BackgroundMethotrexate (MTX) is the cornerstone disease-modifying anti-rheumatic drug in juvenile idiopathic arthritis (JIA). In JIA, it is important to start effective treatment early to avoid long-term sequelae, such as joint damage. To accomplish this goal, it is crucial to know beforehand who is going to respond well to MTX. In addition, MTX adverse effects such as MTX intolerance occur frequently, potentially hindering its efficacy. To avoid inefficacy of an otherwise effective drug, the physician should be timely aware of these adverse events. Consequently, to optimise treatment of JIA patients with MTX, predictors for efficacy and adverse events should be used in daily clinical practice. The aim of this study was to summarise the existing knowledge about such predictors.MethodsA systematic literature search was performed in PubMed, Embase and The Cochrane Library, and 1,331 articles were identified. These were selected based on their relevance to the topic and critically appraised according to pre-defined criteria. Predictors for MTX efficacy and adverse events were extracted from the literature and tabulated.ResultsTwenty articles were selected. The overall quality of the studies was good. For MTX efficacy, candidate predictors were antinuclear antibody positivity, the childhood health assessment questionnaire score, the myeloid-related protein 8/14 level, long-chain MTX polyglutamates, bilateral wrist involvement and some single nucleotide polymorphisms (SNPs) in the adenosine triphosphate binding cassette and solute carrier transporter gene families. For MTX adverse events, potential predictors were alanine aminotransferase and thrombocyte level and two SNPs in the γ-glutamyl hydrolase and methylenetetrahydrofolate reductase genes. However, validation of most predictors in independent cohorts was still lacking.ConclusionsInteresting candidate predictors were found, especially for MTX efficacy. However, most of these were not validated. This should be the goal of future efforts. A clinically relevant way to validate the predictors is by means of creating a clinical prediction model.Electronic supplementary materialThe online version of this article (doi:10.1186/1546-0096-12-51) contains supplementary material, which is available to authorized users.
Juvenile dermatomyositis (JDM) is the most common form of the juvenile idiopathic inflammatory myopathies (IIM) characterised by muscle and skin inflammation, leading to symmetric proximal muscle weakness and cutaneous symptoms. It has a fluctuating course and varying prognosis. In a Bayesian framework, we develop a joint model for four longitudinal outcomes, which accounts for within individual variability as well as inter-individual variability. Correlations among the outcome variables are introduced through a subject-specific random effect. Moreover, we exploit an approach similar to a hurdle model to account for excess of a specific outcome in the response. Clinical markers and symptoms are used as covariates in a regression set-up. Data from an ongoing observational cohort study are available, providing information on 340 subjects, who contributed 2725 clinical visits. The model shows good performance and yields efficient estimations of model parameters, as well as accurate predictions of the disease activity parameters, corresponding well to observed clinical patterns over time. The posterior distribution of the by-subject random intercepts shows a substantial correlation between two of the outcome variables. A subset of clinical markers and symptoms are identified as associated with disease activity. These findings have the potential to influence clinical practice as they can be used to stratify patients according to their prognosis and guide treatment decisions, as well as contribute to on-going research about the most relevant outcome markers for patients affected by JDM.
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