Dataset Information

Development and validation of a prognostic multivariable model to predict insufficient clinical response to methotrexate in rheumatoid arthritis.

ABSTRACT:

Objective

The objective was to predict insufficient response to 3 months methotrexate (MTX) in DMARD naïve rheumatoid arthritis patients.

Methods

A Multivariable logistic regression model of rheumatoid arthritis patients starting MTX was developed in a derivation cohort with 285 patients starting MTX in a clinical multicentre, stratified single-blinded trial, performed in seven secondary care clinics and a tertiary care clinic. The model was validated in a validation cohort with 102 patients starting MTX at a tertiary care clinic. Outcome was insufficient response (disease activity score (DAS)28 >3.2) after 3 months of MTX treatment. Clinical characteristics, lifestyle variables, genetic and metabolic biomarkers were determined at baseline in both cohorts. These variables were dichotomized and used to construct a multivariable prediction model with backward logistic regression analysis.

Results

The prediction model for insufficient response in the derivation cohort, included: DAS28>5.1, Health Assessment Questionnaire>0.6, current smoking, BMI>25 kg/m2, ABCB1 rs1045642 genotype, ABCC3 rs4793665 genotype, and erythrocyte-folate<750 nmol/L. In the derivation cohort, AUC of ROC curve was 0.80 (95%CI: 0.73-0.86), and 0.80 (95%CI: 0.69-0.91) in the validation cohort. Betas of the prediction model were transformed into total risk score (range 0-8). At cutoff of ≥4, probability for insufficient response was 44%. Sensitivity was 71%, specificity 72%, with positive and negative predictive value of 72% and 71%.

Conclusions

A prognostics prediction model for insufficient response to MTX in 2 prospective RA cohorts by combining genetic, metabolic, clinical and lifestyle variables was developed and validated. This model satisfactorily identified RA patients with high risk of insufficient response to MTX.

SUBMITTER: de Rotte MCFJ

PROVIDER: S-EPMC6287811 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a prognostic multivariable model to predict insufficient clinical response to methotrexate in rheumatoid arthritis.

de Rotte Maurits C F J MCFJ Pluijm Saskia M F SMF de Jong Pascal H P PHP Bulatović Ćalasan Maja M Wulffraat Nico M NM Weel Angelique E A M AEAM Lindemans Jan J Hazes J M W JMW de Jonge Robert R

PloS one 20181210 12

<h4>Objective</h4>The objective was to predict insufficient response to 3 months methotrexate (MTX) in DMARD naïve rheumatoid arthritis patients.<h4>Methods</h4>A Multivariable logistic regression model of rheumatoid arthritis patients starting MTX was developed in a derivation cohort with 285 patients starting MTX in a clinical multicentre, stratified single-blinded trial, performed in seven secondary care clinics and a tertiary care clinic. The model was validated in a validation cohort with 1 ...[more]

PMID: 30532219

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Project description:IntroductionMethotrexate (MTX) constitutes the 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 3 months of treatment in the treatment in the Rotterdam Early Arthritis Cohort (tREACH), including baseline predictors: Disease activity score 28 (DAS28), Health Assessment Questionnaire (HAQ), erythrocyte folate, single-nucleotide polymorphisms (SNPs; ABCB1, ABCC3), smoking, and BMI. The purpose of the current study was (1) to externally validate the model and (2) to enhance the model's clinical applicability.MethodsErythrocyte folate and SNPs were assessed in 91 early disease-modifying antirheumatic drug (DMARD)-naïve RA patients starting MTX in the external validation cohort (U-Act-Early). Insufficient response (DAS28 > 3.2) was determined after 3 months and non-response after 6 months of therapy. The previously developed prediction model was considered successfully validated in the U-Act-Early (validation cohort) if the area under the curve (AUC) of the receiver operating characteristic (ROC) was not significantly lower than in the tREACH (derivation cohort).ResultsThe AUCs in U-Act-Early at three and 6 months were 0.75 (95% CI 0.64-0.85) and 0.71 (95% CI 0.60-0.82) respectively, similar to the tREACH. Baseline DAS28 > 5.1 and HAQ > 0.6 were the strongest predictors. The model was simplified by excluding the SNPs, while still classifying 73% correctly. Furthermore, interaction terms between BMI and HAQ and BMI and erythrocyte folate significantly improved the model increasing correct classification to 75%. Results were successfully implemented in Evidencio online platform assisting clinicians in shared decision-making to intensify treatment when appropriate.ConclusionsWe successfully externally validated our recently reported prediction model for MTX non-response and enhanced its clinical application thus enabling its evaluation in a clinical trial.Trial registrationThe U-Act-Early is registered at ClinicalTrials.gov. number: NCT01034137. tREACH is registered retrospectively at ISRCTN registry, number: ISRCTN26791028 at 23 August 2007.

