Introduction: The aim of this study was to determine the prevalence of gastrointestinal and behavioural symptoms occurring before (anticipatory/associative) and after methotrexate (MTX) administration, termed MTX intolerance, in rheumatoid (RA) and psoriatic arthritis (PsA). Methods: Methotrexate Intolerance Severity Score (MISS), previously validated in juvenile idiopathic arthritis patients, was used to determine MTX intolerance prevalence in 291 RA/PsA patients. The MISS consisted of four domains: abdominal pain, nausea, vomiting and behavioural symptoms, occurring upon, prior to (anticipatory) and when thinking of MTX (associative). MTX intolerance was defined as ≥6 on the MISS with ≥1 point on anticipatory and/or associative and/or behavioural items. Results: A total of 123 patients (42.3%) experienced at least one gastrointestinal adverse effect. The prevalence of MTX intolerance was 11%. MTX intolerance prevalence was higher in patients on parenteral (20.6%) than on oral MTX (6.2%) (p < 0.001). Conclusion: Besides well-known gastrointestinal symptoms after MTX, RA and PsA patients experienced these symptoms also before MTX intake. RA and PsA patients on MTX should be closely monitored with the MISS for early detection of MTX intolerance, in order to intervene timely and avoid discontinuation of an effective treatment.

Objectives Methotrexate (MTX) is a cheap and efficacious drug in juvenile idiopathic arthritis (JIA) treatment. If JIA patients are unresponsive to MTX, early and effective combination treatment with biologicals is required to prevent joint damage. The authors developed a prediction model to identify JIA patients not responding to MTX. Methods In a cohort of 183 JIA patients, clinical variables and single nucleotide polymorphisms (SNPs) in genes involved in the mechanism of action of MTX were determined at the start of MTX treatment. These variables were used to construct a prediction model for non-response to MTX treatment during the first year of treatment. Non-response to MTX was defined according the American College of Rheumatology paediatric 70 criteria. The prediction model was validated in a cohort of 104 JIA patients. Results The prediction model included: erythrocyte sedimentation rate and SNPs in genes coding for methionine synthase reductase, multidrug resistance 1 (MDR-1/ABCB1), multidrug resistance protein 1 (MRP-1/ABCC1) and proton-coupled folate transporter (PCFT). The area under the receiver operating characteristics curve (AUC) was 0.72 (95% CI: 0.63 to 0.81). In the validation cohort, the AUC was 0.65 (95% CI: 0.54 to 0.77). The prediction model was transformed into a total risk score (range 0–11). At a cut-off of ≥3, sensitivity was 78%, specificity 49%, positive predictive value was 83% and negative predictive value 41%. Conclusions The prediction model that we developed and validated combines clinical and genetic variables to identify JIA patients not responding to MTX treatment. This model could assist clinicians in making individualised treatment decisions.

The objectives of this study were to assess 27-joint Juvenile Arthritis Disease Activity Score (JADAS-27) responsiveness, JADAS-27 changes corresponding to clinically important differences and cut-off scores for low and high disease activity in a large prospective JIA cohort. JADAS-27 responsiveness, using effect size and standardized response mean (SRM), and changes in the JADAS-27 corresponding to clinically important differences were determined for clinical improvement (ACRpedi30) and worsening (flare). To assess whether various degrees of change in the JADAS-27 could be used to demonstrate improvement or worsening in individual patients, diagnostic parameters were computed for cut-off score changes. Finally, cut-off scores for low and high disease activity and their diagnostic parameters were determined. In 228 patients with 529 consecutive visits, ACRpedi30 was detected in 109 and flare in 111 visits. Regarding responsiveness, the effect size was 0.93 and SRM was 1.26 for clinical improvement, while for clinical worsening the effect size was 0.65 and SRM was 0.60. Changes in the JADAS-27 corresponding to clinically important difference were -5.5 for improvement and +1.7 for worsening. Cut-off score changes in the JADAS-27 had 65-90% sensitivity and 67-86% specificity for improvement, and 31-64% sensitivity and 89-97% specificity for worsening. The JADAS-27 cut-off score for low disease activity was ≤2.7 with 76% sensitivity and 62% specificity, and the cut-off score for high disease activity was ≥6 with 77% sensitivity and 77% specificity. The JADAS-27 had moderate to good responsiveness and was changed by clinically important differences. The JADAS-27 cut-off scores differentiated between low and high disease activity. These JADAS-27 interpretations could be potentially applicable in clinical care and trials.

