clinical trial data Archives - Page 3 of 6

Vivli CEO Rebecca Li to speak at the NIDDK Central Repository 20th Anniversary Workshop

September 18, 2023

Vivli’s CEO, Rebecca Li, will speak at the NIDDK Central Repository 20th Anniversary Workshop: Promoting Secondary Research to Accelerate Medical Breakthroughs and Innovation. This online event, hosted by the National Institute of Diabetes and Digestive and Kidney Diseases, will take place on September 19-20, 2023.

Li will participate as a panelist in a discussion session on ‘Supporting NIDDK-Funded Research and Expanding NIDDK’s Data Ecosystem’. This session will focus on real-world examples in which NIDDK-funded research is strengthening the data ecosystem and accelerating medical breakthroughs through secondary research, and is scheduled to run on Wednesday, September 20. NIDDK has been making data available to researchers in the Vivli repository since 2022.

The 20th Anniversary Workshop will review the NIDDK Central Repository program from its early beginnings to its current state, and engage a diverse range of experts from the research community to share experiences and explore future possibilities. Registration is open until Tuesday, September 19 – find out more and register on the event website.

Vivli Researcher Spotlight: Dr. Neeraj Nerula on the use of Vivli’s platform to advance inflammatory bowel disease research

September 12, 2023

Dr. Neeraj Narula is an Associate Professor of Medicine and Staff Gastroenterologist at McMaster University in Hamilton, Ontario, Canada. His primary research focus is on inflammatory bowel diseases, including Crohn’s disease and ulcerative colitis. Dr. Narula is particularly interested in the endoscopic assessments of these diseases and understanding how to better quantify inflammation. He has devoted his efforts to devising ways to measure inflammation that provide meaningful prognostic value rather than arbitrary numbers.

One of Dr. Narula’s major contributions to research in this area has been the development of a scoring tool called the Modified Multiplier of the Simple Endoscopic Score for Crohn’s Disease (MM-SES-CD). This tool aims to better quantify inflammation in Crohn’s disease and provides higher weight to factors that are harder to heal. In contrast to previous methods, the MM-SES-CD shows great prognostic value and has shown potential in various clinical settings. Dr. Narula’s other interests involve data sharing platforms like Vivli. He values these platforms as a unique resource that younger investigators can access, helping them to answer key questions without the need for large-scale clinical trials. He encourages young researchers to leverage these resources to build their research profiles.

Dr. Narula believes that these platforms need some improvement in search functionality, making it easier for users to find specific trials. He also notes that learning how to interpret the data can be challenging initially; however, once this obstacle has been overcome, these platforms can be an invaluable resource. Dr. Narula’s ongoing research interests involve defining remission in Crohn’s disease and creating a similar tool for ulcerative colitis to the one he developed for Crohn’s disease. He is also working on validating the MM-SES-CD in unrelated datasets. He envisions these tools being incorporated into clinical guidelines in the future, ultimately improving patient care.

What led you to want to research this topic?

Inflammatory bowel disease (IBD) is a complex and multifaceted condition. While we have made significant strides in understanding IBD, there is still a lot to learn. The primary goal of this research was to delve deeper into patient response to various IBD treatments, to better personalize treatment plans and improve patient outcomes. The availability of an extensive dataset on Vivli allowed us to study patient responses in a more comprehensive manner than we might have been able to do otherwise.

What was your experience like in the process of requesting data using the Vivli platform?

The process of requesting data through Vivli was straightforward and intuitive. The platform’s design made it easy to navigate, request the necessary data, and receive prompt responses. Vivli’s wealth of data provided an invaluable resource for our research

What is the current state of management in IBD? How was the data you accessed through Vivli able to help?

Current management of IBD largely involves a somewhat trial and error approach to medication. We aim to identify specific patient characteristics that predict response to particular treatments, enabling a more personalized, efficient approach to management. Access to data through Vivli allowed us to analyze a larger patient population and a wider variety of treatments than we would have been able to in a standard clinical setting.

