June 2017

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JUNE 2017	01
The Power of Harmonization: EMIF Enables Large-Scale Analytics by Embracing the OMOP Common Data Model
EMIF is currently mapping European data sources to the OMOP CDM and is extending OHDSI tools such as ATLAS. In order to properly answer some of the biggest research questions of our time—including those that would help us solve the mysteries of Alzheimer’s disease and metabolic diseases—we need to assess large amounts of data in a fully transparent and reproducible way. Standardization of data structure and language through a Common Data Model (CDM) enables this on an unprecedented scale.	KEY POINTS
		Data harmonization is the process of translating data into a common format and common vocabulary to enable collaborative research and large-scale analytics.
		The European Medical Information Framework (EMIF) has a core objective of building an information framework for consistent re-use of available patient-level data to support novel research.
		The Observational Health Data Sciences and Informatics (OHDSI) collaborative works toward the international implementation of the OMOP Common Data Model (CDM) and its standardized vocabularies.
		EMIF is currently mapping European data sources to the OMOP CDM, and is extending OHDSI tools to enable the large-scale analytics that are key to EMIF’s core objectives.
	CONTRIBUTORS
		MICHEL Van SpeybroeckDirector of Data Sciences at Janssen
		PETER Rijnbeek, PhDAssistant Professor at Erasmus University Medical Center
		JOHAN van der LeiAcademic Coordinator EMIF-PLAT, Professor at Erasmus University Medical Center

Every moment, countless healthcare data points are generated and stored in digital databases across Europe. Analyzing these datasets is a challenge, because data structure and content can vary greatly from one healthcare organization to the next. Data is collected for different purposes: clinical research, patient care, payer reimbursement, and more. This means that the content and level of detail varies considerably. Datasets are stored in different formats using different database management systems. Moreover, different codes are used for the same medical concepts across Europe, which creates additional challenges.

How can we overcome these overwhelming barriers to performing multi-database studies across Europe and beyond?

The European Medical Information Framework (EMIF) recognized these challenges from day one. Standardization was initially done in the context of a specific research question on a limited set of variables. However, EMIF realized that this approach was difficult to maintain when answering research questions on a large scale. A substantial upfront standardization effort would be necessary for scalability. Additionally, studies needed to be performed in a distributive manner to comply with governance and privacy rules by keeping standardized datasets within their local infrastructures. The only shared analytical datasets needed to be generated using a standardized processing pipeline. 

EMIF found the solution to its challenges by embracing the Observational Health Data Sciences and Informatics (OHDSI, pronounced “Odyssey”) model of translating all the data into a single common structure and language. This concept is known as “data harmonization” (also sometimes called “data standardization”).

Diagram representing the process of data harmonization using the OMOP Common Data Model.

OHDSI grew out of the Observational Medical Outcomes Partnership (OMOP), a public-private partnership originally established in the US. The OMOP project developed the initial version of the OMOP Common Data Model, which can accommodate healthcare data from diverse sources in a consistent and standardized way. OHDSI’s mission is to improve health, by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care. A key objective is to stimulate transparent and reproducible research for which the CDM is a prerequisite.

Today, OHDSI is an international collaborative comprised of academics, industry scientists, healthcare providers, and regulators who have come together to create and apply open-source data analytic solutions to a large network of health databases. It’s a non-funded entity, supported by the generosity and passion of individual members as well as in-kind contributions from organizations such as Columbia University and Johnson & Johnson. An inspiring spirit of global collaboration is what powers OHDSI. 

EMIF is embracing OHDSI and the OMOP CDM for many reasons. The OMOP CDM is a well-adopted model covering over 600 million patient lives worldwide. All the code and tools are open source and publicly available, which stimulates collaborative tool improvement and transparency. Moreover, the currently available tools fit naturally with EMIF’s objective to facilitate different levels of data access. 

Workflow to map EMIF EHR data sources to the OMOP CDM, leveraging OHDSI tools and experience.

The CDM is most widely used in the US and Asia, and we are only beginning to scratch the surface of its use in Europe. We at EMIF are helping to prototype the CDM on the European continent. For example, we’re exploring whether there are levels of detail in some European Electronic Health Records (EHR) that would be lost if we translated data to the CDM. Our peers and fellow collaborators at OHDSI are constantly adjusting the CDM and its vocabularies to accommodate those types of insights.

The CDM is also evolving as healthcare evolves. As the OMOP Common Data Model expands to other continents, countries, and disease areas, the CDM expands thanks to the collaboration of OHDSI champions around the globe.

EMIF wants to actively support the adoption of the OMOP CDM across Europe. If you are a data custodian in Europe who wants to participate in this innovative data network, contact Michel Van Speybroeck, Peter Rijnbeek, or Johan van der Lei to learn more. While mapping your data does require a time commitment, EMIF is available to help. Additionally, there are resources available on the ODHSI website and training workshops offered during the free-to-attend OHDSI symposia.

