Prediction of 3 months MTX non-response (DAS28>3.2) in early rheumatoid arthritis
Validated prediction model to identify DMARD-naïve rheumatoid arthritis patients with high risk of insufficient response to MTX.
Research authors: Gosselt HR, Verhoeven MMA, de Rotte MCFJ, Pluijm SMF, Muller IB, Jansen G, Tekstra J, Bulatović-Ćalasan M, Heil SG, Lafeber FPJG, Hazes JMW, and de Jonge R.
Details Formula Study characteristics Files & References
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MeSH terms
  • Methotrexate
  • Clinical Prediction Rule
  • Rheumatoid Arthritis
  • Model type
    Custom model (Calculation)

    Additional information

    Objective: Methotrexate (MTX) constitutes first-line therapy in rheumatoid arthritis (RA), yet approximately 30% of the patients do not benefit from MTX. Recently, we reported a prognostic multivariable prediction model for insufficient clinical response to MTX at three months of  treatment in the Rotterdam Early Arthritis Cohort (tREACH). The purpose of the current study was to externally validate and clinically calibrate this prediction model.

    Methods: Erythrocyte folate and single nucleotide polymorphisms (SNPs) were assessed in 91 early DMARD-naïve RA patients starting MTX in the U-Act-Early cohort. Insufficient response (disease activity score: DAS28>3.2) was determined after three months and non-response after six months of therapy. The previously developed prediction model including baseline predictors: DAS28, Health Assessment Questionnaire (HAQ), erythrocyte folate, SNPs (ABCB1, ABCC3), smoking and BMI, was considered successfully validated in the U-Act-Early if the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was not significantly lower than in tREACH.

    Results: The AUCs in U-Act-Early at three and six months were 0.75 (95% CI: 0.64-0.85) and 0.71 (95% CI: 0.60-0.82) respectively, similar as in the tREACH derivation cohort. Calibration of the model showed that baseline DAS28>5.1 and HAQ>0.6 were the strongest predictors. Erythrocyte folate and lifestyle parameters (BMI/smoking) were required to classify 75% correctly, whereas SNPs had a minimal contribution.  

    Conclusion: We successfully validated and calibrated our recently reported prediction model. The Evidencio online platform could assist clinicians in shared decision-making to intensify treatment when appropriate. Taken together, evaluation of this prediction model in a clinical trial is warrented.  

    Study Population

    Total population size: 368
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    Females: {{ model.numberOfFemales }}

    Categorical characteristics

    Name Subset / Group Nr. of patients
    DAS28 at 3 months >3.2 177
    ≤3.2 191
    DAS28 at 6 months >3.2 145
    ≤3.2 223
    Prediction of 3 months MTX non-response (DAS28>3.2) in early rheumatoid arthritis
    Refer to Intended Use for instructions before use
    Evidencio B.V., Irenesingel 19, 7481 GJ, Haaksbergen, the Netherlands

    Probability of MTX non-response after 3 months of treatment:

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    Probability of MTX non-response after 3 months of treatment: %

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    Outcome stratification

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    Conditional information

    Result interpretation

    How this model should be used: 
    This prediction model could assist in identification of insufficient responders at diagnosis. For patients with high probability of insufficient response to MTX, additional biologics or JAK-inhibitors could be prescribed. For those with low probabilities of insufficient response, these expensive treatments could be spared. This distinction at diagnosis could save precious time for insufficient responders, allowing earlier control of disease activity resulting in better long-term outcomes.

    Model performance: 
    Discriminative power of the model was assessed through evaluating the area under the receiver operating characteristic curve (AUC). The AUC of the model was 0.75 (95% CI: 0.69 – 0.81), indicating that the model correctly classified patients in 75% of the cases.

    Goodness-of-fit between the predicted probabilities and observed values was tested using the Hosmer-Lemeshow test. The associated P-value was 0.634, indicating good model fit. 

    Decisions on appropriate risk cut-offs:
    Taking into consideration the “window of opportunity” for optimal treatment we consider it crucial to adequately treat insufficient MTX responders with additional bDMARDs/tsDMARDs. Therefore, our goal for this prediction model was to identify as many insufficient responders as possible, while at the same time attempting to restrict the use of bDMARDs/tsDMARDs to those patients who really need them, hence to avoid misclassification of sufficient responders. Considering this, a cut-off probability of 70% (of insufficient response) could be chosen.

    At this cut-off, 75% of patients classified as insufficient responder match actual insufficient responders (PPV) and could be treated with additional bDMARDs/tsDMARDs. Additionally, at this cut-off 86% of all sufficient responders would be correctly classified as such (specificity) and could be spared additional treatment.

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    Calculations alone should never dictate patient care, and are no substitute for professional judgement. See our full disclaimer.

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