News & Events

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


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

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 celebrates publication of 200th public disclosure

Vivli celebrates 200 public disclosures

Vivli is delighted to announce publication of the 200th public disclosure resulting from the research team’s work with data from the Vivli platform. 

Rebecca Li, the Chief Executive Officer of Vivli, congratulates all the research teams who have utilized data from the Vivli platform to advance health research through the re-use of valuable clinical trial data. She also acknowledges the organizations, individuals, and thousands of trial participants who have generously shared their data, making this milestone possible.

The Vivli repository houses data from nearly 7,000 trials,  representing the contributions of 1.8 million clinical trial participants. On average, Vivli public disclosures are cited approximately 2.2 times per publication and appear in a wide range of highly-ranked academic journals. 

For more information about how to share and re-use data on the Vivli platform, please visit our Resources page.



Vivli Senior Advisor speaks at CDISC 2023 Japan Interchange Program

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

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!  



Breaking News — Vivli announces the AMR Surveillance Open Data Re-use Challenge, funded by Wellcome, EOI due June 30

Vivli has launched the Vivli AMR Surveillance Open Data Re-use Challenge, funded by Wellcome. The data challenge aims to stimulate and support the innovative re-use of antimicrobial resistance (AMR) surveillance data available in the AMR Register.

This Challenge provides an opportunity for multidisciplinary teams to win prizes by using high-quality industry AMR surveillance data to answer pressing research questions. The data will be shared through the AMR Register.

A series of prizes can be won by research teams from any discipline who find new insights in the data and contributes to the fight against antimicrobial resistance.

What prizes can be won?
There are five monetary awards:
• Grand Prize Award – $20,000
• 4 awards – $10,000 (each) in the categories of Innovation and Impact

Winning teams will additionally be provided with funding towards expenses for ECCMID 2024 if an abstract is accepted.

Sign up to the data challenge Slack Channel to be notified when the challenge is open and to keep updated about the latest information and details about this data challenge.

What’s involved?
Teams are invited to register and submit a short summary of the research they intend to undertake with the data (and Expression of Interest or EOI) by May 10. The EOIs will be reviewed and teams will be given access to the data for a 30-day window, during which solutions must be submitted.

These submissions will be reviewed by a panel of judges and finalists selected. Finalists will have the opportunity to pitch their idea to a panel of judges via Zoom and the prize winners will be chosen.

Winners will be invited to submit a project abstract to ECCMID 2024.

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.

Vivli Executive Director Dr. Rebecca Li Speaking at NIA-DUKE-Alzheimer’s Association Workshop

Rebecca Li will be speaking at the NIA-DUKE-Alzheimer’s Association Workshop held on March 15-16th sponsored by the National Institute on Aging.

The focus of the talk by Rebecca Li and Katherine Welsh-Bohmer (Duke University School of Medicine) is on how the Vivli platform can be used to access trials in Alzheimer’s Disease. Dr. Welsh-Bohmer will discuss the TOMMORROW trial – a large prevention study in Alzheimer’s Disease – as a specific use case.

Share NIH-Funded Data

Guidance for researchers on preparing a DMSP and sharing NIH-funded data

The NIH has updated its policies on data management and sharing (DMS). Effective January 25, 2023, the NIH DMS policy applies to most research funding by the NIH, and requires all applicants planning to generate scientific data to prepare a DMS Plan (DMSP) that describes how they will manage and share data. An effective DMSP requires thoughtful planning, preparation, and execution. We’ve compiled information and resources here to support every step of the process.

How to prepare a DMSP

The DMSP is a set of principles and guidelines that outline requirements for sharing data generated by NIH-funded research. It includes six major elements:

  1. A description of the data type
  2. Related tools, software, and/or code
  3. Common data standard that will be applied to the data
  4. Information about data preservation, access, and associated timelines
  5. Factors affecting access, distribution, or reuse of data
  6. Overview of how compliance with plans for management and sharing will be managed

The DMSP should also include information about direct costs required to support the activities outlined in the Plan.

Vivli has a step-by-step guide to understanding each of these elements and items to consider when developing a DMSP. We also have a customizable DMSP exemplary language available for download and adaptation, which includes sample text as well as guidance on preparing and submitting a budget as part of the DMSP.

Fill out the form below to access all the DMSP Guidance provided by Vivli.

    What best describes your current role?

    Do you plan to include Vivli in any future data management plans?

    How to choose the right repository to share your data

    To enable the implementation of the updated DMS policy, NIH has supported the establishment of the Generalist Repository Ecosystem Initiative (GREI). GREI is a collaboration of seven established generalist repositories who are working together to develop consistent standards and processes to facilitate sharing and reuse of data from NIH-funded studies. As part of preparing a DMSP, researchers will have the opportunity to review repository options and choose the one that best aligns with their needs. Vivli is part of the GREI initiative. The Vivli platform is the only GREI repository that focuses on sharing completed clinical research data at the individual participant level. To assist in considering these options, NIH has prepared guidance on selecting a data repository.

    Once your grant is approved – what next?

    How to submit studies to Vivli for data sharing

    If you’ve decided that Vivli is the right repository for your study data, great! We’ve developed a straightforward and efficient submission process, and we’ve got detailed guidance on how to submit your data and a checklist when you’re ready to begin the process to share your data.

    ResourceDescription
    Vivli Study Submission GuideHow to submit studies for sharing via the Vivli platformDownload PDF
    Study Submission ChecklistA checklist of all information needed for the submission of a studyDownload

    Further questions?

    Email Vivli at support@vivli.org and we will be delighted to assist you.

     

    Public Disclosures

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