Assessing prognosis and prediction of treatment response in early rheumatoid arthritis: systematic reviews

A Network-Based Framework to Discover Treatment-Response-Predicting Biomarkers for Complex Diseases

The potential of precision medicine to transform complex autoimmune disease treatment is often challenged by limited data availability and inadequate sample size when compared with the number of molecular features found in high-throughput multi-omics data sets. To address this issue, the novel framework PRoBeNet (Predictive Response Biomarkers using Network medicine) was developed. PRoBeNet operates under the hypothesis that the therapeutic effect of a drug propagates through a protein-protein interaction network to reverse disease states. PRoBeNet prioritizes biomarkers by considering i) therapy-targeted proteins, ii) disease-specific molecular signatures, and iii) an underlying network of interactions among cellular components (the human interactome). PRoBeNet helped discover biomarkers predicting patient responses to both an established autoimmune therapy (infliximab) and an investigational compound (a mitogen-activated protein kinase 3/1 inhibitor). The predictive power of PRoBeNet biomarkers was validated with retrospective gene-expression data from patients with ulcerative colitis and rheumatoid arthritis and prospective data from tissues from patients with ulcerative colitis and Crohn disease. Machine-learning models using PRoBeNet biomarkers significantly outperformed models using either all genes or randomly selected genes, especially when data were limited. These results illustrate the value of PRoBeNet in reducing features and for constructing robust machine-learning models when data are limited. PRoBeNet may be used to develop companion and complementary diagnostic assays, which may help stratify suitable patient subgroups in clinical trials and improve patient outcomes.

Prognostic modeling in early rheumatoid arthritis: reconsidering the predictive role of disease activity scores

OBJECTIVE

In this prospective cohort study, we provide several prognostic models to predict functional status as measured by the modified Health Assessment Questionnaire (mHAQ). The early adoption of the treat-to-target strategy in this cohort offered a unique opportunity to identify predictive factors using longitudinal data across 20 years.

METHODS

A cohort of 397 patients with early RA was used to develop statistical models to predict mHAQ score measured at baseline, 12 months, and 18 months post diagnosis, as well as serially measured mHAQ. Demographic data, clinical measures, autoantibodies, medication use, comorbid conditions, and baseline mHAQ were considered as predictors.

RESULTS

The discriminative performance of models was comparable to previous work, with an area under the receiver operator curve ranging from 0.64 to 0.88. The most consistent predictive variable was baseline mHAQ. Patient-reported outcomes including early morning stiffness, tender joint count (TJC), fatigue, pain, and patient global assessment were positively predictive of a higher mHAQ at baseline and longitudinally, as was the physician global assessment and C-reactive protein. When considering future function, a higher TJC predicted persistent disability while a higher swollen joint count predicted functional improvements with treatment.

CONCLUSION

In our study of mHAQ prediction in RA patients receiving treat-to-target therapy, patient-reported outcomes were most consistently predictive of function. Patients with high disease activity due predominantly to tenderness scores rather than swelling may benefit from less aggressive treatment escalation and an emphasis on non-pharmacological therapies, allowing for a more personalized approach to treatment. Key Points • Long-term use of the treat-to-target strategy in this patient cohort offers a unique opportunity to develop prognostic models for functional outcomes using extensive longitudinal data. • Patient reported outcomes were more consistent predictors of function than traditional prognostic markers. • Tender joint count and swollen joint count had discordant relationships with future function, adding weight to the possibility that disease activity may better guide treatment when the components are considered separately.

Development and validation of a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China

Background

Considering its high prevalence, estimating the risk of arthritis in middle-aged and older Chinese adults is of particular interest. This study was conducted to develop a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China.

