OHDSI News Updates

EHDEN Knee Replacement Study Results Published In Lancet Rheumatology; OHDSI Tools, Collaborators Helped Lead Important Study

The IMI European Health Data & Evidence Network (EHDEN) project is pleased to announce the publication of the results of its first ‘study-a-thon’ in Lancet Rheumatology the effectiveness and safety associated with unicompartmental versus total knee replacement, a milestone after its first year.1

The choice of which type of knee replacement to recommend remains difficult for surgeons, and there remains insufficient information to inform them and patients of the best approach, dependent on the patient’s personal context.

Researchers associated with the Observational Health Data Sciences and Informatics (OHDSI) network and EHDEN met in Oxford for five days in December 2018 to design, analyze and draft a report of the study results. The resulting study emulated to the extent possible, the design of the five year Total or Partial Knee Arthroplasty Trial (TOPKAT). The study-a-thon assessed whether the efficacy results seen in the trial translated into effectiveness in real-world settings and provided further consideration of safety outcomes that were too uncommon to assess in TOPKAT.

Lancet Paper Shows Most Popular Hypertension Drug Isn’t Most Effective, Per OHDSI’s LEGEND Study

Meta-analytic safety profiles comparing THZ to ACEi, ARB, dCCB, and ndCCB new users across 46 outcomes listed on product labels.

Thiazide diuretics demonstrate better effectiveness and cause fewer side effects than ACE inhibitors as first-line antihypertensive drugs, according to a report published Oct. 24 in The Lancet. The study factors insurance claim data and electronic health records from 4.9 million patients across nine observational databases, making it the most comprehensive one ever on first-line antihypertensives, and it provides additional context to the 2017 guidelines for high blood pressure treatment developed by the American College of Cardiology (ACC) and American Heart Association (AHA).

Collaborators in the Observational Health Data Sciences and Informatics (OHDSI) network produced the paper “Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis” as part of the collaborative’s ongoing Large-Scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) project, which applies high-level analytics to perform observational research on hundreds of millions of patient records within OHDSI’s international database network.

OHDSI researchers believe LEGEND will continue to significantly enhance how real-world evidence is used to study important healthcare questions that impact millions of patients worldwide.

Symposium Research Will Be Virtually Unveiled During #OHDSISocialShowcase

There were more than 80 research highlights presented during the Collaborator Showcase at the 2019 OHDSI U.S. Symposium. For those who couldn’t attend, or who want to check out all the OHDSI innovations over the last year, you’ll have your chance on the OHDSI Twitter and LinkedIn platforms. Each weekday, a different poster will be highlighted with an individual URL. You’ll be able to check out the poster, as well as other materials the author may have included (abstract, software demo, lightning talk, etc.).

Book of OHDSI Introduced At Symposium As Central Respository For Current, Potential Collaborators

Martijn Schuemie, PhD, took the stage at the 2019 OHDSI U.S. Symposium and laid out a collection of internet locations where potential collaborators could learn about the tools and best practices developed within the community. To an audience that included about 200 first-time attendees, it must have been a daunting moment.

That feeling wouldn’t last long, as Schuemie followed by reaching under a white cover and pulling out the first version of the Book of OHDSI, the product of a year-long collaborative effort within the community to provide the best documentation for all aspects of OHDSI. Twenty chapters within five sections (the OHDSI Community, Uniform Data Representation, Data Analytics, Evidence Quality, and OHDSI Studies) were written to empower any new researcher with the ability to generate real-world evidence to improve the healthcare community.

For those who didn’t attend the Symposium, the Book of OHDSI is available here as HTML, as well as EPUB and PDF (click the small download icon at the top). Anybody who wants an actual copy of the book can order it through Amazon at cost price.

Check Out Our All-Encompassing 2019 Symposium Recap Page!

The fifth annual OHDSI Symposium was a tremendous success. From the insightful talks and impressive poster presentations on Monday to the spectacular Women in Real-World Analytics Leadership Forum and the six tutorials, there is no shortage of important material that is now available from this event (including videos of all speeches and tutorials, as well as slides from the presentations). The page also includes video and photo recaps from the weekend, as well as the virtual collaborator showcase, which highlights more than 80 posters and software demos from the event.

A Look Inside What Is Coming at the 2019 OHDSI Symposium

Using real-world evidence to meaningfully impact the healthcare community will be the prevailing theme Sept. 15-17 during the 5th annual OHDSI U.S. Symposium. Collaborators from around the world will discuss both the direction of the OHDSI community, as well as some of its most important research achievements of the past year, in the highlight event on the OHDSI calendar.

