JIA and to determine cluster alignment with JIA categories.

METHODS

A Canadian prospective inception cohort comprising 150 children with JIA was evaluated at baseline (visit 1) and after six months (visit 2). Data included clinical manifestations and inflammation-related biomarkers. Probabilistic principal component analysis identified sets of composite variables, or principal components, from 191 original variables. To discern new clinical-biomarker clusters (clusters), Gaussian mixture models were fit to the data. Newly-defined clusters and JIA categories were compared. Agreement between the two was assessed using Kruskal-Wallis analyses and contingency plots.

RESULTS

Three principal components recovered 35% (three clusters) and 40% (five clusters) of the variance in patient profiles in visits 1 and 2, respectively. None of the clusters aligned precisely with any of the seven JIA categories but rather spanned multiple categories. Results demonstrated that the newly defined clinical-biomarker lustres are more homogeneous than JIA categories.

CONCLUSION

Applying unsupervised data mining to clinical and inflammatory biomarker data discerns discrete clusters that intersect multiple JIA categories. Results suggest that certain groups of patients within different JIA categories are more aligned pathobiologically than their separate clinical categorizations suggest. Applying data mining analyses to complex datasets can generate insights into JIA pathogenesis and could contribute to biologically based refinements in JIA classification.

Implications of juvenile idiopathic arthritis genetic risk variants for disease pathogenesis and classification

PURPOSE OF REVIEW

We assess the implications of recent advances in the genetics of juvenile idiopathic arthritis (JIA) for the evolving understanding of inflammatory arthritis in children.

RECENT FINDINGS

JIA exhibits prominent genetic associations with the human leukocyte antigen (HLA) region, extending perhaps surprisingly even to the hyperinflammatory systemic JIA category. Some HLA associations resemble those for adult-onset inflammatory arthritides, providing evidence for pathogenic continuity across the age spectrum. Genome-wide association studies have defined an increasing number of JIA-linked non-HLA loci, many again shared with adult-onset arthritis. As most risk loci contain only noncoding variants, new experimental methods such as SNP-seq and innovative big-data strategies help identify responsible causative mutations, termed functional SNPs (fSNPs). Alternately, gene hunting in multiplex families implicates new genes in monogenic childhood arthritis, including MYD88 and the intriguing innate immune gene LACC1.

SUMMARY

Genetic data indicate a continuity between JIA and adult arthritis poorly reflected in current nomenclature. Advancing methodologies will help to identify new pathogenic mechanisms that inform the understanding of biologic subdivisions within JIA. Resulting insights will facilitate the application of lessons learned across the age spectrum to the treatment of arthritis in children and adults.

Psoriatic arthritis in childhood: A commentary on the controversy

Approximately 5% of children with juvenile idiopathic arthritis (JIA) are diagnosed with the psoriatic form of the disease. In recent years, there has been substantial scholarship demonstrating both heterogeneity within the disease as well as similarities with other forms of JIA, culminating in a recent proposal for the categorization of JIA that excluded the psoriatic form altogether. The purpose of the review is to summarize the clinical, epidemiologic, and genetic features of psoriatic JIA (PsJIA), comparing it with other categories of JIA including spondyloarthritis. We conclude that there are sufficient unique clinical and genetic features within PsJIA as well as similarities with its adult counterpart that warrant including it within the JIA paradigm.

A Clinically and Biologically Based Subclassification of the Idiopathic Inflammatory Myopathies Using Machine Learning

OBJECTIVE

Published predictive models of disease outcomes in idiopathic inflammatory myopathies (IIMs) are sparse and of limited accuracy due to disease heterogeneity. Computational methods may address this heterogeneity by partitioning patients based on clinical and biological phenotype.

METHODS

To identify new patient groups, we applied similarity network fusion (SNF) to clinical and biological data from 168 patients with myositis (64 adult polymyositis [PM], 65 adult dermatomyositis [DM], and 39 juvenile DM [JDM]) in the Rituximab in Myositis trial. We generated a sparse proof-of-concept bedside classifier using multinomial regression and identified characteristics that distinguished these groups. We conducted χ² tests to link new patient groups with the myositis subtypes.

