These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Prediction of clinical non-response to methotrexate treatment in juvenile idiopathic arthritis.
    Author: Bulatovic M, Heijstek MW, Van Dijkhuizen EH, Wulffraat NM, Pluijm SM, de Jonge R.
    Journal: Ann Rheum Dis; 2012 Sep; 71(9):1484-9. PubMed ID: 22473625.
    Abstract:
    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.
    [Abstract] [Full Text] [Related] [New Search]