data sharing Archives

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

July 30, 2024

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Using the Vivli platform enabled the research team to include previously unpublished data, the first opportunity to perform meta-analysis on this complete dataset. Previously unpublished data are included in the Supplementary Material, available online alongside the article recently published in the journal Rheumatology.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

February 28, 2024

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: Reusing Data from a Completed Clinical Trial to Inform Guidelines

February 21, 2024

In a recent study led by Dr. Sarah Nevitt, a senior research associate at the University of Liverpool, a team of researchers examined the effectiveness of various antiepileptic drugs (AEDs) used as monotherapy for people experiencing seizures due to epilepsy. Epilepsy, a common neurological disorder, results from abnormal electrical discharges in the brain, causing recurrent seizures. Typically, around 60% to 70% of individuals with epilepsy achieve longer-term remission, often shortly after beginning treatment with antiepileptic drugs.

Dr. Nevitt and her team sought to compare the performance of 12 different AEDs in terms of treatment failure, remission, and occurrence of first seizures among both children and adults with focal onset or generalized tonic‐clonic seizures. They analyzed data from an extensive collection of clinical trials encompassing more than 22,000 participants, incorporating individual patient data from various studies using a network meta-analysis.

The analysis revealed some key insights. Older drugs like phenobarbitone and phenytoin demonstrated better seizure control for both focal and generalized seizures but had poorer long-term retention rates compared to newer medications such as lamotrigine and levetiracetam. Sodium valproate emerged as the top choice for achieving control and remission of generalized tonic‐clonic seizures, although it might not be suitable for everyone, especially people of childbearing age, due to associated risks.

These findings, published in the Cochrane Library, have already influenced the update of UK guidelines in 2022, providing immediate impact on the treatment of individuals newly diagnosed with epilepsy in the UK. In a recent conversation with Vivli, Dr. Nevitt highlighted that these results are guiding the approach to epilepsy treatment, emphasizing the importance of considering both efficacy and potential side effects when selecting appropriate medications for individuals experiencing seizures.

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: Assessing Clinical Trial Data on Cardiac Risk in Type 2 Diabetes Treatment

February 2, 2024

Dr. João Sérgio Neves is an endocrinologist, based in the Faculty of Medicine of the University of Porto and São João Hospital in Porto, Portugal. Dr. Neves’s team submitted a research proposal to access Vivli to conduct analysis relevant to their topic, “Albiglutide and Cardiovascular Outcomes in Type 2 Diabetes With and Without Concomitant Sodium-Glucose Cotransporter-2 Inhibition Use”. The team’s completed research has been presented in publications including the Journal of the American College of Cardiology. Dr. Neves sat down with Vivli to tell us more about accessing individual participant data to advance his research, and the potential for combination therapy to reduce the risk of cardiovascular events in patients with Type 2 Diabetes.

Could you tell us a little bit about your research? What got you interested in the particular area of research that you carried out working with Vivli?

So I am a clinical endocrinologist; I do clinical research in the field of Endocrinology. I have a particular interest in the effects of endocrine diseases on cardiovascular risk and on cardiac function. My main areas of research have been obesity, diabetes, and pre-diabetes. Previous studies conducted by our team have explored the effects of GLP-1 receptor agonists in patients with diabetes, both with and without heart failure. One of the questions that was still unanswered from the literature was whether the benefits of GLP-1 receptor agonists were still observed in those that were already treated with SGLT2 inhibitors. These two classes of drugs are known to be protective from a cardiovascular perspective for patients with Type 2 Diabetes. However, the two classes were developed and the clinical trials were conducted in parallel. So when clinical trials of GLP-1 receptor agonists (AMPLITUDE-O trial and Harmony Outcomes study) were conducted a bit later and included participants that were already treated with SGLT2 inhibitors at baseline. The authors of the AMPLITUDE-O trial had already performed a sub-analysis evaluating the effects of the GLP-1 receptor agonists in patients treated with SGLT2 inhibitors. Given the relevance of further exploring the combination of GLP-1 receptor agonists with SGLT2 inhibitors, our group requested the Harmony Outcomes study from Vivli. The we also performed a meta-analysis combining the results of the Harmony Outcomes study with the results from the AMPLITUDE-O trial. So there are two trials that we included that evaluate the effects of GLP-1 receptor agonists and included some patients using SGLT2 inhibitors and we wanted to know if this data could help us understand if both drugs when combined can give further cardiovascular protection to patients with Type 2 Diabetes.