Project description: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.

Project description:ObjectiveDespite the development of new biologic therapies, methotrexate (MTX) remains the preferred initial disease-modifying anti-rheumatic drug to treat rheumatoid arthritis (RA). Adherence to disease-modifying anti-rheumatic drugs is suspected to be highly variable potentially leading to reduced treatment effectiveness. This work aimed to develop and validate the Methotrexate Experience Questionnaire (MEQ), a tool to identify and characterize non-adherence to oral MTX.MethodsMEQ development included a literature review and qualitative interviews with RA patients and physicians in the United States. A retrospective, cross-sectional study using data from Optimum Patient Care Research Database, a large primary care database of electronic medical records in the United Kingdom, was conducted to finalize the MEQ and evaluate its psychometric properties.ResultsThree hundred seven e-consented subjects (66% women, mean age of 65 years) completed the MEQ remotely, and were included in this analysis. Item-convergent and divergent validity were generally supportive of the construct validity of the MEQ and Cronbach's alpha of 0.87 supported its reliability. The MEQ Total score presented statistically significant correlations of small to medium size with all selected concurrent scales, as expected; the highest correlation was obtained between the general acceptance score of ACCEPT and the MEQ Total score (0.55, p < 0.001). Known-groups validity was demonstrated as a logical pattern of higher MEQ scores was obtained for patients considered adherent with both the 6- and 12-month Proportion of Days Covered (mean MEQ total score 82.7 for 12-month PDC ≥ 80% against 76.3 for 12-month PDC < 80%, p< 0.0001). Additionally, a pattern of lower MEQ scores was obtained for patients with more severe disease assessed with Routine Assessment of Patient Index Data 3.ConclusionThe 24-item MEQ is a reliable and valid instrument to assess the adherence of RA patients taking MTX, potentially improving over historical refill rate metrics by providing insights into the individual reasons for lack of adherence. This information should facilitate clinician-patient discussions and help inform treatment decisions.

Project description:ObjectivesTo identify biomarkers at the gene expression level to predict response to methotrexate (MTX) in patients with rheumatoid arthritis (RA).MethodsMTX-naïve patients with RA were started on MTX and followed up over three months. The disease activity score 28 (DAS28) was used to classify patients into responders and non-responders. Genome-wide gene expression analysis was performed in CD4 + and CD14 + mononuclear cells sampled from whole blood at baseline to identify differentially expressed genes in responders versus non-responders. Gene selection methods and prediction modelling obtained the most relevant differentially expressed genes. A logistic regression prediction model was subsequently constructed and validated via bootstrapping. The area under the receiver operating characteristic (AUC) curve was calculated to judge model quality.ResultsSeventy-nine patients with RA (53.4 ± 13.9 years, 74.7% females) were enrolled, and 70 finished the study with a documented treatment EULAR response (77.1% responders). Forty-six differentially expressed genes were found. The most promising genes were KRTAP4-11, LOC101927584, and PECAM1 in CD4 + cells and PSMD5 and ID1 in CD14 + cells. The final prediction model using these genes reached an AUC of 90%; the validation set's AUC was 82%.ConclusionsOur prediction model constructed via genome-wide gene expression analysis in CD4 + and CD14 + mononuclear cells yielded excellent predictions. Our findings necessitate confirmation in other cohorts of MTX-naïve RA patients. Especially if used in conjunction with previously identified clinical and laboratory (bio)markers, our results could help predict response to MTX in RA to guide treatment decisions. Key Points • Patients with rheumatoid arthritis may or may not respond to treatment with methotrexate, which is the recommended first-line drug in guidelines around the world. • In non-responders, valuable time is lost until second-line treatments are started. • This study aimed at predicting response to methotrexate by identifying differentially expressed genes from peripheral blood samples. • The final prediction model yielded excellent prognostic values, but validation in other cohorts is necessary to corroborate these findings.