The symposium takes place at the Bethesda North Marriott Hotel & Conference Center, and for the first time, it will include a Women in Real-World Analytics Leadership Forum, hosted Sunday night by the Women of OHDSI. This free event, which is open to all symposium attendees (you can RSVP here), will feature four prominent leaders in the real-world analytics community (Noémie Elhadad, Violanda Grigorescu, Janet Woodcock, and Joanne Waldstreicher), each of whom will share thoughts on their own journey, where they see this emerging discipline headed, and how OHDSI collaborators can improve healthcare in the future.

OHDSI FYI: How To Start A New Working Group

There has been interest recently in developing new working groups within the OHDSI community, but many have wondered what it takes to actually start a new working group. This was addressed previously in an OHDSI forum post, but we wanted to share the steps with you again.

An OHDSI working group represents a group of OHDSI collaborators who hold regular meetings with the purpose of developing shared solutions to tackle a common problem or address a knowledge/technology gap. A group of collaborators aiming to complete a network research study can be considered a study working group and are encouraged to follow these guidelines.

PheValuator Paper Highlights Potential For Reliable Phenotype Evaluation In Future Research

Constructing phenotype algorithms (PAs) is a primary method for both defining diseases and identifying subjects at risk for disease in observational research. While the role of PAs is crucial for effective, reproducible research, the ability to complete detailed PA evaluations has traditionally been limited due to both cost and efficiency.

Lead author Joel Swerdel provided a potential solution to this challenge in PheValuator: Development and evaluation of a phenotype algorithm evaluator, published in the latest issue of the Journal of Biomedical Informatics. Utilizing tools within the OHDSI Network, the research team developed a method that showed promise for reliable phenotype evaluation without reliance on manual review of patient data.

Phenotype Sharing Feasibility Through OMOP Demonstrated In Recent JBI Paper

Traditional methods of identifying phenotypes over varied networks of electronic health record (EHR) databases is challenging. The recently published “Facilitating Phenotype Transfer Using A Common Data Model” paper in the Journal of Biomedical Informatics demonstrated success in creating a systematic process for sharing disease definitions—known as phenotypes—across a network using the Observational Health Data Sciences and Informatics (OHDSI) OMOP Common Data Model, which could lead to dramatic improvements in the ability to study diseases in the future.

George Hripcsak, MD, MS, the co-PI of the OHDSI Coordinating Center at Columbia University, served as lead author for a paper that demonstrated an efficient alternative to phenotype sharing that allows for rapid exchange and execution across different medical centers, improving the speed and reproducibility of the research process.

China Symposium, Tutorials Welcome Potential Collaborators To OHDSI Community

The 2019 China Symposium introduced top scholars, practioners, researchers and more to the potential of OHDSI collaboration.

The annual OHDSI China Symposium, which took place June 27-29 at Shanghai Jiaotong University, reinforced the impressive potential of the OHDSI network via the collaboration of multiple, motivated stakeholders.

Attendees of the symposium included experts and scholars from major universities in China, medical information-related practitioners of various medical and health institutions, medical-related scientific research personnel, and workers interested in big data in the pharmaceutical industry.

The symposium led off with a pair of keynote addresses, including one entitled “FEEDERNET: Evolution of Distributed Research Network in Korea” by OHDSI collaborator Rae Woong Park. Park is a top advocate for the development of OHDSI in Korea, and his presentation helped demonstrate the unique possibilities of the network.

OHDSI Provides Oxford Tutorial, Leads One-Day Study That Posts Impressive Results

OHDSI collaborated with the Oxford Summer School session for a tutorial on tools and a one-day study.

The boundless potential to create real-world evidence through OHDSI was demonstrated for a second time in as many weeks, as collaborators from both sides of the Atlantic met in England during the Real World Epidemiology: Oxford Summer School session.

Peter Rijnbeek and Patrick Ryan led the June 27 session on the OMOP common data model, OHDSI, and the analytical use case of patient-level prediction.

Rijnbeek and Ryan supported the mission of OHDSI collaborator and Oxford professor Dani Prieto-Alhambra. The previous week, Rijnbeek and Ryan supported OHDSI collaborators Iannis Drakos and Ismail Gögenur during a three-day seminar for the Denmark Center for Surgical Sciences (CSS).

“It was great to have OHDSI join our summer school,” Prieto-Alhambra said. “Forty people’s jaws dropped whilst learning what can be achieved through open science, community and a common data model!”

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