And having come to the conclusions that you did – that there may be further reduction in cardiovascular risk but that more clinical trials with combination therapy are required – have the findings from this made any impact in terms of research practice that you’re aware of, since the findings have become available?

So since the findings became available, there has been some interest from other doctors contacting us on how to interpret our findings. We are very cautious and we believe that further data and dedicated clinical trials are necessary to thoroughly evaluate this drug combination. However, acknowledging that these trials might take several years to be conducted, we also recognize that our existing data could assist physicians in making informed decisions about utilizing this combination in the interim. We believe that the results of the Harmony Outcomes trial, in combination with the AMPLITUDE-O trial, favor the possibility that the combination of both drugs is protective from a cardiovascular perspective.

Interestingly, in the same month our paper was published in the Journal of the American College of Cardiology, the European Society of Cardiology published an updated guideline on the treatment of patients with Type 2 Diabetes and cardiovascular disease, and they recommended that patients with Type 2 Diabetes and cardiovascular disease should be treated with both drugs. They did not yet cite our paper because it was published just before publication of the guideline, but they do cite, for example, the AMPLITUDE-O trial. So I believe that our data will reinforce this recommendation; and we see that the field of treatment and prevention of cardiovascular disease in Type 2 Diabetes was already moving in the direction of our findings. But as there was only one study evaluating this combination, we think that our results will be very important for supporting the use of GLP-1 receptor agonists in combination with SGLT2 inhibitors.

Can you talk a little bit about using the data that was available through Vivli; what were you able to do using that data that you were not able to do otherwise?

The type of analysis we aimed to conduct could theoretically be performed using observational data. However, utilizing observational data poses significant challenges due to numerous confounders, particularly when assessing the effects of therapeutic interventions involving drugs. This limitation is well-documented, and such an approach would lack robustness, potentially raising more questions than providing answers. I think that the most interesting thing about the analysis that we performed was that this was a clinical trial that was already performed; the data was already available.

When we analyzed the data we worked with the authors from the primary paper; we got in contact with the authors of the primary analysis and we planned this analysis together. Our interactions with the original authors were invaluable in interpreting the data, given their familiarity with it. This collaborative effort resulted in an interesting analysis and yielded important results.

Can you talk a little bit about your experience of working with the Vivli platform – the processes and technology and what that was like?

I think that the process was quite easy, the instructions are clear. We know that there is always some type of bureaucracy that is involved, but that’s part of how it works, because we are dealing with data from patients. Of course it is anonymized data but I think that’s not different from what I was used to with other types of shared data. , The process works quite smoothly.

The thing that I feel that was a little bit different from our previous experience with secondary analyses, was the use of a platform for analyzing the data outside of our computers. Nevertheless, we successfully conducted our data analysis, and the data was also accessible within the remote computer, allowing us to execute the entire analysis seamlessly.

And how did you find out about Vivli and the opportunity to reuse shared data in general?

We had previously conducted analyses through the secondary analysis of existing data, utilizing platforms such as BioLINCC , which incorporates data from studies sponsored by NIH. Our awareness of Vivli stemmed from mentions in papers that disclosed their data sharing approaches, indicating that access could be facilitated through Vivli. This was my first personal experience using Vivli, and I must say that I find the work undertaken by the Vivli team truly remarkable. Your efforts contribute significantly to the future of research and the enhanced utilization of already collected data.

How has the direction of your own research been affected by the research that you did on this project? Has it affected what you’re doing or changed your direction in any way?

I believe it has provided clarity on the next steps to enhance knowledge in this field. In our team, we recognized that addressing whether the treatments were additive or not would be a pivotal question. If we discovered that the combination did not yield additional risk reduction, we needed to understand which drug to select for specific patients. With the results we obtained, our focus shifted towards understanding how to improve access to these drugs and assessing their effectiveness in other populations, particularly in the earlier phases of Type 2 Diabetes and even pre-diabetes. As we design new clinical trials, we are already incorporating the insights gained from this analysis.