Documents with best practices for converting data into the OMOP CDM are available on the OHDSI wiki page.

There are clear advantages to adopting the tools that ODHSI and EMIF are working on. Thanks to the multi-disciplinary expertise of the OHDSI community, the quality and functionality of the tools is constantly being improved. And the process is open source from start to finish, so no licenses are ever needed.

Perhaps most importantly, all data privacy concerns are respected by keeping your data local. You are implementing the CDM yourself. Even if we at EMIF provide support with the technical side of the process, your data is still kept locally. You’ll be able to maintain the privacy of your database while contributing to a worldwide effort to harmonize data. Your decision to map your database to the CDM means that you are supporting the progress of reproducible scientific research on a local, national, and international level. 

EMIF is actively mapping nine European data sources to the OMOP CDM and OHDSI vocabularies: Denmark (AUH), Italy (ARS, IMS HEALTH LPD, PEDIANET), Spain (IMASIS, SIDIAP), UK (THIN), Estonia (EGCUT), and the Netherlands (IPCI, PHARMO). EMIF will produce a scientific paper this year that evaluates the mapping process of these European datasets and formulates suggestions for improving the CDM and its vocabularies. Furthermore, EMIF will continue the effort of extending and refining the OHDSI tools to meet its objectives.

If we want data analysis at an unprecedented scale, along with fully transparent and reproducible research pipelines, we need to collectively harmonize our data structures and terminology. The OMOP CDM is clearly becoming the de facto standard for electronic health data across the world. EMIF recognizes this exciting new reality, from which all stakeholders will benefit: researchers, organizations, and most importantly patients—who are ultimately at the center of this global effort.

If you are a researcher and want to explore more, please visit the EMIF Catalogue. If you are a data custodian interested in learning more about the OMOP-CDM for your data source, contact Michel Van Speybroeck, Peter Rijnbeek, or Johan van der Lei. 

JUNE 2017	02
With Knowledge Comes Responsibility: Harmonizing Cohort Data for EMIF-AD
European Medical Information Framework for Alzheimer’s Disease (EMIF-AD) Work Package (WP) 1 team members, left to right: Pieter Jelle Visser, Stephanie Vos, and Isabelle Bos. European Medical Information Framework for Alzheimer’s Disease (EMIF‐AD) researchers Simon Lovestone, Pieter Jelle Visser, and Johannes Streffer led their teams to identify potential fluid markers of brain amyloid in small studies of cognitively normal people. They wanted to confirm their findings in a sample of 1,000 individuals, but didn’t have the resources to assemble such a large cohort de novo. Luckily, one of EMIF’s objectives is to make it possible to re-use data instead of generating new data from a single purpose. Electronic Health Record (EHR) data is standardized through the OMOP Common Data Model; however, for cohort data, another solution was necessary.	KEY POINTS
		The European Medical Information Framework (EMIF) Catalogue allows users to quickly complete new research by identifying cohorts via the EMIF Alzheimer’s disease (EMIF-AD) community.
		Analyses of large pooled datasets are made possible by the EMIF-Platform technical team who developed the idea of using knowledge objects for the purpose of data harmonization.
		The EMIF-AD team invites data custodians and researchers to provide metadata about their cohort and share more data and research questions to help crack the dementia code.
	CONTRIBUTORS
		RUDI VerbeeckSenior Manager at Janssen R&D IT
		ISABELLE BosNeuropsychologist and PhD Candidate at Maastricht University
		LUIZA Gabriel-ZeltzerPostdoctoral Researcher at Janssen R&D IT
		STEPHANIE VosNeuropsychologist and Postdoctoral Researcher at Maastricht University
		PIETER Jelle VisserAssociate Professor, Maastricht University, VU University Medical Center, Project Lead of the AD topic

Within EMIF-AD, projects are divided into work packages (WPs). WP 1 focuses on the “definition of study population data requirements and data collection.” Members of the EMIF-AD WP 1 team (Pieter Jelle Visser, Stephanie Vos, and Isabelle Bos) consulted the EMIF Catalogue AD community, an online community of existing cohorts maintained by EMIF‐AD. They identified 11 suitable cohorts with participants who had the characteristics the researchers were looking for in terms of age, sex, cognitive status, amyloid biomarkers in cerebrospinal fluid (CSF) or on positron imaging tomography (PET), and availability of MRI scans and fluid samples in plasma or CSF.

The EMIF-AD WP 1 team contacted principal investigators of these cohorts and all of them were interested to participate. Contracts were set-up to allow data-sharing and biomarker analysis. Data source owners harmonized—or translated—their local variables to a global set of variables so that the data could be used in the same study. In this way, a large cohort was assembled quickly and inexpensively.