RESULTS

SNF identified five patient groups in the discovery cohort that subdivided the myositis subtypes. The sparse multinomial regressor to predict patient group assignments (areas under the receiver operating characteristic curve = [0.78, 0.97]; areas under the precision-recall curve = [0.55, 0.96]) found that autoantibody enrichment defined four of these groups: anti-Mi-2, anti-signal recognition peptide (SRP), anti-nuclear matrix protein 2 (NXP2), and anti-synthetase (Syn). Depletion of immunoglobulin M (IgM) defined the fifth group. Each group was associated with one subtype, with adult DM being associated with anti-Mi-2 and anti-Syn autoantibodies, JDM being associated with anti-NXP2 autoantibodies, and adult PM being associated with IgM depletion and anti-SRP autoantibodies. These associations enabled us to further resolve the current myositis subtypes.

CONCLUSION

Using unsupervised machine learning, we identified clinically and biologically homogeneous groups of patients with IIMs, forming the basis of an integrated disease classification based on both clinical and biological phenotype, thus validating other approaches and what has been previously described.

Features distinguishing juvenile idiopathic arthritis among children with musculoskeletal complaints

BACKGROUND

Musculoskeletal (MSK) complaints in children vary, ranging from benign, self-limited conditions to serious disorders. Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease, initially presenting with MSK complaints. Delayed diagnosis and appropriate treatment have an enormous impact on the long-term outcomes and the level of disability. This study aimed to identify the features distinguishing JIA among children presenting with MSK complaints and to describe the spectrum of diseases at a large, single, tertiary center.

METHODS

A retrospective chart review was performed of patients evaluated by pediatric rheumatology consultation at the Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand, from July 2011 to June 2015.

RESULTS

Of 531 patients, 285 (53.6%) had at least one MSK complaint. The mean age of the patients was 9.1 ± 4.1 years. Joint pain was the most common MSK complaint (86.3%), followed by limping (33%) and refusal to walk (19.6%). Joint swelling and limited range of motion were found in 146 (51.2%) and 115 (40.4%) patients, respectively. Seventy-three (25.6%) patients were diagnosed as JIA. The other common diagnoses included Henoch-Schönlein purpura (16.1%), reactive arthritis (14.2%), and systemic lupus erythematosus (13.7%). Morning stiffness  ≥ 15 minutes [odds ratio (OR) 8.217 (3.404-19.833)]; joint swelling on MSK examination [OR 3.505 (1.754-7.004)]; a duration of MSK complaints of more than 6 weeks [OR 2.071 (1.120-3.829)]; and limping [OR 1.973 (1.048-3.712)] were significantly associated with the ultimate diagnosis of JIA.

CONCLUSIONS

Morning stiffness ≥ 15 minutes is a strong predictor of JIA. Comprehensive history taking and an MSK examination will provide clues for making the ultimate diagnosis for children with MSK complaints.

The Challenges and Opportunities of Classifying Childhood Arthritis

PURPOSE OF REVIEW

Childhood arthritis is in need of a new system of classification, owing to deficiencies in the International League of Associations for Rheumatology (ILAR) criteria. We briefly review the history of classification of childhood arthritis, discuss the major criticisms of the current system, and highlight current initiatives to address those concerns.

RECENT FINDINGS

Recent studies in both pediatric and adult rheumatology into the biologic basis of disease as well as the clinical patterns of presentation have informed the efforts toward developing a new classification system. Several efforts are currently underway to improve the classification of childhood arthritis, most notably the project of the Pediatric Rheumatology International Trials Organization (PRINTO). This international alliance of pediatric rheumatologists has begun a 4-step process to create new classification criteria for childhood arthritis. They are currently on step 3 of the process.