Methods

Our study included a total of 9599 participants utilising data from the China Health and Retirement Longitudinal Study (CHARLS). Participants were randomly assigned to training and validation groups at a 7:3 ratio. Univariate and multivariate binary logistic regression analyses were used to identify the potential predictors of arthritis. Based on the results of the multivariate binary logistic regression, a nomogram was constructed, and its predictive performance was evaluated using the receiver operating characteristic (ROC) curve. The accuracy and discrimination ability were assessed using calibration curve analysis, while decision curve analysis (DCA) was performed to evaluate the net clinical benefit rate.

Results

A total of 9599 participants were included in the study, of which 6716 and 2883 were assigned to the training and validation groups, respectively. A nomogram was constructed to include age, hypertension, heart diseases, gender, sleep time, body mass index (BMI), residence address, the parts of joint pain, and trouble with body pains. The results of the ROC curve suggested that the prediction model had a moderate discrimination ability (AUC >0.7). The calibration curve of the prediction model demonstrated a good predictive accuracy. The DCA curves revealed a favourable net benefit for the prediction model.

Conclusions

The predictive model demonstrated good discrimination, calibration, and clinical validity, and can help community physicians and clinicians to preliminarily assess the risk of arthritis in middle-aged and older community-dwelling adults.

Initial Treatment with Biological Therapy in Rheumatoid Arthritis

BACKGROUND

We aimed to analyse the effectiveness, efficiency, and safety of initial treatment with biological therapies in rheumatoid arthritis (RA).

METHODS

Qualitative study. A group of RA experts was selected. A scoping review in Medline was conducted to analyse the evidence of initial RA treatment with biological therapies. Randomised clinical trials were selected. Two reviewers analysed the articles and compiled the data, whose quality was assessed using the Jadad scale. The experts discussed the review's findings and generated a series of general principles: Results: Seventeen studies were included. Most of the included patients were middle-aged women with early RA (1-7 months) and multiple poor prognostic factors. Initial treatment with TNF-alpha inhibitors combined with methotrexate (MTX) and an IL6R inhibitor (either in mono or combination therapy) is effective (activity, function, radiographic damage, quality of life), safe, and superior to MTX monotherapy in the short and medium term. In the long term, patients who received initial treatment with biologicals presented better results than those whose initial therapy was with MTX.

CONCLUSIONS

Initial treatment of RA with biological therapies is effective, efficient, and safe in the short, medium, and long term, particularly for patients with poor prognostic factors.

Real-World Sarilumab Use and Rule Testing to Predict Treatment Response in Patients with Rheumatoid Arthritis: Findings from the RISE Registry

INTRODUCTION

Clinical trial findings may not be generalizable to routine practice. This study evaluated sarilumab effectiveness in patients with rheumatoid arthritis (RA) and tested the real-world applicability of a response prediction rule, derived from trial data using machine learning (based on C-reactive protein [CRP] > 12.3 mg/l and seropositivity [anticyclic citrullinated peptide antibodies, ACPA +]).

METHODS

Sarilumab initiators from the ACR-RISE Registry, with ≥ 1 prescription on/after its FDA approval (2017-2020), were divided into three cohorts based on progressively restrictive criteria: Cohort A (had active disease), Cohort B (met eligibility criteria of a phase 3 trial in RA patients with inadequate response/intolerance to tumor necrosis factor inhibitors [TNFi]), and Cohort C (characteristics matched to the phase 3 trial baseline). Mean changes in Clinical Disease Activity Index (CDAI) and Routine Assessment of Patient Index Data 3 (RAPID3) were evaluated at 6 and 12 months. In a separate cohort, predictive rule was tested based on CRP levels and seropositive status (ACPA and/or rheumatoid factor); patients were categorized into rule-positive (seropositive with CRP > 12.3 mg/l) and rule-negative groups to compare the odds of achieving CDAI low disease activity (LDA)/remission and minimal clinically important difference (MCID) over 24 weeks.

