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Awardees Announced for the Vivli AMR Surveillance Open Data Re-Use Data Challenge, funded by Wellcome

Awardees Announced for the Vivli AMR Surveillance Open Data Re-Use Data Challenge, funded by Wellcome

Vivli is pleased to announce the awardees of the Vivli Antimicrobial Resistance (AMR) Surveillance Open Data Re-Use Data Challenge. This initiative comes at a crucial juncture, with the World Health Organization (WHO) identifying Antimicrobial Resistance as one of the top 10 global health threats facing humanity. Alarmingly, antimicrobial-resistant infections have the potential to become the leading cause of death worldwide by 2050. In response to this pressing issue, Vivli and Wellcome joined forces in mid-2022 to launch the AMR Register, a novel platform featuring industry datasets, consolidating surveillance data for the benefit of researchers.

The AMR Data Challenge, funded by Wellcome, was launched in April 2023, as a catalyst for innovation and support for the inventive reutilization of the wealth of surveillance data available within the AMR Register.

“The AMR data challenge not only reflects the extensive interest but also underscores the significance of making AMR data readily accessible to investigators. Data serves as a catalyst for innovative approaches, which are essential in addressing the global AMR challenge,” said Arjun Srinivasan, MD. CAPT, USPS, Deputy Director for Program Improvement Division of Healthcare Quality Promotion, CDC.

A total of 56 teams from 28 different countries participated in the AMR Data Challenge. This event served as a unique platform for multidisciplinary teams to leverage high-quality industry AMR surveillance data, proposing groundbreaking advancements and tools for use in AMR surveillance. The Challenge culminated in the recognition of six outstanding winners for the AMR Surveillance Open Data Re-Use Data Challenge.

The Grand Prize was awarded to 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. 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 fulfilment 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.”

Other notable awardees and their project titles include:

  • Impact Award Winner: Quentin Leclerc, Institut Pasteur, “Stronger together? Potential and limitations of combining industry datasets to fill in global AMR surveillance gaps.”
  • Impact Award Winner: Yanhong Jessika Hu, Murdoch Children’s Research Institute, “Global Geographic Patterns and Trends of WHO Priority Pathogens and AWaRe Antibiotic Resistance Among Children: amrinkids.com.”
  • Innovation Award: Robert Beardmore, University of Exeter, “Are antibiotic breakpoints globally consistent, and does it matter if not?”
  • Innovation Award Winner: Shraddha Karve, Ashoka University, “Novel approach to antibiogram analysis: looking at the composite resistance phenotype.”
  • Innovation Award Runner-up: Jacob Wildfire, LSHTM/SGUL, “Analysis of variations in minimum inhibitory concentration distributions by patient group.”

Data contributed by GSK, Johnson & Johnson, Paratek, Pfizer, Shionogi, and Venatorx was made accessible through the AMR Register, significantly enhancing the impact of the Challenge.

Prof. Marc Mendelson, Chair of the Vivli AMR Scientific Advisory Board, Professor of Infectious Diseases and Head of the Division of Infectious Diseases & HIV Medicine, Groote Schuur Hospital, University of Cape Town noted the exceptional quality of the Challenge applications,

“The quality of applications for the Vivli AMR surveillance Open Re-use Challenge was excellent and it is particularly exciting to see the innovative approaches used,” he said. “Ensuring open access to data across the spectrum of private and public sources is a fundamental key to driving innovation towards a better understanding of AMR and the mitigation of this global health crisis.”

Patricia Bradford, PhD, Antimicrobial Development Specialist and a member of the Judging panel spoke of the innovation of the solutions and their impact, “It was exciting to see the creativity of the various teams with regards to novel uses for the susceptibility data generated by the pharmaceutical industry.  Our hope is that these efforts will better enable patient care and foster antimicrobial stewardship on a local level.”

Alisa Serio, PhD, Executive Director of Microbiology and Nonclinical Development at Paratek Pharmaceuticals Inc. was impressed by the innovative approaches taken by the participating teams and noted, “The outputs of this challenge are exactly what the Vivli AMR initiative was set up for, specifically to openly share surveillance data for researchers to investigate a myriad of questions in AMR to help further understanding, decision-making and policy changes worldwide.”

For more details and to view the winning teams’ solutions, please visit https://amr.vivli.org/data-challenge/finalist-and-award-winning-solutions.

