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Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease in childhood and the temporomandibular joint (TMJ) is often involved. TMJ arthritis in growing individuals can cause deformation of facial skeleton (dentofacial deformity) and TMJ components (TMJ deformity). Treatment outcome hinges on early initiation of anti-inflammatory treatment and orthopaedic treatment with dental splints. The aim of the present study was to characterize the radiological signs of dentofacial deformity in patients with a JIA-induced need for orthopaedic treatment. We retrospectively studied 96 patients with JIA and 20 non-JIA controls to identify the initial radiological signs of JIA-induced dentofacial deformity leading to initiation of orthopaedic treatment. We found that initial radiological signs of dentofacial deformities were subtle and characterized by minor mandibular asymmetry and occlusal plane steepening. Radiological findings of TMJ deformity associated with initial dentofacial deformity were frequent and characterized by condylar articular surface flattening (OR 8.42), condylar subcortical cyst (OR 5.94), condylar surface erosion (OR 5.38) and condylar deviation in form (OR 25.39). Radiological signs of TMJ deformity were also documented in TMJs considered “healthy” during initial clinical and radiological examination. This study presents new knowledge of importance for early diagnosis of dentofacial deformity in JIA. Early diagnosis of dentofacial deformity is important as treatment outcome is greatly influenced by timely initiation.

Acute lymphoblastic leukemia (ALL) may present with arthritis implying the risk of being misdiagnosed as juvenile idiopathic arthritis (JIA). The aim of this study was to identify predictors for ALL based on clinical and laboratory information. This cross-sectional, retrospective study compared clinical presentation and laboratory results of 26 children with ALL and arthritis versus 485 children with JIA (433 non-systemic, 52 systemic JIA). Using a Bayesian score approach the findings were evaluated by calculating odds ratios (OR) and lnOR as a measure of diagnostic weight. Distinction on clinical grounds was difficult, as even a high number of joints involved did not exclude ALL. One or more hematologic cell counts were low (Hb <10 g/dL, platelet count <100 x 10.sup.9 /L, neutrophil count < 1.0 x 10.sup.9 /L) in 92% with ALL, 25% with systemic JIA and 10% with non-systemic JIA. Neutropenia and thrombocytopenia had the highest ORs of 128 (95% CI 43-387) and 129 (95% CI 26-638), each giving a diagnostic weight of 4. The estimated risks of ALL were 0.2% with normal cell counts and 9%, 67% and 100% when one, two or three cell lines were affected. A simple count of cell lines with low counts can serve as a basic diagnostic strategy. Children with tri- or bilinear involvement should be referred to a bone marrow, and those with unilinear involvement a thorough screen for further evidence of ALL (organomegaly, ESR, LDH, uric acid, and blood smear).

Background In juvenile idiopathic arthritis (JIA) clinical remission is unattainable in some patients despite modern biologic disease-modifying antirheumatic drugs (bDMARD) therapy and switching bDMARD is required. The best choice of second-line bDMARD remains unclear. This retrospective observational study aims to describe the pattern, timing, frequency, and reasons for bDMARD switching among children diagnosed with non-systemic JIA. Methods Patients were identified by combining unique personal identification numbers, the International Code of Diagnosis (ICD10) for JIA and biologic therapy. Clinical characteristics were collected retrospectively from the electronic medical records. Included were 200 children diagnosed with non-systemic JIA initiating their first biologic drug between January 1st, 2012, and March 1st, 2021. We compared characteristics of non-switchers vs switchers and early switchers (≤ 6 months) vs late switchers (> 6 months). Results The median age at diagnosis was 7.7 years. We found that 37% switched to a different bDMARD after a median age of 6.3 years after diagnosis. In total, and 17.5% of patients switched at least twice, while 6% switched three or more times. The most common reason for switching was inefficacy (57%) followed by injection/infusion reactions (15%) and uveitis (13%). 77% were late switchers, and switched primarily due to inefficacy. All patients started a tumor necrosis factor inhibitor (TNFi) as initial bDMARD (Etanercept (ETN): 49.5%, other TNFis: 50.5%). The patients who started ETN as first-line bDMARD were more likely to be switchers compared to those who started another TNFi. Conclusion During a median 6.3-year follow-up biologic switching was observed in more than one third, primarily due to inefficacy. Key points 1. Biologic switching was common and found in more than one third of the patients 2. The reason for switching was mainly inefficacy 3. More than 90% switched to a second TNFi