RESULTS

Among sarilumab initiators (N = 2949), treatment effectiveness was noted across cohorts, with greater improvement noted for Cohort C at 6 and 12 months. Among the predictive rule cohort (N = 205), rule-positive (vs. rule-negative) patients were more likely to reach LDA (odds ratio: 1.5 [0.7, 3.2]) and MCID (1.1 [0.5, 2.4]). Sensitivity analyses (CRP > 5 mg/l) showed better response to sarilumab in rule-positive patients.

CONCLUSIONS

In real-world setting, sarilumab demonstrated treatment effectiveness, with greater improvements in the most selective population, mirroring phase 3 TNFi-refractory and rule-positive RA patients. Seropositivity appeared a stronger driver for treatment response than CRP, although optimization of the rule in routine practice requires further data.

Estimating Patient-Specific Relative Benefit of Adding Biologics to Conventional Rheumatoid Arthritis Treatment: An Individual Participant Data Meta-Analysis

Importance

Current evidence remains ambiguous regarding whether biologics should be added to conventional treatment of rheumatoid arthritis for specific patients, which may cause potential overuse or treatment delay.

Objectives

To estimate the benefit of adding biologics to conventional antirheumatic drugs for the treatment of rheumatoid arthritis given baseline characteristics.

Data Sources

Cochrane CENTRAL, Scopus, MEDLINE, and the World Health Organization International Clinical Trials Registry Platform were searched for articles published from database inception to March 2, 2022.

Study Selection

Randomized clinical trials comparing certolizumab plus conventional antirheumatic drugs with placebo plus conventional drugs were selected.

Data Extraction and Synthesis

Individual participant data of the prespecified outcomes and covariates were acquired from the Vivli database. A 2-stage model was fitted to estimate patient-specific relative outcomes of adding certolizumab vs conventional drugs only. Stage 1 was a penalized logistic regression model to estimate the baseline expected probability of the outcome regardless of treatment using baseline characteristics. Stage 2 was a bayesian individual participant data meta-regression model to estimate the relative outcomes for a particular baseline expected probability. Patient-specific results were displayed interactively on an application based on a 2-stage model.

Main Outcomes and Measures

The primary outcome was low disease activity or remission at 3 months, defined by 3 disease activity indexes (ie, Disease Activity Score based on the evaluation of 28 joints, Clinical Disease Activity Index, or Simplified Disease Activity Index).

Results

Individual participant data were obtained from 3790 patients (2996 female [79.1%] and 794 male [20.9%]; mean [SD] age, 52.7 [12.3] years) from 5 large randomized clinical trials for moderate to high activity rheumatoid arthritis with usable data for 22 prespecified baseline covariates. Overall, adding certolizumab was associated with a higher probability of reaching low disease activity. The odds ratio for patients with an average baseline expected probability of the outcome was 6.31 (95% credible interval, 2.22-15.25). However, the benefits differed in patients with different baseline characteristics. For example, the estimated risk difference was smaller than 10% for patients with either low or high baseline expected probability.

Conclusions and Relevance

In this individual participant data meta-analysis, adding certolizumab was associated with more effectiveness for rheumatoid arthritis in general. However, the benefit was uncertain for patients with low or high baseline expected probability, for whom other evaluations were necessary. The interactive application displaying individual estimates may help with treatment selection.

Predicting Disease Activity in Rheumatoid Arthritis With the Fibromyalgia Survey Questionnaire: Does the Severity of Fibromyalgia Symptoms Matter?

OBJECTIVE

To determine if the degree of baseline fibromyalgia (FM) symptoms in patients with rheumatoid arthritis (RA), as indicated by the Fibromyalgia Survey Questionnaire (FSQ) score, predicts RA disease activity after initiation or change of a disease-modifying antirheumatic drug (DMARD).