Treatment Withdrawal Following Remission in Juvenile Idiopathic Arthritis: A Systematic Review of the Literature

BACKGROUND

Early diagnosis and treatment of juvenile idiopathic arthritis (JIA) with conventional and biologic disease-modifying anti-rheumatic drugs have vastly improved outcomes for children with these diseases. Currently, a large proportion of children with JIA are able to achieve clinical inactive disease and remission. With this success, important questions have arisen about when medications can be stopped and how to balance the risks and benefits of continuing medications versus the potential for flare after stopping.

AIM

The aim was to conduct a systematic review of the available literature to summarize current evidence about medication withdrawal for JIA in remission.

METHODS

We conducted a systematic literature search in PubMed and Embase from 1990 to 2019. References were first screened by title and then independently screened by title and abstract by two authors. A total of 77 original papers were selected for full-text review. Data were extracted from 30 papers on JIA and JIA-associated uveitis, and the quality of the evidence was evaluated using National Institutes of Health (NIH)/National Heart, Lung, and Blood Institute (NHLBI) tools. Studies on biochemical and radiologic biomarkers were also reviewed and summarized.

RESULTS

Most studies investigating treatment withdrawal in JIA have been observational and of poor or fair quality; interpretations of these studies have been limited by differences in study populations, disease and remission durations, the medications withdrawn, approaches to withdrawal, and definitions of disease outcomes. Overall the data suggest that flares are common after stopping JIA medications, particularly biologic medications. Clinical characteristics associated with increased risks of flare have not been consistently identified. Biochemical biomarkers and ultrasound findings have been shown to predict outcomes after stopping medications, but to date, no such predictor has been consistently validated across JIA populations. Studies have also not identified optimal strategies for withdrawing medication for well-controlled JIA. Promising withdrawal strategies include discontinuing methotrexate before biologic medications in children receiving combination therapy, dose reduction for children on biologics, and treat-to-target approaches to withdrawal. These and other strategies require further investigation in larger, high-quality studies.

CONCLUSIONS

The published literature on treatment withdrawal in JIA has varied in design and quality, yielding little conclusive evidence thus far on the management of JIA in remission. Given the importance of this question, international collaborative efforts are underway to study clinical and biologic predictors of successful medication withdrawal in JIA. These efforts may ultimately support the development of personalized approaches to withdrawing medication in children with JIA in remission.

The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches

Background

The response to treatment for juvenile idiopathic arthritis (JIA) can be staged using clinical features. However, objective laboratory biomarkers of remission are still lacking. In this study, we used machine learning to predict JIA activity from transcriptomes from peripheral blood mononuclear cells (PBMCs). We included samples from children with Native American ancestry to determine whether the model maintained validity in an ethnically heterogeneous population.

Methods

Our dataset consisted of 50 samples, 23 from children in remission and 27 from children with an active disease on therapy. Nine of these samples were from children with mixed European/Native American ancestry. We used 4 different machine learning methods to create predictive models in 2 populations: the whole dataset and then the samples from children with exclusively European ancestry.

Results

In both populations, models were able to predict JIA status well, with training accuracies > 74% and testing accuracies > 78%. Performance was better in the whole dataset model. We note a high degree of overlap between genes identified in both populations. Using ingenuity pathway analysis, genes from the whole dataset associated with cell-to-cell signaling and interactions, cell morphology, organismal injury and abnormalities, and protein synthesis.

Conclusions

This study demonstrates it is feasible to use machine learning in conjunction with RNA sequencing of PBMCs to predict JIA stage. Thus, developing objective biomarkers from easy to obtain clinical samples remains an achievable goal.

Review of environmental factors and juvenile idiopathic arthritis

Juvenile idiopathic arthritis is a common rheumatic disease that presents as chronic childhood arthritis. JIA is considered a multifactorial disease that may result from diverse genetic and environmental risk factors. A minority of the population-attributable risk of JIA is estimated to be due to familial factors. Thus, non-genetic or environmental factors likely account for a majority of the risk of developing JIA. Yet, while substantial data have linked environmental factors to the development of rheumatoid arthritis, similar evidence regarding JIA is sparse. This narrative review provides updates on recent literature about environmental factors that might influence the risk of developing JIA, including studies about potentially beneficial and harmful influences as well as factors with unclear effects.