Contact: Catherine D’Arcy, Rebecca Li


About Vivli
Vivli is a non-profit organization working to advance human health through the insights and discoveries gained by sharing and analyzing data. Data sharing initiatives include the AMR Register for AMR surveillance data and the Vivli Platform for clinical trial data. Vivli acts as a neutral broker between data contributor and data user and the wider data sharing community. For more information, visit www.vivli.org and follow us on LinkedIn and Twitter @VivliCenter.

There’s more on Vivli than just clinical trial data

Did you know there’s more on Vivli than just clinical trial data? The majority of our repository of data comes from clinical trial data, but also includes significant numbers of platform trials, observational studies, and real-world evidence resources.

Vivli’s repository of data from nearly 7,000 research studies is available to search freely. Enter any relevant keyword and you’ll get a listing of pertinent studies of different kinds. Here are some recent search results using common keywords for studies that are not clinical trials: 

Observational: 198

Natural history: 26

Platform trial: 420

Explore our How To Guides to learn more about accessing and searching for data with Vivli. If you’d like to take your research further, you can get in touch with us for more information about specific studies or requesting access to data.

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.

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.

T1D exercise data RFP from Helmsley Charitable Trust due the end of this month

The Leona M. and Harry B. Helmsley Charitable Trust’s Type 1 Diabetes Program has launched an initiative to support innovative and practicable solutions to help people with type 1 diabetes (T1D) exercise safely and to improve their quality of life. To help address knowledge gaps about the effect of exercise on T1D, Helmsley supported a research collaboration centered on two large observational exercise studies in people with T1D: one in adults (T1-DEXI) and one in children (T1-DEXIP). More than 500 people took part in the adult study, and another 250 in the pediatric study. Both studies were recruited and conducted fully remotely by design, enabling full monitoring and data collection which was unimpeded by COVID-19 pandemic restrictions.

The data from these studies include information about types of physical activity, heart rate, insulin use, CGM, diet, and genetics. The research collaboration collected and organized the observational data to maximize interoperability, and Helmsley has partnered with Vivli to make the data from T1-DEXI and T1-DEXIP publicly available. Sharing study data publicly will enable a range of researchers to access and explore the data from a variety of perspectives and research specialties.

In addition to facilitating public access through the Vivli platform, Helmsley has also opened a request for proposals (RFP) to support researchers, clinicians, and data scientists interested in analyzing the data and testing novel solutions in people with T1D. The RFP aims to fund projects that will improve the understanding of how exercise impacts T1D and that will provide people with T1D and their healthcare providers practical management solutions and clinical guidelines. The overarching purpose of this initiative is to move real-world data towards real-world solutions.

Concept notes for the RFP are due by April 30, 2023, and full proposals will be invited for submission later in 2023. Researchers interested in exploring the T1D dataset on Vivli in order to prepare concept notes for submission can search data publicly available through Vivli’s search portal, and submit a data request using the standard process. Complete information about the RFP, including key dates and submission criteria, is available on The Helmsley Charitable Trust site.

Deniz Dalton, Program Officer in T1D at Helmsley, said, “People with T1D face many burdensome challenges in their daily lives, including complex decisions around exercise, diet, monitoring glucose levels, and administering insulin. We are excited to make this data publicly available and to launch this RFP, to advance the understanding of how exercise affects glucose management, and ultimately to help create real-world solutions to challenges faced by people with T1D around exercise.”

For more information, please watch Dalton’s presentation on this initiative at the Vivli 2022 Annual Meeting, or contact Vivli User Support directly at support@vivli.org with any questions

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.

 NIH’s New Data Sharing Policy: Maximizing the Value of Research through Data Re-use and Data Access on the Vivli Repository

Learn how the Vivli repository is making it easier for scientists to share and access data, and how you can comply with the NIH’s data management and sharing policy (DMSP) to maximize the value of your research

The National Institutes of Health (NIH) has a policy in place to ensure that data generated by NIH-funded research is accessible to the scientific community starting on January 25, 2023.

Data should be made available as soon as possible or the acceptance for publication of the main findings from the final dataset but the latest date is the end of the award. Data sharing can be done through a variety of mechanisms, including NIH domain repositories or NIH generalist repositories. These can be open-access or controlled-access systems. One such controlled-access repository recognized by the NIH is Vivli, a generalist repository for sharing of clinical data for human research studies.