How could your findings be used in future clinical trials on IBD? How can your findings be helpful in treatments for IBD patients?

We believe our research can help inform future clinical trials, guiding more efficient study design by highlighting potentially significant predictors of treatment response. Additionally, our findings can help clinicians make more informed decisions about treatment plans, ultimately benefiting IBD patients.

Would you use the Vivli platform again? What improvements would you recommend?

Absolutely, the Vivli platform was instrumental in our research. In terms of improvements, perhaps a more detailed search functionality for the data could help researchers more quickly identify relevant studies. However, overall, the platform is user-friendly and efficient.

Please share any final comments about Vivli. What would you tell other researchers about doing this kind of analysis?

Vivli is a powerful tool for researchers in any medical field. The ability to access and analyze data from a vast array of clinical trials is truly invaluable. I encourage all researchers to consider using Vivli or similar platforms to enhance their studies, ensuring they have a clear research plan and question in mind to effectively leverage the available data.

Vivli to host workshop at 2023 Cochrane Colloquium

August 31, 2023

Vivli will host a workshop at the 2023 Cochrane Colloquium in London on September 4. The Cochrane Colloquium is an annual event for Cochrane in support of its mission to promote evidence-informed health decision-making.

Vivli’s workshop will focus on “Practical advice for accessing patient-level data from a data sharing platform for evidence synthesis.” This session will provide:

an introduction to the Vivli platform, including an overview of data available and hands-on group work on how to submit a request for data
an overview of first-hand experience of using patient level data using Vivli’s data sharing platform as a case study
a discussion session focusing on some of the opportunities and challenges associated with using patient level data (for example: what to do when data needed to answer the hypothesis is not available via a single data sharing platform?)

This session will be a mixture of hands-on practice and opportunities to hear from researchers who have used the platform, patient advocates, and data providers. There will also be time for participants to ask questions about the data access process.

Vivli Senior Director Julie Wood will be joined by Alan Chant, the patient representative on the Independent Review Panel; Catrin Tudur Smith from the University of Liverpool; and Rebecca Sudlow from Roche to facilitate this workshop.

For more information on the 2023 Cochrane Colloquium and to register for this session, please see the event schedule. Please note that participation in this session is open only to registered Colloquium attendees.

Vivli Senior Advisor speaks at CDISC 2023 Japan Interchange Program

July 10, 2023

Vivli Data Request Process

Vivli Senior Advisor Azusa Tsukida spoke at Clinical Data Interchange Standards Consortium (CDISC) 2023 Japan Interchange Program on July 10.

Tsukida presented during the session on ‘Real World Data & Regulatory Presentations/Perspectives’. Her talk focused on the benefits of data sharing, using case studies from data contributors who are sharing high-quality data via the Vivli platform to enable access to researchers worldwide and contribute to scientific discovery.

CDISC works to develop and advance data standards to support transforming incompatible formats, inconsistent methodologies, and diverse perspectives into a coherent framework for generating clinical research data that is accessible, interoperable, and reusable. More than 80% of the data available in Vivli is formatted in the CDISC-SDTM standard.

Find out more about how you can request data from Vivli’s repository and help accelerate the progress of health research.

Vivli Researcher Spotlight: Dr. Fasihul Khan on the potential for biomarkers to predict outcomes for people with pulmonary fibrosis

May 23, 2023

Fasihul Khan, M.D., Ph.D., is a consultant at Glenfield Hospital, University Hospitals of Leicester NHS UK. Dr. Khan’s team submitted a research proposal to access Vivli to conduct analysis relevant to their topic, “A systematic review and individual patient data meta-analysis of physiological biomarkers in idiopathic pulmonary fibrosis”. The team’s completed research has been presented to the research community at conferences and in publications including American Journal of Respiratory and Critical Care Medicine. He sat down with Vivli to tell us more about accessing individual participant data to advance his research, and the potential for biomarkers to predict outcomes for people with pulmonary fibrosis.

Please tell us more about your research – what led you to want to research this particular topic?