Methotrexate (MTX) is an effective and safe drug in the treatment of juvenile idiopathic arthritis (JIA). Despite its safety, MTX-related gastrointestinal adverse effects before and after MTX administration, termed MTX intolerance, occur frequently, leading to non-compliance and potentially premature MTX termination. The aim of this study was to construct a risk model to predict MTX intolerance. In a prospective JIA cohort, clinical variables and single nucleotide polymorphisms were determined at MTX start. The Methotrexate Intolerance Severity Score was employed to measure MTX intolerance in the first year of treatment. MTX intolerance was most prevalent at 6 or 12 months after MTX start, which was defined as the outcome for the prediction model. The model was developed in 152 patients using multivariable logistic regression analysis and subsequently internally validated using bootstrapping. The prediction model included the following predictors: JIA category, antinuclear antibody, parent/patient assessment of pain, Juvenile Arthritis Disease Activity Score-27, thrombocytes, alanine aminotransferase and creatinine. The model classified 77.5% of patients correctly, and 66.7% of patients after internal validation by bootstrapping. The lowest predicted risk of MTX intolerance was 18.9% and the highest predicted risk was 85.9%. The prediction model was transformed into a risk score (range 0–17). At a cut-off of ≥6, sensitivity was 82.0%, specificity 56.1%, positive predictive value was 58.7% and negative predictive value 80.4%. This clinical prediction model showed moderate predictive power to detect MTX intolerance. To develop into a clinically usable tool, it should be validated in an independent cohort and updated with new predictors. Such an easy-to-use tool could then assist clinicians in identifying patients at risk to develop MTX intolerance, and in turn to monitor them closely and intervene timely in order to prevent the development of MTX intolerance. ISRCTN register, www.isrctn.com , ISRCTN13524271

The goals of this study were to examine whether machine-learning algorithms outperform multivariable logistic regression in the prediction of insufficient response to methotrexate (MTX); secondly, to examine which features are essential for correct prediction; and finally, to investigate whether the best performing model specifically identifies insufficient responders to MTX (combination) therapy. The prediction of insufficient response (3-month Disease Activity Score 28-Erythrocyte-sedimentation rate (DAS28-ESR) > 3.2) was assessed using logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting (XGBoost). The baseline features of 355 rheumatoid arthritis (RA) patients from the “treatment in the Rotterdam Early Arthritis CoHort” (tREACH) and the U-Act-Early trial were combined for analyses. The model performances were compared using area under the curve (AUC) of receiver operating characteristic (ROC) curves, 95% confidence intervals (95% CI), and sensitivity and specificity. Finally, the best performing model following feature selection was tested on 101 RA patients starting tocilizumab (TCZ)-monotherapy. Logistic regression (AUC = 0.77 95% CI: 0.68–0.86) performed as well as LASSO (AUC = 0.76, 95% CI: 0.67–0.85), random forest (AUC = 0.71, 95% CI: 0.61 = 0.81), and XGBoost (AUC = 0.70, 95% CI: 0.61–0.81), yet logistic regression reached the highest sensitivity (81%). The most important features were baseline DAS28 (components). For all algorithms, models with six features performed similarly to those with 16. When applied to the TCZ-monotherapy group, logistic regression’s sensitivity significantly dropped from 83% to 69% (p = 0.03). In the current dataset, logistic regression performed equally well compared to machine-learning algorithms in the prediction of insufficient response to MTX. Models could be reduced to six features, which are more conducive for clinical implementation. Interestingly, the prediction model was specific to MTX (combination) therapy response.

Objective. The balance between Treg and effector T cells (Teff) is crucial for immune regulation in JIA. How MTX, the cornerstone treatment in JIA, influences this balance in vivo is poorly elucidated. The aim of this study was to investigate quantitative and qualitative effects of MTX on Treg and Teff in JIA patients during MTX treatment. Methods. Peripheral blood samples were obtained from JIA patients at the start of MTX and 3 and 6 months thereafter. Treg numbers and phenotypes were determined by flow cytometry and suppressive function in allogeneic suppression assays. Teff proliferation upon stimulation with anti-CD3, activation status and intracellular cytokine production were determined by flow cytometry. Effector cell responsiveness to suppression was investigated in autologous suppression assays. Effector cell cytokines in supernatants of proliferation and suppression assays and in plasma were measured by cytokine multiplex assay. Results. MTX treatment in JIA did not affect Treg phenotype and function. Instead, MTX treatment enhanced, rather than diminished, CD4⁺ and CD8⁺ T cell proliferation of JIA patients after 6 months of therapy, independent of clinical response. Effector cells during MTX treatment were equally responsive to Treg-mediated suppression. MTX treatment did not attenuate Teff activation status and their capacity to produce IL-13, IL-17, TNF-α and IFN-γ. Similarly to Teff proliferation, plasma IFN-γ concentrations after 6 months were increased. Conclusion. This study provides the novel insight that MTX treatment in JIA does not attenuate Teff function but, conversely, enhances T cell proliferation and IFN-γ plasma concentrations in JIA patients.