The final Biomarker Discovery Study included 1,221 subjects. The data combined proteomics, genomics, metabolomics, and imaging data in a single analysis. This broad spectrum analysis in a large cohort of subjects at various cognitive stages will provide new insights into underlying mechanisms and facilitate the discovery of new diagnostic and prognostic markers for AD. This study is coordinated by Simon Lovestone, Johannes Streffer, and Pieter Jelle Visser in close collaboration with proteomics, metabolomics, transcriptomics, genomics, bioinformatics and imaging experts in EMIF-AD.

The Biomarker Discovery Study and other analyses of large cohorts were made possible by a collaboration between EMIF-AD researchers and the EMIF-Platform technical team who developed the idea of using knowledge objects for the purpose of data harmonization. A knowledge object is a variable, plus all the information that you need in order to correctly interpret what that variable means. For instance, “sex” is a knowledge object included in the Biomarker Discovery Study, and “male” and “female” are common values for that knowledge object.

Diagram envisioning the process of data harmonization, mapping local knowledge objects to global knowledge objects. Data sources appear on the left, while harmonized data appears on the right.

When the EMIF-AD researchers gathered the data from 11 different studies to assemble the EMIF-AD WP3 1000 cohort, they were in different languages and none of them used exactly the same standards for recording variables. If they had tried to access the original data from the data sources, the variable “sex” might have appeared as “genero” or “geschlecht.” More obscure variables may have been difficult to interpret or impossible to translate to similar datasets without a common reference point.

That’s why Rudi Verbeeck, Senior Manager at Janssen R&D IT and EMIF contributor, believes cohort data custodians play a crucial role in the quest to aggregate data. Only a data custodian knows what the variables in their data source truly represent. Thanks to the EMIF-Platform team, custodians now have the power to define their local variables so their data can be translated to a standard set of global variables.

“If you have the knowledge, you have the responsibility,” explains Rudi. Data custodians have knowledge about their data, so they are responsible for defining their local variables. Researchers know what points of data support their research, so they are responsible for identifying the variables they want to access. But who brings together the knowledge of the data custodians with the knowledge of the researchers so that data can be harmonized and large cohorts can be analyzed?

Isabelle Bos and Stephanie Vos, neuropsychologists and researchers at Maastricht University, are members of the EMIF-AD WP 1 team. They’ve chosen to take on this responsibility of helping data custodians translate their local knowledge objects to global knowledge objects. If data custodians don’t have the technical skills to map the data themselves, the data custodian specifies how the mapping should be done, then the EMIF-AD WP 1 team creates the code that needs to be executed. There are currently 14 cohorts on the data platform tranSMART from across Europe (Netherlands, UK, Belgium, Spain, Italy, Sweden, France, Greece). The total number of subjects across all 14 cohorts is 3,423, and this number keeps growing.

Local knowledge objects (LKO) appear on the left side of this semantic dependency graph. The local knowledge objects are translated to global knowledge objects (GKO) using snippets of code (represented by the arrows). Sometimes, there is a one-to-one correlation between a local knowledge object and a global knowledge object. Other times, multiple local knowledge objects feed into a single global knowledge object through a series of standardizing equations.

Over time, EMIF-Platform will continue building its library of reusable knowledge objects and the snippets of code that connect them. If a researcher makes a request in the EMIF Catalogue AD community for variables that haven’t been used before, those knowledge objects will need to be mapped by the data custodians with support from the EMIF-AD WP 1 team.

Since every research question adds to the library, the probability of being able to reuse a knowledge object or snippet of code increases with every research question that comes along.

In order to tackle some of the biggest health challenges of our time, researchers must be able to conduct efficient and cost-effective data analyses on a large scale. For that, data harmonization is a necessity. With a cohort of 100 subjects, you can’t answer complex research questions—but increase that cohort to 1,000, and you may uncover the kind of exciting results that the EMIF-AD group and their collaborators are seeing in the Biomarker Discovery Study.

Before we can harmonize data, it needs to be shared. If you own cohort-derived data—especially if it includes biomarkers—and would like to collaborate, please consider working with the EMIF-Platform team to harmonize your data using knowledge objects. Harmonized cohort data could lead to other breakthroughs in the fight against Alzheimer’s.

If you are a researcher, explore the data sources already available through the EMIF Catalogue AD and EHR communities, which were made publicly available to bona fide researchers outside of EMIF in January 2017. Every day, new people from around the world join the 309 users who have accessed the Data Catalogue as they work toward their research goals. If you could assemble huge cohorts quickly and inexpensively, what kinds of studies would you create? What discoveries might you spearhead? EMIF wants to know and wants to help you make them happen.