Project description:BackgroundMethotrexate (MTX) is the first line treatment for rheumatoid arthritis (RA), but failure of satisfying treatment response occurs in a significant proportion of patients. Here we present a longitudinal multi-omics study aimed at detecting molecular and cellular processes in peripheral blood associated with a successful methotrexate treatment of rheumatoid arthritis.MethodsEighty newly diagnosed patients with RA underwent clinical assessment and donated blood before initiation of MTX, and 3 months into treatment. Flow cytometry was used to describe cell types and presence of activation markers in peripheral blood, the expression of 51 proteins was measured in serum or plasma, and RNA sequencing was performed in peripheral blood mononuclear cells (PBMC). Response to treatment after 3 months was determined using the EULAR response criteria. We assessed the changes in biological phenotypes during treatment, and whether these changes differed between responders and non-responders with regression analysis. By using measurements from baseline, we also tried to find biomarkers of future MTX response or, alternatively, to predict MTX response.ResultsAmong the MTX responders, (Good or Moderate according to EULAR treatment response classification, n = 60, 75%), we observed changes in 29 partly overlapping cell types proportions, levels of 13 proteins and expression of 38 genes during treatment. These changes were in most cases suppressions that were stronger among responders compared to non-responders. Within responders to treatment, we observed a suppression of FOXP3 gene expression, reduction of immunoglobulin gene expression and suppression of genes involved in cell proliferation. The proportion of many HLA-DR expressing T-cell populations were suppressed in all patients irrespective of clinical response, and the proportion of many IL21R+ T-cells were reduced exclusively in non-responders. Using only the baseline measurements we could not detect any biomarkers or prediction models that could predict response to MTX.ConclusionWe conclude that a deep molecular and cellular phenotyping of peripheral blood cells in RA patients treated with methotrexate can reveal previously not recognized differences between responders and non-responders during 3 months of treatment with MTX. This may contribute to the understanding of MTX mode of action and explain non-responsiveness to MTX therapy.

Project description:Methotrexate (MTX) is the standard first-line therapy in rheumatoid arthritis (RA) with variable clinical efficacy that is difficult to predict. The glycosylation status of immunoglobulin G (IgG) is altered in RA and influenced by MTX treatment. We aimed to further investigate if IgG glycosylation in untreated early RA can predict therapeutic response to MTX.We used a shotgun proteomic approach to screen for the Fc glycopeptides in the serum of 12 control subjects and 59 untreated patients with early RA prior to and following MTX initiation. MTX treatment response was defined according to the European League Against Rheumatism at a median of 14 weeks (range 13-15) after treatment initiation. Seropositive patients were defined as those testing positive for anticitrullinated protein antibodies and/or rheumatoid factor at baseline (n = 44). Data analysis was performed using uni- and multivariate statistics.We could confirm a low abundance of galactosylated glycans in untreated patients with early RA compared with control subjects that was partially restored by MTX treatment. This was more evident among future nonresponders than among responders to MTX treatment. Results were further validated and confirmed by multivariate statistical analysis of the baseline Fc glycan, proteomic, and clinical data. We found that the ratio between the main agalactosylated (FA2) and main mono- and di-galactosylated Fc glycans (FA2G1 and FA2G2) of IgG1 ranked as the most prominent factor distinguishing responders from nonresponders. A low baseline ratio of FA2/[FA2G1 + FA2G2]-IgG1 was associated with nonresponse (OR 5.3 [1.6-17.0]) and was able to discriminate future nonresponders from responders to MTX therapy with a sensitivity of 70% (95% CI 46-88%) and a specificity of 69% (95% CI 52-83%). For seropositive patients (n = 44), this trend was improved with a sensitivity of 73% (95% CI 45-92%) for nonresponse and a specificity of 79% (95% CI 60-92%).We show that the FA2/[FA2G1 + FA2G2] of IgG1 is a biomarker candidate that is significantly associated with nonresponding patients and has potential value for prediction of MTX clinical response.

Dataset Information

Development and validation of a prognostic multivariable model to predict insufficient clinical response to methotrexate in rheumatoid arthritis.

Objective

Methods

Results

Conclusions

Publications

Development and validation of a prognostic multivariable model to predict insufficient clinical response to methotrexate in rheumatoid arthritis.

Similar Datasets

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