As a condition of their grant application, investigators are now required to prospectively plan for management of their data and preparing it for re-use, submit a data management and sharing plan (DMSP), and comply with the drafted plan. The NIH Data Sharing Policy (DSMP) encourages investigators to share their data in order to maximize the value of NIH research funds.  But what exactly is a DMSP and how do you draft one for submission to the Vivli Repository? The DMSP is a set of principles and guidelines that outline the requirements for sharing data generated by NIH-funded research. It includes 6 major elements that were selected to ensure that the data is shared as widely and promptly as possible, to maximize the scientific and public health value of the research, while protecting participant privacy and confidentiality. To fill out the DMSP, decisions should be made about the choice of repository, how long the repository will hold/archive the data, whether special tools/software will be provided to access the data, whether consensus data standards apply or exist, whether controlled access will be required and the oversight management details.

To help investigators on their journey to fulfilling the NIH data sharing policy, we have created a list of all the other resources available on our website, including the DMSP template guidance and budget guidance specific to using Vivli to help you navigate the process. 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 DMPS Guidance provided by Vivli.

Vivli has recently released new features timed to the NIH policy launch including: branded portals for research programs / institutions; academic credit; streamlined process for data sharing and reporting for institutions.

In summary, the NIH encourages data sharing as part of its mission to advance biomedical research and to promote collaboration among scientists. Vivli is a non-profit organization that provides a platform recognized by the NIH for funded researchers to share and access anonymized clinical trial data in a secure and compliant way.

    What best describes your current role?

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

     

    Vivli webinar: “Applying the SAFE Data Standard to Securely Share Clinical Trial Data”

    Vivli hosted a webinar to discuss applications of the SAFE Data Standard as defined in “Sharing Anonymized and Functionally Effective (SAFE) Data Standard for Safely Sharing Rich Clinical Trial Data.”

    The webinar was held on November 29th, 4pm CET/10am EST/7am PST.

    VIEW THE RECORDING

    This one-hour webinar features authors of the recently published paper “Sharing Anonymized and Functionally Effective (SAFE) Data Standard for Safely Sharing Rich Clinical Trial Data,” presenting how data transformation is measured as part of the SAFE Data Standard and how to apply the SAFE Data Standard in different security contexts. This webinar is intended to help sponsors understand how to use the SAFE Data Standard to securely share rich clinical trial data to preserve data utility and for users of data to understand how accessing data in different contexts may change the utility of the data being provided.

    Read the paper here!

    Speakers include:
    Luk Arbuckle, Chief Methodologist, Privacy Analytics (an IQVIA company)
    Stephen Bamford, Head of Clinical Data Standards & Transparency, Janssen Research and Development
    Aaron Mann, CEO, Clinical Research Data Sharing Alliance

    Moderated by:
    Marcia Levenstein, Senior Advisor, Vivli

     

    View the Recording of Vivli’s 2022 Annual Meeting

    “Enabling the Data Sharing Ecosystem”

    Vivli’s 2022 Annual Meeting was held on Wednesday, November 2nd, 2022, 9:00am – 12:00pm EST / 2:00  – 5:00pm CET / 10:00pm – 1:00am JST. This meeting served as a chance to allow Vivli data contributors, key collaborators and funders to discuss data sharing and its importance for furthering scientific discoveries.

    View the Recording

    Agenda

    Welcome remarks
    Vivli Update for 2022, Rebecca Li, Executive Director, Vivli

    Sharing COVID-19 Clinical Trial Data on Vivli
    Reflecting on best practices and current experience

    • Julie Holtzople, Senior Director Clinical Transparency and Data Sharing, AstraZeneca
    • Steve Kern, Interim CEO, Global Health Labs
    • Ben Rotz, Associate Vice President – Global Medical Policy Strategy and Operations, Lilly
    • David Leventhal, Enterprise Clinical Trial Data Sharing Lead, Pfizer
    • Rebecca Sudlow, Global Lead Patient Level Data Sharing, Roche

    Q&A

    NIH Data Sharing Policy Changes in 2023

    • Dr. Susan Gregurick, Associate Director for Data Science and Director of the Office of Data Science Strategy (ODSS), NIH

    Q&A

    Data Challenges
    Spotlighting data challenges Vivli is embarking on with key partners in 2022-2023

    • Deniz Dalton, Program Officer, The Leona M. and Harry B. Helmsley Charitable Trust, presenting “Moving Type 1 Diabetes and Exercise Data to Solutions”
    • Tetsuyuki Maruyama, Executive Director, Alzheimer’s Disease Data Initiative (ADDI), presenting “ADDI/Vivli Data Challenge in Alzheimer’s Disease”

    Next Steps and Discussion