JUNE 2017	03
Introducing Use Case 12: Using Electronic Health Record Data to Understand the Relationship Between Dementia and Inflammation
An Oxford University research team is exploring the links between dementia and inflammatory diseases.  The medical science and research communities are pondering the relationship between dementia and inflammatory diseases. Alejo J. Nevado-Holgado and Danielle Newby, post-doctoral researchers at the University of Oxford, are exploring these links in more depth. They found themselves asking: Do inflammatory diseases—or the drugs used to treat them—change the risk of dementia in later life? This compelling research question set the stage for Use Case 12, a project now underway within the European Medical Information Framework for Alzheimer’s disease (EMIF-AD).	KEY POINTS
		By analyzing electronic health records (EHR) data through the European Medical Information Framework (EMIF) platform, we can find out more about the link between inflammation and dementia.
		Use Case 12 is utilizing EMIF’s visible, accessible, harmonized datasets to complete ground-breaking research about whether inflammatory diseases or the drugs used to treat them change the risk of dementia in later life.
		Later in 2017, look for published information that will likely assist in the development of therapies and treatments for dementia.
	CONTRIBUTORS
		DANIELLE NEWBYPost-doctoral researcher at University of Oxford, Data analyst of Use Case 12
		ALEJO NEVADO-HOLGADOSenior post-doctoral researcher at University of Oxford, Lead of Use Case 12 project
		SIMON LOVEStoneProfessor of Translational Neuroscience at University of Oxford, Overall joint program lead for EMIF

The motivated team at Oxford was curious about the relationship between inflammatory diseases, anti-inflammatory drugs, and dementia. They saw an exiting opportunity to work from large, real-world, routine care datasets, but realized such studies are challenging to perform. For one thing, large datasets are needed for analysis. For another, research study cohorts can suffer from selection and are not always fully representative. “If large datasets are accessible,” Danielle emphasizes, “then another challenge presents itself: it’s tough to get consistent datasets due to the different structures of electronic health records (HER) in different countries.”

EMIF provided an opportunity to overcome these problems by making datasets visible, accessible, and harmonized. Through Use Case 12, the Oxford team is now examining the relationship between diseases of inflammation and the drugs used to treat inflammatory disorders in multiple datasets. Through this work, they’re revealing stronger evidence of the relationship between dementia and inflammatory exposures. The team’s ground-breaking research sets an example for others. Other researchers may be excited to learn that the questions like those posed within Use Case 12 can be analyzed and answered efficiently by working with EMIF.

To start Use Case 12, the team needed a good question that could potentially be answered using EHR data. With a hypothesis relating to inflammation and dementia, their protocol underwent ethical and peer review by experts in the field.

The next step was to contact the data custodians responsible for some of the datasets in the EMIF Catalogue; those who were able to participate joined Use Case 12. The team then asked the data custodians to extract the variables they needed from anonymized patient level records from their databases and place them on a secure remote research environment. Daniel Prieto-Alhambra, scientific coordinator for the SIDIAP database, was enthusiastic about contributing the SIDIAP database for Use Case 12, stating that “the inclusion of the SIDIAP dataset into Use Case 12 offers a great opportunity to utilize the wealth of information from this database to help understand such an important topic.”

After quality control checks for each of the datasets, local database experts started their work. For each dataset, they ran a software tool which aggregated the files for each dataset, removed any outliers, and selected the sample population based on the criteria specified in the original protocol. Thanks to a seamless collaboration with the EMIF-Platform team, the data for each dataset at this stage was then ready for statistical analysis.

The Workflow of Use Case 12

Use Case 12 is on the cusp of the most exciting phase: data analysis! Nearly all the datasets have been extracted and are almost ready to be analyzed. The team built a clever step into their process; they are analyzing one dataset first, so they can run preliminary checks and help troubleshoot the analysis of the rest of the datasets.

Alejo confirms that participating in Use Case 12 has been “an excellent learning experience for our team” when it comes to the opportunities and challenges of analysis across multiple real-world datasets. “We have been fortunate to have good help and expertise to guide us and move the work forward successfully,” Danielle reports. Later in 2017, look for a publication of the findings from all of the datasets in Use Case 12. The team believes the results could lead to meaningful advancements in the treatment of Alzheimer’s disease and dementia.

The EMIF use cases are a perfect example of successful collaborations and data sharing between and among researchers and data custodians. If you have an idea that you think could be answered using EHR data, then the progress of Use Case 12 should give you confidence that a robust analysis is possible. If you would like to learn more and get involved in the continuing progress of Use Case 12, contact Danielle or Alejo to collaborate or network. They welcome ideas, help, and expertise from anyone within EMIF who is interested in being involved with dementia research. Danielle, speaking on behalf of the Use Case 12 team, says: “We want to collaborate with you and share our knowledge to make even greater progress toward our ultimate goal: the development of new therapies and treatments for dementia.”

For further reading about determining the molecular pathways underlying the protective effect of non-steroidal anti-inflammatory drugs for Alzheimer’s Disease, please see Alejo and Simon’s October 2016 publication.