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Tag: rheumatoid arthritis

Vivli Researcher Spotlight: Examining serological status to better understand treatment response in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disorder, which primarily affects the joints and is characterized by inflammation and pain. RA most commonly affects the hands and wrists, but can also affect other parts of the body. There is currently no cure for RA, but treatment options have improved considerably in recent years with the development of new therapies and treatment strategies.

Conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) are well established as a standard treatment for RA, used to slow down progression of the disease through broad restriction of the immune systems. Biologic DMARDs (bDMARDs) take a more targeted approach, but it is unclear if it is equally effective across the spectrum of types of RA. In particular, the serological status of people diagnosed with RA may be linked to long-term outcomes. Also,  based on differences in associations with genetic and environmental risk factors, seropositive and seronegative disease are presumed to have different underlying pathophysiological mechanisms. However, whether serological status may also affect treatment responses to biological disease-modifying anti-rheumatic drugs (bDMARDs) is not completely clear. 

In a recently completed study, Dr. Kaoru Takase-Minegishi and colleagues undertook a meta-analysis based on a systematic literature review including data from 28 randomized controlled trials (RCTs). The research team’s goal was to investigate whether the efficacy of bDMARDs differs in seropositive RA patients (seropositive) compared to those classified as seronegative. Some of the study participants were also receiving csDMARDs, while other were treated bDMARDs exclusively.

The research findings indicate that seropositivity was not associated with a better response to bDMARDs, regardless of whether patients were also receiving csDMARDs. Other outcomes mostly showed no significant difference between the groups, and efficacy was generally comparable between seropositive and seronegative patients for a range of treatment protocols. 

Using the Vivli platform enabled the research team to include previously unpublished data, the first opportunity to perform meta-analysis on this complete dataset. Previously unpublished data are included in the Supplementary Material, available online alongside the article recently published in the journal Rheumatology. This research provides a very important additional perspective to the data reported from observational studies thus far, uncovering essential differences. 

Read more about Dr. Takase-Minegishi’s research:

Effect of rheumatoid factor and anticitrullinated peptide antibody on the efficacy of biological disease-modifying antirheumatic drugs in patients with rheumatoid arthritis (Vivli Research Requests 4922, 3274)
The Impact of Autoantibodies (RF and ACPA) on the Efficacy of Biological Disease-modifying Antirheumatic Drugs in Rheumatoid Arthritis: Meta-analysis of Randomized Controlled Trials (Abstract, American College of Rheumatology Convergence 2022)
The impact of autoantibodies (RF and ACPA) on the efficacy of biological disease-modifying antirheumatic drugs in rheumatoid arthritis: meta-analysis of randomized controlled trials (Annals of the Rheumatic Diseases)
The impact of autoantibodies on the efficacy of biological disease-modifying anti-rheumatic drugs in rheumatoid arthritis: meta-analysis of randomized controlled trials (Rheumatology)

Interested in finding out more about how access to Vivli’s data repository can help advance your research? Find out more about how to search and request data.

Vivli Researcher Spotlight: Rethinking Rheumatoid Arthritis Management: The Dual Target Strategy

Rheumatoid arthritis (RA) poses a significant challenge due to its chronic nature and impact on joint health. While treatments have evolved, achieving remission remains elusive for many patients. Using a dataset from the Vivli repository, Dr. Ricardo Ferreira and a team of researchers delved into examining the assessment criteria for remission in RA, shedding light on the limitations of current approaches and proposing a new model—the dual target strategy.

The current standard of treatment is the Treat-to-Target (T2T) approach, which aims for remission or low disease activity. Dr. Ferreira’s team questioned the reliance on a single criterion, the patient global assessment (PGA/PtGA), included in composite measures for determining remission. Their study, drawing from extensive clinical trial data, notably revealed that 19% of patients failed to attain remission based solely on this patient-reported assessment, leading the team to explore a more comprehensive model. In a recent conversation with Vivli, Dr. Ferreira highlighted the research’s key finding: the lack of significant difference in radiographic outcomes between patients classified as “PGA-near-remission” versus those in full remission. This challenges the established understanding of remission incorporating the patient global assessment.

The research team has proposed a dual target strategy, which integrates patient-reported outcomes with objective measures like inflammation status and joint counts to assess inflammatory status and guide immunosuppressive therapy management. The second target (disease impact) would be assessed by informative patient-reported outcome measures, other than PGA. By assigning equal significance to subjective experiences and clinical data, this innovative approach proposes a new benchmark for remission in RA management. Moreover, it has the potential to guide both pharmaceutical and nonpharmacological interventions.