So my area of interest is pulmonary fibrosis, which is a condition causing scarring of the lungs. Pulmonary fibrosis is a relatively rare condition, and therefore the number of studies in this area are limited, although expanding rapidly. I was keen to synthesize some of the existing information that was already available. I wanted to perform a systematic review, specifically looking to see whether there are blood biomarkers that can predict outcomes in patients with diagnosed pulmonary fibrosis. When I started searching the literature, it very quickly became apparent that there were several published studies, but actually the data and the way the studies were reported were very heterogeneous. Individually the studies yielded inconsistent results, utilized data-dependent thresholds, and frequently did not adjust for confounders. Therefore, I sought individual participant data which helped overcome these limitations and enabled robust data analyses to be performed leading to reliable conclusions.

Could you talk about what it was like to work across multiple data-sharing platforms; how did you handle that?

This was not straightforward! I created summary estimates from each study separately on the different platforms in Vivli and in CSDR, then imported them manually onto my own database. I then used additional software to pool the summary estimates. Having the data all in one place would have saved me a lot of time and stress!

Not a lot of researchers have the perseverance to do what you did. What advice would you give to researchers before they start off? Things you wish you’d known before you started?

I think it’s important to consider the project as a whole. It is highly likely the process will take much longer than you think, and that’s not necessarily any individual or organization’s fault. You need to have a clear understanding with contingency plans for each stage, and give yourself plenty of time! Be clear about your research question, and whether individual participant data are likely to improve your research, before committing to the additional effort. Speak to others who have been through the process of acquiring individual participant data, and your institution to understand timescales for data sharing agreements as these are likely to be time consuming and potential limiting factors.

Once you were able to access the individual patient data, were you able to get past the reporting limitations and find what you needed?

Absolutely; once we had the raw data, we were able to perform our analysis and produce some very meaningful results, which we have subsequently published in two journals. The first was a blood biomarker paper in the European Respiratory Journal which was the first blood biomarker study in pulmonary fibrosis to utilize this approach, and provides robust estimates of the association between matrix-metalloproteinase 7 and disease progression.

The second paper was published in the American Journal of Respiratory Critical Care Medicine. In this paper, we looked at change in FVC which is a lung function measurement used to assess progression in pulmonary fibrosis. All interventional clinical trials measure FVC as an endpoint – typically at 12 months, but patients have additional FVC measurements at baseline, 3, and 6 months. The purpose of our research was to evaluate whether short term changes in FVC i.e. over three-months, are associated with overall mortality. In other words, can we shorten clinical trials by finding an earlier signal than the 12 months FVC change that is currently accepted by regulators. Since the association between short term FVC change and mortality was not reported in any clinical study, we needed the individual participant data to model this association. Indeed, we were able to find that three-month FVC change is associated with mortality, and perhaps more importantly a treatment effect could be observed between treatment and placebo arms at three-months. The findings of this study have been well received by the research community, and have already been adopted into the design of an adaptive trial in IPF. Lots of hard work, but worth it as the results are likely to generate further research which ultimately will hopefully impact patients in a positive manner!

Vivli Board names Rebecca Li CEO

May 3, 2023

Vivli announced that its Board of Directors has promoted Rebecca Li to the position of CEO. Li previously held the position of Executive Director and has been with the Vivli since its founding when it launched as a project from the MRCT Center of the Brigham and Women’s Hospital and Harvard.

“Rebecca has overseen the extraordinary growth of Vivli from the start and we are excited that she will continue to lead Vivli through the next phase of growth as we aim to go even further in our mission to make clinical trial available for research. She has demonstrated herself to be an exceptional team leader and strategic thinker with a clear vision for how we will develop globally in the coming years,” said Dr. Michael Stebbins, Vivli’s Board Chair.

“I am privileged to lead Vivli and envision further expanding our successful platform technology into Europe and Asia as we enter our next phase of innovation and growth” said Dr. Rebecca Li.

Vivli was founded in 2018 as a non-profit organization that is currently the largest individual participant-level (IPD) data sharing platform focused on sharing clinical trial data serving the international research community.

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

April 26, 2023

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.

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