The 4-H of Biomarkers in Arthritis: A Lot of Help, Occasional Harm, Some Hype, Increasing Hope

(Gilles Boire): It was both a pleasure and an honor to present the 2019 Dunlop-Dottridge Lecture. My co-author and I will now discuss benefits and pitfalls of biomarkers developed through emerging techniques, evaluated through the experiential perspective of a seasoned clinician, as they apply to the quest for biomarker identification in rheumatic diseases.

Molecular signature characterisation of different inflammatory phenotypes of systemic juvenile idiopathic arthritis

Objectives

The International League of Associations for Rheumatology classification criteria define systemic juvenile idiopathic arthritis (SJIA) by the presence of fever, rash and chronic arthritis. Recent initiatives to revise current criteria recognise that a lack of arthritis complicates making the diagnosis early, while later a subgroup of patients develops aggressive joint disease. The proposed biphasic model of SJIA also implies a ‘window of opportunity’ to abrogate the development of chronic arthritis. We aimed to identify novel SJIA biomarkers during different disease phases.

Methods

Children with active SJIA were subgrouped clinically as systemic autoinflammatory disease with fever (SJIAsyst) or polyarticular disease (SJIApoly). A discovery cohort of n=10 patients per SJIA group, plus n=10 with infection, was subjected to unbiased label-free liquid chromatography mass spectrometry (LC-MS/MS) and immunoassay screens. In a separate verification cohort (SJIAsyst, n=45; SJIApoly, n=29; infection, n=32), candidate biomarkers were measured by multiple reaction monitoring MS (MRM-MS) and targeted immunoassays.

Results

Signatures differentiating the two phenotypes of SJIA could be identified. LC-MS/MS in the discovery cohort differentiated SJIAsyst from SJIApoly well, but less effectively from infection. Targeted MRM verified the discovery data and, combined with targeted immunoassays, correctly identified 91% (SJIAsyst vs SJIApoly) and 77% (SJIAsyst vs infection) of all cases.

Conclusions

Molecular signatures differentiating two phenotypes of SJIA were identified suggesting shifts in underlying immunological processes in this biphasic disease. Biomarker signatures separating SJIA in its initial autoinflammatory phase from the main differential diagnosis (ie, infection) could aid early-stage diagnostic decisions, while markers of a phenotype switch could inform treat-to-target strategies.

Patterns of joint involvement in juvenile idiopathic arthritis and prediction of disease course: A prospective study with multilayer non-negative matrix factorization

BACKGROUND

Joint inflammation is the common feature underlying juvenile idiopathic arthritis (JIA). Clinicians recognize patterns of joint involvement currently not part of the International League of Associations for Rheumatology (ILAR) classification. Using unsupervised machine learning, we sought to uncover data-driven joint patterns that predict clinical phenotype and disease trajectories.

METHODS AND FINDINGS

We analyzed prospectively collected clinical data, including joint involvement using a standard 71-joint homunculus, for 640 discovery patients with newly diagnosed JIA enrolled in a Canada-wide study who were followed serially for five years, treatment-naïve except for nonsteroidal anti-inflammatory drugs (NSAIDs) and diagnosed within one year of symptom onset. Twenty-one patients had systemic arthritis, 300 oligoarthritis, 125 rheumatoid factor (RF)-negative polyarthritis, 16 RF-positive polyarthritis, 37 psoriatic arthritis, 78 enthesitis-related arthritis (ERA), and 63 undifferentiated arthritis. At diagnosis, we observed global hierarchical groups of co-involved joints. To characterize these patterns, we developed sparse multilayer non-negative matrix factorization (NMF). Model selection by internal bi-cross-validation identified seven joint patterns at presentation, to which all 640 discovery patients were assigned: pelvic girdle (57 patients), fingers (25), wrists (114), toes (48), ankles (106), knees (283), and indistinct (7). Patterns were distinct from clinical subtypes (P < 0.001 by χ2 test) and reproducible through external data set validation on a 119-patient, prospectively collected independent validation cohort (reconstruction accuracy Q2 = 0.55 for patterns; 0.35 for groups). Some patients matched multiple patterns. To determine whether their disease outcomes differed, we further subdivided the 640 discovery patients into three subgroups by degree of localization-the percentage of their active joints aligning with their assigned pattern: localized (≥90%; 359 patients), partially localized (60%-90%; 124), or extended (<60%; 157). Localized patients more often maintained their baseline patterns (P < 0.05 for five groups by permutation test) than nonlocalized patients (P < 0.05 for three groups by permutation test) over a five-year follow-up period. We modelled time to zero joints in the discovery cohort using a multivariate Cox proportional hazards model considering joint pattern, degree of localization, and ILAR subtype. Despite receiving more intense treatment, 50% of nonlocalized patients had zero joints at one year compared to six months for localized patients. Overall, localized patients required less time to reach zero joints (partial: P = 0.0018 versus localized by log-rank test; extended: P = 0.0057). Potential limitations include the requirement for patients to be treatment naïve (except NSAIDs), which may skew the patient cohorts towards milder disease, and the validation cohort size precluded multivariate analyses of disease trajectories.