METHODS

One hundred ninety-two participants with active RA were followed for 12 weeks after initiation or change of DMARD therapy. Participants completed the FSQ at the initial visit. The Disease Activity Score in 28 joints using C-reactive protein (DAS28-CRP) was measured at baseline and follow-up to assess RA disease activity. We evaluated the association between baseline FSQ score and follow-up DAS28-CRP. As a secondary analysis, we examined the relationship between the 2 components of the FSQ, the Widespread Pain Index (WPI) and Symptom Severity Scale (SSS), with follow-up DAS28-CRP. Multiple linear regression analyses were performed, adjusting for clinical and demographic variables.

RESULTS

In multiple linear regression models, FSQ score was independently associated with elevated DAS28-CRP scores 12 weeks after DMARD initiation (B = 0.04, P = 0.01). In secondary analyses, the WPI was significantly associated with increased follow-up DAS28-CRP scores (B = 0.08, P = 0.001), whereas the SSS was not (B = -0.03, P = 0.43).

CONCLUSION

Higher levels of FM symptoms weakly predicted worse disease activity after treatment. The primary factor that informed the FSQ's prediction of disease activity was the spatial extent of pain, as measured by the WPI.

Personalized medicine in rheumatoid arthritis: Combining biomarkers and patient preferences to guide therapeutic decisions

The last few decades have seen major therapeutic advancements in rheumatoid arthritis (RA) therapeutics. New disease-modifying antirheumatic drugs (DMARDs) have continued to emerge, creating more choices for people. However, no therapeutic works for all patients. Each has its own inherent benefits, risks, costs, dosing, and monitoring considerations. In parallel, there has been a focus on personalized medicine initiatives that tailor therapeutic decisions to patients based on their unique characteristics or biomarkers. Personalized effect estimates require an understanding of a patient's baseline probability of response to treatment and data on the comparative effectiveness of the available treatments. However, even if accurate risk prediction models are available, trade-offs often still need to be made between treatments. In this paper, we review the history of RA therapeutics and progress that has been made toward personalized risk predictive models for DMARDs, outlining where knowledge gaps still exist. We further review why patient preferences play a key role in a holistic view of personalized medicine and how this links with shared decision-making. We argue that a "preference misdiagnosis" may be equally important as a medical misdiagnosis but is often overlooked.

Baseline IgG-Fc N-glycosylation profile is associated with long-term outcome in a cohort of early inflammatory arthritis patients

Background

Rheumatoid arthritis (RA) is a chronic autoimmune disease for which prediction of long-term prognosis from disease’s outset is not clinically feasible. The importance of immunoglobulin G (IgG) and its Fc N-glycosylation in inflammation is well-known and studies described its relevance for several autoimmune diseases, including RA. Herein we assessed the association between IgG N-glycoforms and disease prognosis at 2 years in an early inflammatory arthritis cohort.

Methods

Sera from 118 patients with early inflammatory arthritis naïve to treatment sampled at baseline were used to obtain IgG Fc glycopeptides, which were then analyzed in a subclass-specific manner by liquid chromatography coupled to mass spectrometry (LC-MS). Patients were prospectively followed and a favorable prognosis at 2 years was assessed by a combined index as remission or low disease activity (DAS28 < 3.2) and normal functionality (HAQ ≤ 0.25) while on treatment with conventional synthetic DMARDs and never used biologic DMARDs.

Results

We observed a significant association between high levels of IgG2/3 Fc galactosylation (effect 0.627 and adjusted p value 0.036 for the fully galactosylated glycoform H5N4F1; effect −0.551 and adjusted p value 0.04963 for the agalactosylated H3N4F1) and favorable outcome after 2 years of treatment. The inclusion of IgG glycoprofiling in a multivariate analysis to predict the outcome (with HAQ, DAS28, RF, and ACPA included in the model) did not improve the prognostic performance of the model.

Conclusion

Pending confirmation of these findings in larger cohorts, IgG glycosylation levels could be used as a prognostic marker in early arthritis, to overcome the limitations of the current prognostic tools.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-022-02897-5.