Would you use the Vivli platform again? Are there any changes or improvements that you would recommend to how it works?

Certainly, the experience was highly positive, and I look forward to working again with Vivli in the future. One overarching improvement (that’s not specific to Vivli) would be to expand access to even more data. I do believe that the data is very valuable and that it is very important to share the data from large clinical trials. The type of study that we analyzed is probably the most relevant that should be shared – of course with a very specific and detailed analysis plan and with all the regulations that are needed in this context. Considering the substantial resources and time invested in these clinical trials, there is often a wealth of data that remains untapped. Many crucial analyses may not have been conducted and researchers not primarily involved in the clinical trial may be able to identify these questions and answer them using the data from that trial. Therefore, it is important to facilitate access to this valuable resource.

So my main recommendation is to try to increase even further the number of studies that are available. Of course this also depends on the companies that own the data and the drugs that are being evaluated. But our analysis could not be performed without the sharing by GSK, so we are also thankful for their contribution to Vivli and for the sharing of the data.

And is there any advice you would give to other researchers who are at the beginning of the process of requesting or using shared data?

My main advice is to have a very specific question that the researchers want to answer; develop a detailed analysis plan; and submit the request to the Vivli platform. While the process may take some time, it is not overly complex. With patience and adherence to the required steps, one can successfully obtain access to the data. I firmly believe that enhancing the utilization of the Vivli platform and increasing access to data from large clinical trials will significantly improve the quality of knowledge across various fields in medicine.

AMR Data Challenge Grand Prize Winners are leveraging the power of AI to combat antimicrobial resistance more effectively

October 20, 2023

The World Health Organization (WHO) has identified Antimicrobial Resistance (AMR) as one of the top 10 global health threats facing humanity. Projections warn that antimicrobial-resistant infections have the potential to become the leading cause of death by 2050.

Recognizing the need for action on this pressing public health issue, Vivli joined forces with Wellcome in 2022 to launch the AMR Register. This innovative resource houses a growing collection of datasets shared by industry partners, offering consolidated access to surveillance data collected on dozens of antimicrobial interventions.

To raise awareness and encourage reutilization of this wealth of data, Wellcome funded the launch of the AMR Data Challenge in April 2023. The event offered a unique opportunity for multidisciplinary teams to access and leverage high-quality AMR surveillance data, and 56 teams from 28 countries submitted project proposals. The participating teams submitted a wide range of innovative proposals, making use of datasets contributed by GSK, Johnson & Johnson, Paratek, Pfizer, Shinogi, and Venatorx.

Submitted proposals were assessed by a judging panel of international experts, who selected six outstanding proposals for recognition as winners of the AMR Surveillance Open Data Reuse Data Challenge. The team that received the Grand Prize was led by Dr. Fredrick Mutisya, Health Data Scientist & Medical Doctor of Narok County, Kenya, and Dr. Rachael Kanguha, Pediatrician, Chuka County Referral Hospital, Kenya.

Their groundbreaking work involved training machine learning models on the Pfizer ATLAS datasets and the development of a novel artificial intelligence web application capable of predicting antibacterial/antifungal susceptibility. Their proposal notes that traditional methods of prediction have proved insufficiently dynamic to cope with the growing amount of genomic data available, or to effectively monitor and predict trends in antimicrobial resistance, leaving gaps in researchers’ understanding and ability to respond. Their goal is to showcase the best predictive model in order to enable proactive measures and early detection of emerging resistance patterns, and provide a model for ethically and effectively integrating AI into an evidence-based epidemiology approach.

Dr. Mutisya expressed his team’s commitment to AMR and highlighted the importance of providing equitable data accessibility to scientists from his region:

“Our team feels incredibly privileged to have participated in such a meaningful data challenge. Winning the grand prize not only fills us with a profound sense of fulfillment but also ignites a stronger motivation within us to continue seeking solutions for global issues, especially in combating antimicrobial resistance,” he said. “We are deeply grateful to Vivli for providing a platform that facilitates data accessibility. This is particularly significant for scientists like us hailing from the Global South, where opportunities like these are often scarce.”