It is anticipated that the dual target strategy has a more patient-centric approach; integrating subjective experiences with objective clinical markers holds promise for improving treatment efficacy and enhancing patient outcomes. With key RA researchers expressing interest in this model, the dual target strategy could significantly change both RA management, and the experience of patients navigating this complex condition. A pragmatic, multicenter, randomized controlled trial is currently being prepared to test this strategy.

Interested in finding out more about how access to Vivli’s data repository can help advance your research? Find out more about how to search and request data.

 

Vivli Researcher Spotlight: Dr. Elena Myasoedova on accessing clinical trial data to advance research and the possibilities for using machine learning as a tool in clinical support for people with rheumatoid arthritis

Elena Myasoedova, M.D., Ph.D., is a clinical rheumatologist with specialty interest in inflammatory arthritis. She is an associate professor of medicine and epidemiology and clinical practice, and leads research in rheumatology and specifically inflammatory arthritis at the Mayo Clinic. Dr. Myasoedova’s team submitted a research proposal to access Vivli to conduct analysis relevant to their topic, “Individualized Prediction of Treatment Response to Methotrexate in Patients with Rheumatoid Arthritis: A Machine Learning Approach”. Their completed research has been presented to the rheumatology research community at conferences and in publications including Annals of the Rheumatic Diseases. She sat down with Vivli to tell us more about accessing clinical trial data to advance her research, and the possibilities for using machine learning as a tool in clinical support for people with rheumatoid arthritis.

Please tell us a little bit more about your research – what led you to want to investigate this particular topic?
In patients with rheumatoid arthritis, methotrexate is a medicine that is used very commonly, and frequently is the first medicine and the most common medicine used in combination later on during the disease course. The challenge that we are facing in rheumatology in general is that we do not have individualized prediction of response to treatments. This means that we use a trial and error approach to treatments; we start patients on medications that are generally effective and safe, and then if a patient does not respond, we upgrade to a different medicine. Because most of the medicines that we use take weeks and months to show their effects, it’s important to understand early on if a person is likely to be a responder. That would help to save time and potentially save some unwanted side effects for the patient, and also help us to be more proactive and helpful.

For this specific study, we looked at clinical markers or clinical predictors of response to methotrexate. We found more than 1400 patients from 13 randomized trials. A total of 775 patients from 4 RCTs were included in the study, and we monitored their response across a six-month timeframe. We further evaluated whether people who did not respond to methotrexate and had some moderate to high disease activity at 12 weeks – who out of this population would respond by follow-up at 24 weeks. We also found a couple of markers for that: specifically if there was a drop in DAS28 sat rate from baseline to 12 weeks of at least 1 point, then it was predictive of then achieving remission or low disease activity at 24 weeks. Otherwise the chances were slim.

We developed this algorithm, and externally validated it using two independent trials with good results. I think that these findings advance the understanding of who are the most likely responders, and how we should discuss with patients their likelihood of response at the very start of their treatment.

Are you hoping that this is going to change the clinical approach? Has it already had an impact in clinical approaches to working with people who have arthritis?
This particular study, and similar studies would probably change the way we discuss this treatment with a patient; changing the treatment approach is a very complex task that probably has to come through the association guidelines.

Is there anything specific that you’d like to say about what working with the dataset in Vivli enabled you to do that you might not have been able to do otherwise?
It was actually a very good experience for us to work with Vivli datasets. It provided longitudinal data on patients who were users of methotrexate but not other medicines, and there were hundreds of these patients available from the trials. So it’s a fairly good dataset to work with, and it had multiple data points longitudinally. Also, at each point, there were multiple clinical points collected, so it was fairly comprehensive.

Do you have any advice that you would give to other researchers who might be interested in accessing this type of data using a platform like Vivli?
The most important advice is to put together a comprehensive proposal with a plan right off the bat, to make sure that the timeline is feasible and that the question that they want to address is feasible with the available data – just to make sure that they are not over-expecting or under-planning. I think it’s most important to ensure that the study question matches the data set.

Have you had feedback about your research and findings?
This research has been presented at several conferences, and the comments have mostly been positive, acknowledging the need for developing such algorithms.

Public Disclosures

Vivli aims to advance human health through clinical research data sharing. One of the key ways we support this is through the Vivli platform, which facilitates data sharing. Vivli platform metrics as of 28 Feb 2025. The metrics will be updated every two months.

These tables provide details of approved research proposals that have published or presented their results.