Validation in the ESPOIR cohort of vitamin K-dependent protein S (PROS) as a potential biomarker capable of predicting response to the methotrexate/etanercept combination

Background

To validate the ability of PROS (vitamin K-dependent protein S) and CO7 (complement component C7) to predict response to the methotrexate (MTX)/etanercept (ETA) combination in rheumatoid arthritis (RA) patients who received this therapeutic combination in a well-documented cohort.

Method

From the ESPOIR cohort, RA patients having received the MTX/ETA or MTX/adalimumab (ADA) combination as a first-line biologic treatment were included. Serum concentrations of PROS and CO7 were measured by ELISA prior to the initiation of ETA or ADA, at a time where the disease was active (DAS28 ESR > 3.2). The clinical efficacy (response/non-response) of both combinations has been evaluated after at least 6 months of treatment, according to the EULAR response criteria with some modifications.

Results

Thirty-two were treated by MTX/ETA; the numbers of responders and non-responders were 24 and 8, respectively. Thirty-three patients received the MTX/ADA combination; 27 and 5 patients were respectively responders and non-responders. While there were no differences for demographic, clinical, biological, and X-rays data, as well as for CO7, serum levels of PROS tended to be significantly higher in responders to the MTX/ETA combination (p = 0.08) while no difference was observed in the group receiving MTX/ADA. For PROS, the best concentration threshold to differentiate both groups was calculated at 40 μg/ml using ROC curve. The theranostic performances of PROS appeared better for the ETA/MTX combination. When considering the response to this combination, analysis of pooled data from ESPOIR and SATRAPE (initially used to validate PROS and CO7 as potential theranostic biomarkers) cohorts led to a higher theranostic value of PROS that became significant (p = 0.009).

Conclusion

PROS might be one candidate of a combination of biomarkers capable of predicting the response to MTX/ETA combination in RA patients refractory to MTX.

Trial registration

ClinicalTrials.gov identifiers: NCT03666091 and NCT00234234.

Intensive therapy for moderate established rheumatoid arthritis: the TITRATE research programme

Background

Rheumatoid arthritis is a major inflammatory disorder and causes substantial disability. Treatment goals span minimising disease activity, achieving remission and decreasing disability. In active rheumatoid arthritis, intensive management achieves these goals. As many patients with established rheumatoid arthritis have moderate disease activity, the TITRATE (Treatment Intensities and Targets in Rheumatoid Arthritis ThErapy) programme assessed the benefits of intensive management.

Objectives

To (1) define how to deliver intensive therapy in moderate established rheumatoid arthritis; (2) establish its clinical effectiveness and cost-effectiveness in a trial; and (3) evaluate evidence supporting intensive management in observational studies and completed trials.

Design

Observational studies, secondary analyses of completed trials and systematic reviews assessed existing evidence about intensive management. Qualitative research, patient workshops and systematic reviews defined how to deliver it. The trial assessed its clinical effectiveness and cost-effectiveness in moderate established rheumatoid arthritis.

Setting

Observational studies (in three London centres) involved 3167 patients. These were supplemented by secondary analyses of three previously completed trials (in centres across all English regions), involving 668 patients. Qualitative studies assessed expectations (nine patients in four London centres) and experiences of intensive management (15 patients in 10 centres across England). The main clinical trial enrolled 335 patients with diverse socioeconomic deprivation and ethnicity (in 39 centres across all English regions).

Participants

Patients with established moderately active rheumatoid arthritis receiving conventional disease-modifying drugs.

Interventions

Intensive management used combinations of conventional disease-modifying drugs, biologics (particularly tumour necrosis factor inhibitors) and depot steroid injections; nurses saw patients monthly, adjusted treatment and provided supportive person-centred psychoeducation. Control patients received standard care.

Main outcome measures

Disease Activity Score for 28 joints based on the erythrocyte sedimentation rate (DAS28-ESR)-categorised patients (active to remission). Remission (DAS28-ESR < 2.60) was the treatment target. Other outcomes included fatigue (measured on a 100-mm visual analogue scale), disability (as measured on the Health Assessment Questionnaire), harms and resource use for economic assessments.