CONCLUSIONS

Multilayer NMF identified patterns of joint involvement that predicted disease trajectory in children with arthritis. Our hierarchical unsupervised approach identified a new clinical feature, degree of localization, which predicted outcomes in both cohorts. Detailed assessment of every joint is already part of every musculoskeletal exam for children with arthritis. Our study supports both the continued collection of detailed joint involvement and the inclusion of patterns and degrees of localization to stratify patients and inform treatment decisions. This will advance pediatric rheumatology from counting joints to realizing the potential of using data available from uncovering patterns of joint involvement.

Using Chromatin Architecture to Understand the Genetics and Transcriptomics of Juvenile Idiopathic Arthritis

The presence of abnormal gene expression signatures is a well-described feature of the oligoarticular and polyarticular forms of juvenile idiopathic arthritis. In this review, we discuss how new insights into genetic risk for JIA and the three dimensional architecture of the genome may be used to develop a better understanding of the mechanisms driving these gene expression patterns.

Toward New Classification Criteria for Juvenile Idiopathic Arthritis: First Steps, Pediatric Rheumatology International Trials Organization International Consensus

OBJECTIVE

To revise the current juvenile idiopathic arthritis (JIA) International League of Associations for Rheumatology (ILAR) classification criteria with an evidence-based approach, using clinical and routine laboratory measures available worldwide, to identify homogeneous clinical groups and to distinguish those forms of chronic arthritis typically seen only in children from the childhood counterpart of adult diseases.

METHODS

The overall project consists of 4 steps. This work represents Step 1, a Delphi Web-based consensus and Step 2, an international nominal group technique (NGT) consensus conference for the new provisional Pediatric Rheumatology International Trials Organization JIA classification criteria. A future large data collection of at least 1000 new-onset JIA patients (Step 3) followed by analysis and NGT consensus (Step 4) will provide data for the evidence-based validation of the JIA classification criteria.

RESULTS

In Step 1, three Delphi rounds of interactions were implemented to revise the 7 ILAR JIA categories. In Step 2, forty-seven questions with electronic voting were implemented to derive the new proposed criteria. Four disorders were proposed: (a) systemic JIA; (b) rheumatoid factor-positive JIA; (c) enthesitis/spondylitis-related JIA; and (d) early-onset antinuclear antibody-positive JIA. The other forms were gathered under the term "others." These will be analyzed during the prospective data collection using a list of descriptors to see whether the clustering of some of them could identify homogeneous entities.

CONCLUSION

An international consensus was reached to identify different proposed homogeneous chronic disorders that fall under the historical term JIA. These preliminary criteria will be formally validated with a dedicated project.