Background To study for the first-time, pain perception, pain sensitivity, and self-reported pain in young adults with long disease duration of juvenile idiopathic arthritis (JIA) compared with controls. Methods Children from Central Norway diagnosed with JIA between 1997 and 2004 were included consecutively in a population-based prospective study. Children with onset 1997–2000 were part of the Nordic JIA cohort. Controls were age- and sex-matched. In 2015–2017, study visits with investigator-blinded quantitative sensory testing (QST) comprising cold and warm detection thresholds (CDT/WDT), cold and heat pain thresholds (CPT/HPT), pressure pain threshold (PPT), and a suprathreshold heat pain test were performed. We constructed separate multilevel models for each variable of detection and pain thresholds with interaction between groups and site adjusted for the effect of age and sex. Results Among 96 young adults with JIA, 71% were female, median age was 22.7 years, disease duration was 16.1 years, and 47% had oligoarticular disease. Among 109 controls, 71% were female, and median age was 23.5 years. Participants with JIA had lower pressure pain thresholds (PPTs) (95% CI) compared to controls, upper limb 888 (846,930) versus 1029 (999,1059) kPa and lower limb 702 (670,734) versus 760 (726,794) kPa. Participants with inactive disease had the lowest PPTs and cold pain thresholds (CPTs), compared to those in remission off medication and those with active disease. Minor differences were found regarding CDT/WDT and CPT/HPT in JIA compared to controls. The median (IQR) temperature needed to evoke pain = 6 on a 0–10 numeric rating scale (NRS) in the suprathreshold heat pain tests were lower in JIA than in controls (46 °C (45–47 °C) versus 47 °C (46–48 °C)). We found no associations between self-reported pain and pain thresholds. Conclusions Our results indicate for the first time that young adults with long disease duration of JIA may have altered pain perception and sensitivity compared to controls. A clinical implication may be the importance of early treatment to quickly achieve pain-free remission and avoid long-term pain sensitization.

Juvenile idiopathic arthritis (JIA) commonly affects the temporomandibular joint (TMJ), leading to dentofacial deformities and orofacial symptoms. Timely diagnosis and treatment initiation are essential for optimizing patient outcomes. However, clinical examination—the primary screening method— has limited accuracy, increasing the risk of delayed TMJ involvement detection. This study develops, trains and tests an artificial intelligence (AI) model for predicting TMJ involvement in newly diagnosed JIA patients and compares its model performance with expert clinician assessments for validation. A longitudinal dataset of 6,153 standardized orofacial examinations from 1,054 patients with JIA was used to train an Extreme Gradient Boosting (XGBoost) model to predict TMJ involvement. An independent cohort of 55 newly diagnosed patients was used to evaluate the model. Twenty-six clinically relevant features were selected and preprocessed for model input. Model performance was evaluated based on classification accuracy and concordance with expert clinician assessments. Model interpretability was analysed using Shapley additive explanations (SHAP) to identify key predictive features. The XGBoost model achieved an overall accuracy of 85.5% in predicting TMJ involvement. Model predictions showed significant concordance with expert clinician assessments ( p  < 0.001), although the model identified a higher prevalence of TMJ involvement than experts. The most influential predictive features were reduced condylar translation, facial asymmetry, protrusion, patient-reported orofacial pain and reduced mouth-opening capacity. The developed AI model demonstrates strong predictive performance for TMJ involvement based on clinical examination. By facilitating earlier detection, the model has the potential to support clinical decision-making, enable timely intervention, and improve patient outcomes.

This investigation aimed to develop a radiographic 3D cephalometric index to grade severity of dentofacial deformity in patients with juvenile idiopathic arthritis (JIA), and to perform a validation against expert evaluations. Data were collected from a population-based Nordic JIA cohort of 240 patients that received a cone-beam computed tomography (CBCT) scan approximately 17 years after onset of JIA. The cohort was randomized into two groups: A baseline group for establishing the index (n = 210) and a test group (n = 30). The standardized index generation process led to the inclusion of six radiographic outcome measures, each representing different facets of dentofacial deformities. Specific severity scores were established for three domains; dentofacial asymmetry (0–8), vertical deviation (0–8), and sagittal deviation (0–8). An overall dentofacial deformity index score was computed by aggregating the scores from the six variables, with the total score ranging from 0 to 24. The validity of the index was assessed by comparing the index score with expert evaluations. Based on the generated index, 10 patients exhibited severe deformities. Reliable agreement between data-generated index scores and expert assessments was found. A 3D radiographic dentofacial deformity index was generated and tested. This index can support an objective assessment of the severity of dentofacial deformities related to JIA in a clinical setting and future research.

Background The aim was to develop prediction rules that may guide early treatment decisions based on baseline clinical predictors of long-term unfavorable outcome in juvenile idiopathic arthritis (JIA). Methods In the Nordic JIA cohort, we assessed baseline disease characteristics as predictors of the following outcomes 8 years after disease onset. Non-achievement of remission off medication according to the preliminary Wallace criteria, functional disability assessed by Childhood Health Assessment Questionnaire (CHAQ) and Physical Summary Score (PhS) of the Child Health Questionnaire, and articular damage assessed by the Juvenile Arthritis Damage Index-Articular (JADI-A). Multivariable models were constructed, and cross-validations were performed by repeated partitioning of the cohort into training sets for developing prediction models and validation sets to test predictive ability. Results The total cohort constituted 423 children. Remission status was available in 410 children: 244 (59.5%) of these did not achieve remission off medication at the final study visit. Functional disability was present in 111/340 (32.7%) children assessed by CHAQ and 40/199 (20.1%) by PhS, and joint damage was found in 29/216 (13.4%). Model performance was acceptable for making predictions of long-term outcome. In validation sets, the area under the curves (AUCs) in the receiver operating characteristic (ROC) curves were 0.78 (IQR 0.72–0.82) for non-achievement of remission off medication, 0.73 (IQR 0.67–0.76) for functional disability assessed by CHAQ, 0.74 (IQR 0.65–0.80) for functional disability assessed by PhS, and 0.73 (IQR 0.63–0.76) for joint damage using JADI-A. Conclusion The feasibility of making long-term predictions of JIA outcome based on early clinical assessment is demonstrated. The prediction models have acceptable precision and require only readily available baseline variables. Further testing in other cohorts is warranted.