Results

Evaluation of existing evidence for intensive rheumatoid arthritis management showed the following. First, in observational studies, DAS28-ESR scores decreased over 10–20 years, whereas remissions and treatment intensities increased. Second, in systematic reviews of published trials, all intensive management strategies increased remissions. Finally, patients with high disability scores had fewer remissions. Qualitative studies of rheumatoid arthritis patients, workshops and systematic reviews helped develop an intensive management pathway. A 2-day training session for rheumatology practitioners explained its use, including motivational interviewing techniques and patient handbooks. The trial screened 459 patients and randomised 335 patients (168 patients received intensive management and 167 patients received standard care). A total of 303 patients provided 12-month outcome data. Intention-to-treat analysis showed intensive management increased DAS28-ESR 12-month remissions, compared with standard care (32% vs. 18%, odds ratio 2.17, 95% confidence interval 1.28 to 3.68; p = 0.004), and reduced fatigue [mean difference –18, 95% confidence interval –24 to –11 (scale 0–100); p < 0.001]. Disability (as measured on the Health Assessment Questionnaire) decreased when intensive management patients achieved remission (difference –0.40, 95% confidence interval –0.57 to –0.22) and these differences were considered clinically relevant. However, in all intensive management patients reductions in the Health Assessment Questionnaire scores were less marked (difference –0.1, 95% confidence interval –0.2 to 0.0). The numbers of serious adverse events (intensive management n = 15 vs. standard care n = 11) and other adverse events (intensive management n = 114 vs. standard care n = 151) were similar. Economic analysis showed that the base-case incremental cost-effectiveness ratio was £43,972 from NHS and Personal Social Services cost perspectives. The probability of meeting a willingness-to-pay threshold of £30,000 was 17%. The incremental cost-effectiveness ratio decreased to £29,363 after including patients’ personal costs and lost working time, corresponding to a 50% probability that intensive management is cost-effective at English willingness-to-pay thresholds. Analysing trial baseline predictors showed that remission predictors comprised baseline DAS28-ESR, disability scores and body mass index. A 6-month extension study (involving 95 intensive management patients) showed fewer remissions by 18 months, although more sustained remissions were more likley to persist. Qualitative research in trial completers showed that intensive management was acceptable and treatment support from specialist nurses was beneficial.

Limitations

The main limitations comprised (1) using single time point remissions rather than sustained responses, (2) uncertainty about benefits of different aspects of intensive management and differences in its delivery across centres, (3) doubts about optimal treatment of patients unresponsive to intensive management and (4) the lack of formal international definitions of ‘intensive management’.

Conclusion

The benefits of intensive management need to be set against its additional costs. These were relatively high. Not all patients benefited. Patients with high pretreatment physical disability or who were substantially overweight usually did not achieve remission.

Future work

Further research should (1) identify the most effective components of the intervention, (2) consider its most cost-effective delivery and (3) identify alternative strategies for patients not responding to intensive management.

Trial registration

Current Controlled Trials ISRCTN70160382.

Funding

This project was funded by the National Institute for Health Research (NIHR) Programme Grants for Applied Research programme and will be published in full in Programme Grants for Applied Research; Vol. 9, No. 8. See the NIHR Journals Library website for further project information.

