Dataset Information

Validation of a Prognostic Multivariable Prediction Model for Insufficient Clinical Response to Methotrexate in Early Rheumatoid Arthritis and Its Clinical Application in Evidencio.

ABSTRACT:

Introduction

Methotrexate (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.

Methods

Erythrocyte 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).

Results

The 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.

Conclusions

We 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 registration

The U-Act-Early is registered at ClinicalTrials.gov. number: NCT01034137. tREACH is registered retrospectively at ISRCTN registry, number: ISRCTN26791028 at 23 August 2007.

SUBMITTER: Gosselt HR

PROVIDER: S-EPMC7695780 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.