Five other teams, including scientists from Australia, China, France, India, Spain, the United Kingdom, and the United States, were recognized by the judging panel for proposals which demonstrated notable impact and innovation. A complete list of the winning proposals and finalists is available on the Vivli AMR platform.

Members of the judging panel commented favorably on the excellent quality of the proposals submitted, the innovative approaches used, and creative solutions developed. One of the judges, Professor Marc Mendelson, also noted the importance of open access to data, calling it “a fundamental key to driving innovation towards a better understanding of AMR and the mitigation of this global health crisis.”

The Challenge is over for 2023, but the work of fighting AMR goes on. If you are interested in accelerating research and tackling a global public health challenge at the same time, explore Vivli’s AMR surveillance data sharing platform and find out how you can request access to data.

Vivli Researcher Spotlight: Dr. Yizhe Xu on analyzing clinical trial data to inform development of machine learning tools

October 2, 2023

Yizhe Xu is a Postdoctoral Researcher at Stanford Center for Biomedical Informatics Research, Stanford University. Dr. Xu’s team submitted a research proposal to access Vivli to conduct analysis relevant to their topic, “Applying machine learning tools to personalize dabigatran treatment decisions”. The team’s completed research has been presented to the research community at conferences and in publications including the Journal of Biomedical Informatics. She sat down with Vivli to tell us more about accessing individual participant data to advance her research, and the potential for machine learning tools to support more accurate estimation and evaluation of heterogeneous treatment effects.

Tell us more about your research. What is the current state of management in your public disclosure topic?
Our final paper has been published in the Journal of Biomedical Informatics as of July 2023.

What led you to want to research this topic?
First of all, treatment effect heterogeneity is an important question that informs clinical decision making given the fact that treatment effects often vary across patients. Thus, accurate estimation of individual treatment effects helps to tailor treatment to patient characteristics and maximizes their benefits. However, it has been realized by a wide group of researchers that estimating treatment heterogeneity is challenging, so we summarized the best practices and advanced methodologies and showed a case study on how to carefully estimate heterogeneous treatment effects using the RE-LY and RELY-ABLE trials.

What difference do you hope your research might make, either in the field or for patients? How has it moved forward the treatment of patients?
We hope our case study provides clear instructions and serves as a concrete example for clinical researchers, and that by following our suggestions, they will be able to avoid possible false discoveries of treatment heterogeneity and prevent misleading findings. The improvement of research quality will directly benefit everyday clinical care in the sense that patients will truly benefit from personalized treatment selection if there is treatment heterogeneity and can be estimated reasonably well. On the other hand, we can save the clinicians’ time and efforts on considering personalized treatment when the treatment effect is essentially uniform across patients.

How could your findings be used in future clinical trials in your disease area?
The statistical methods we have summarized and the guidance we provide on how to select a method and evaluate the model performance can be applied to clinical trials in any disease area. However, for observational studies, practitioners need to consider adjusting for confounding, for instance, using methods such as propensity score matching or weighting.

How did the data you accessed through Vivli help you in answering your research question?
Very well. The RE-LY trial enables a case study for us to demonstrate the principled approach we proposed for estimating heterogeneous treatment effect in a real study. The RE-LY study has a large data size, and the fact that it is an RCT helps to simplify the task of treatment effect estimation.

What was your experience like in the process of requesting data using the Vivli platform?
It was an okay experience – we had some difficulties in resolving issues related to the DUA, which made us wait for quite a while, but we were able to get the access eventually.

Would you use the Vivli platform again? Would you recommend Vivli to others? What improvements would you recommend?
Yes, especially if I think some of the unique data sources on the Vivli platform will help to answer particular research questions of my interest. I will also recommend Vivli to others for the same reason. I would recommend simplifying the data requesting process to shorten the waiting time, as well as expanding user autonomy – particularly that users do not need to make a request every time they want to export results.

What advice would you give to other researchers about doing this kind of analysis?
We encourage researchers to understand their data first, then select the most suitable statistical approaches based on that knowledge. After that, we suggest interpreting findings based on the results from multiple estimators that are weighted by their performance, which is evaluated using several different metrics. Please also see our paper for our detailed recommendations.