Disease activity trajectories in rheumatoid arthritis: a tool for prediction of outcome

Objective: Predicting treatment response and disease progression in rheumatoid arthritis (RA) remains an elusive endeavour. Identifying subgroups of patients with similar progression is essential for understanding what hinders improvement. However, this cannot be achieved with response criteria based on current versus previous Disease Activity Scores, as they lack the time component. We propose a longitudinal approach that identifies subgroups of patients while capturing their evolution across several clinical outcomes simultaneously (multi-trajectories). Method: For exploration, the RA cohort BARFOT (n = 2829) was used to identify 24 month post-diagnosis simultaneous trajectories of 28-joint Disease Activity Score and its components. Measurements were available at inclusion (0), 3, 6, 12, 24, and 60 months. Multi-trajectories were found with latent class growth modelling. For validation, the TIRA-2 cohort (n = 504) was used. Radiographic changes, assessed by the modified Sharp van der Heijde score, were correlated with trajectory membership. Results: Three multi-trajectories were identified, with 39.6% of the patients in the lowest and 18.9% in the highest (worst) trajectory. Patients in the worst trajectory had on average eight tender and six swollen joints after 24 months. Radiographic changes at 24 and 60 months were significantly increased from the lowest to the highest trajectory. Conclusion: Multi-trajectories constitute a powerful tool for identifying subgroups of RA patients and could be used in future studies searching for predictive biomarkers for disease progression. The evolution and shape of the trajectories in TIRA-2 were very similar to those in BARFOT, even though TIRA-2 is a newer cohort.

Predicting the treatment response of certolizumab for individual adult patients with rheumatoid arthritis: protocol for an individual participant data meta-analysis

BACKGROUND

A model that can predict treatment response for a patient with specific baseline characteristics would help decision-making in personalized medicine. The aim of the study is to develop such a model in the treatment of rheumatoid arthritis (RA) patients who receive certolizumab (CTZ) plus methotrexate (MTX) therapy, using individual participant data meta-analysis (IPD-MA).

METHODS

We will search Cochrane CENTRAL, PubMed, and Scopus as well as clinical trial registries, drug regulatory agency reports, and the pharmaceutical company websites from their inception onwards to obtain randomized controlled trials (RCTs) investigating CTZ plus MTX compared with MTX alone in treating RA. We will request the individual-level data of these trials from an independent platform (http://vivli.org). The primary outcome is efficacy defined as achieving either remission (based on ACR-EULAR Boolean or index-based remission definition) or low disease activity (based on either of the validated composite disease activity measures). The secondary outcomes include ACR50 (50% improvement based on ACR core set variables) and adverse events. We will use a two-stage approach to develop the prediction model. First, we will construct a risk model for the outcomes via logistic regression to estimate the baseline risk scores. We will include baseline demographic, clinical, and biochemical features as covariates for this model. Next, we will develop a meta-regression model for treatment effects, in which the stage 1 risk score will be used both as a prognostic factor and as an effect modifier. We will calculate the probability of having the outcome for a new patient based on the model, which will allow estimation of the absolute and relative treatment effect. We will use R for our analyses, except for the second stage which will be performed in a Bayesian setting using R2Jags.

DISCUSSION

This is a study protocol for developing a model to predict treatment response for RA patients receiving CTZ plus MTX in comparison with MTX alone, using a two-stage approach based on IPD-MA. The study will use a new modeling approach, which aims at retaining the statistical power. The model may help clinicians individualize treatment for particular patients.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number pending (ID#157595).

Assessment of structural damage progression in established rheumatoid arthritis by conventional radiography, computed tomography, and magnetic resonance imaging

Structural damage progression in patients with established rheumatoid arthritis (RA) has traditionally been assessed by conventional radiography (CR), which has proven its value in clinical practice and clinical trials over the past decades. The most prominent abnormalities visualized by CR in RA patients are erosions as a consequence of bone destruction and joint space narrowing (JSN) as a consequence of cartilage damage. Several validated scoring systems to quantify the structural joint damage and progression herein are available. Computed tomography and magnetic resonance imaging are newer, more sensitive methods for detection and monitoring of structural joint damage. A validated scoring system for magnetic resonance imaging of the hands and wrists exists, while no consensus has been reached on a scoring system for computed tomography. Structural damage identified by either CR or magnetic resonance imaging predicts a poorer disease course in patients with both early and established rheumatoid arthritis.