Review: Genetics and the Classification of Arthritis in Adults and Children

Current classification of primary inflammatory arthritis begins from the assumption that adults and children are different. No form of juvenile idiopathic arthritis bears the same name as an adult arthritis, a nomenclature gap with implications for both clinical care and research. Recent genetic data have raised questions regarding this adult/pediatric divide, revealing instead broad patterns that span the age spectrum. Combining these genetic patterns with demographic and clinical data, we propose that inflammatory arthritis can be segregated into 4 main clusters, largely irrespective of pediatric or adult onset: seropositive, seronegative (likely including a distinct group that usually begins in early childhood), spondyloarthritis, and systemic. Each of these broad clusters is internally heterogeneous, highlighting the need for further study to resolve etiologically discrete entities. Eliminating divisions based on arbitrary age cutoffs will enhance opportunities for collaboration between adult and pediatric rheumatologists, thereby helping to promote the understanding and treatment of arthritis.

Cow's Milk Allergy in Infancy and Later Development of Juvenile Idiopathic Arthritis: A Register-Based Case-Control Study

We examined the association between cow's milk allergy (CMA) and juvenile idiopathic arthritis (JIA). The material for this case-control study was collected from national registers of all children born in Finland between 2000 and 2010 and diagnosed with JIA (n = 1,298) and age-, sex-, and place-matched controls (n = 5,179). We identified 235 children with CMA; 66 of these children also had JIA. A conditional logistic regression analysis was performed to evaluate the association between CMA and JIA and to test whether exposure to antibiotics would be a covariate for this association. In boys (but not in girls), a diagnosis of CMA and the use of hypoallergenic formula in infancy were associated with the later development of JIA (odds ratio = 2.4, 95% confidence interval: 1.6, 3.6). The association was most evident in boys who were diagnosed with JIA before age 3 years or diagnosed with CMA with predominantly gastrointestinal symptoms. There was no statistically significant additive interaction between CMA and antibiotic exposure in the later development of JIA. These associations may reflect impaired maturation of intestinal immunity and integrity in boys with a risk of JIA. Predisposing factors related to JIA pathogenesis seem to display a sex-linked disparity.

Dynamic contrast-enhanced magnetic resonance imaging can play a role in predicting flare in juvenile idiopathic arthritis

PURPOSE

The study was performed to determine whether conventional and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters of a previously affected target joint in patients with clinically inactive juvenile idiopathic arthritis (JIA) have prognostic meaning for a flare of joint inflammation during follow-up.

MATERIAL AND METHODS

Thirty-two JIA patients with clinically inactive disease at the time of MRI of the knee were prospectively included. DCE-MRI provided both descriptive measures and time-intensity-curve shapes, representing functional properties of the synovium. Conventional MRI outcome measures included validated scores for synovial hypertrophy, bone marrow edema, cartilage lesions and bone erosions. During a 2-year period the patients were monitored by their pediatric rheumatologist and clinical flares were registered.

RESULTS

MRI analysis revealed synovial hypertrophy in 13 (39.4%) of the clinically inactive patients. Twelve patients (37.5%) had at least one flare during 2-year clinical follow-up. Persistently inactive and flaring patients differed significantly in the maximum enhancement of the synovium on the DCE-MRI (p<0.05), whereas no difference was found between these two groups in any of the baseline scores of conventional MRI.

CONCLUSIONS

Our prospective clinical follow-up study indicates that the assessment of 'maximum enhancement' upon DCE-MRI may be able to predict a clinical flare within 2 years in inactive JIA patients.

Enhancing translational research in paediatric rheumatology through standardization

The past decade has seen many successes in translational rheumatology, from dramatic improvements in outcomes brought about by novel biologic therapies, to the discovery of new monogenic inflammatory disorders. Advances in molecular medicine, combined with progress towards precision care, provide an excellent opportunity to accelerate the translation of biological understanding to the bedside. However, although the field of rheumatology is a leader in the standardization of data collection and measures of disease activity, it lags behind in standardization of biological sample collection and assay performance. Uniform approaches are necessary for robust collaborative research, particularly in rare diseases. Standardization is also critical to increase reproducibility between centres, a prerequisite for clinical implementation of translational research. This Perspectives article emphasizes the need for standardization and implementation of best practices, presented in the context of lessons learned from international biorepository networks.