Background The aim of this long-term follow-up study was to compare the disease characteristics of HLA-B27 positive and negative patients with juvenile idiopathic arthritis (JIA). Methods The study is a cohort study with consecutive cases of newly diagnosed Finnish patients with JIA according to the International League of Associations for Rheumatology (ILAR) criteria [ 1 ]. Patients were enrolled between 1997 and 2000 from a defined area of Southern Finland. Clinical data including disease activity and serology were registered during a mean period of 17.5 years. Results 159 patients completed the 18-year follow-up study. HLA-B27 was available for 151 patients, of which 25% were HLA-B27 positive. Chronic uveitis was diagnosed in 30% of HLA-B27 positive and 29% of HLA-B27 negative patients. HLA-B27 positive patients had a lower prevalence of temporomandibular (TMJ) involvement than the antigen negative ones, 19% versus 28%. None of the HLA-B27 positive patients had cervical spine affected compared to 11% of antigen negative patients (p = 0.022). Of the HLA-B27 positive patients, 54% had had biological medication at some point during follow-up versus 25% in the negative group (p = 0.003). At last follow-up, 32% of antigen positive patients were not in remission compared to 18% of the antigen negative (p = 0.017). Conclusions The use of biological medication was more common in HLA-B27 positive patients with JIA. At the 18-year follow-up, more antigen positive patients had active disease compared HLA-B27 negative patients. This real-world follow-up study indicates that the prospects for worse outcome with HLA-B27 positivity in long-term should be taken into consideration.

Background Models to predict disease course and long-term outcome based on clinical characteristics at disease onset may guide early treatment strategies in juvenile idiopathic arthritis (JIA). Before a prediction model can be recommended for use in clinical practice, it needs to be validated in a different cohort than the one used for building the model. The aim of the current study was to validate the predictive performance of the Canadian prediction model developed by Guzman et al. and the Nordic model derived from Rypdal et al. to predict severe disease course and non-achievement of remission in Nordic patients with JIA. Methods The Canadian and Nordic multivariable logistic regression models were evaluated in the Nordic JIA cohort for prediction of non-achievement of remission, and the data-driven outcome denoted severe disease course. A total of 440 patients in the Nordic cohort with a baseline visit and an 8-year visit were included. The Canadian prediction model was first externally validated exactly as published. Both the Nordic and Canadian models were subsequently evaluated with repeated fine-tuning of model coefficients in training sets and testing in disjoint validation sets. The predictive performances of the models were assessed with receiver operating characteristic curves and C-indices. A model with a C-index above 0.7 was considered useful for clinical prediction. Results The Canadian prediction model had excellent predictive ability and was comparable in performance to the Nordic model in predicting severe disease course in the Nordic JIA cohort. The Canadian model yielded a C-index of 0.85 (IQR 0.83–0.87) for prediction of severe disease course and a C-index of 0.66 (0.63–0.68) for prediction of non-achievement of remission when applied directly. The median C-indices after fine-tuning were 0.85 (0.80–0.89) and 0.69 (0.65–0.73), respectively. Internal validation of the Nordic model for prediction of severe disease course resulted in a median C-index of 0.90 (0.86–0.92). Conclusions External validation of the Canadian model and internal validation of the Nordic model with severe disease course as outcome confirm their predictive abilities. Our findings suggest that predicting long-term remission is more challenging than predicting severe disease course.

Background With juvenile idiopathic arthritis (JIA), there are several protocols and practices used worldwide for the transition from paediatric to adult care. In this study, we examined the transferral rates and disease activity after transition, as well as the disease- and health-related outcomes. We also introduce the transition practices employed in the Nordic countries. Methods The study population comprised 408 participants with a disease onset from 1997 to 2000 who attended an 18-year follow-up visit in this population-based Nordic JIA cohort study. The patients were retrospectively divided into three subgroups: Patients transferred directly from paediatric care to adult rheumatology care, patients referred there later, and patients never transferred during the 18-year follow-up period. Results One hundred and sixty-three (40%) JIA patients had been directly transferred to an adult clinic. The cumulative transition rate was 52%, but there were significant differences between the participating centres. Fifty patients had later been referred to an adult clinic. Among the 195 patients who had never been transferred, 39% were found to have disease activity at the study visit. Conclusion This study highlights the need to reconsider transition practices to avoid our undesirable finding of patients with disease activity in JIA, but no appropriate health care follow-up.