Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

The IMI EHDEN project: large-scale analysis of observation data in Europe - CDISC EU April 2nd 2020 - Maxim Moinat and Nigel Hughes

129 views

Published on

The European Health Data & Evidence Network (EHDEN) project, funded via the Innovative Medicines Initiative (IMI), is the largest of its kind in Europe working in the domain of RWD/RWE. It is a public private partnership consortium of 22 partners, from 2018 to 2024, led by Erasmus Medical Center (EMC) and Janssen, working to create an open science community symbiotic with the Observational Health Data Science and Informatics (OHDSI) global framework to facilitate observational/RWD-based research at scale and acceleration, without impinging on quality. At its core is the standardisation of RWD via use of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM), standardised analytics and a sustainable research community for the coming decades. Potentially, between EHDEN and OHDSI there are several use cases developed on the boundaries between clinical trials and observational data, and we look forward discussing these with the CDISC community.

Published in: Health & Medicine
  • Be the first to comment

The IMI EHDEN project: large-scale analysis of observation data in Europe - CDISC EU April 2nd 2020 - Maxim Moinat and Nigel Hughes

  1. 1. CDISC 2020 Europe Interchange Virtual Conference 1-2 April 2020
  2. 2. Maxim Moinat is a data engineer at The Hyve with a background in Bioinformatics and Medical Sciences. In the last few years, he has been an active member of the OHDSI community and gained a vast experience in converting a variety of observational healthcare data to the OMOP Common Data Model. As a co-lead of the technical work package in the EHDEN consortium, he is contributing to establishing an European health data network. Besides EHDEN, Maxim is driving The Hyve's involvement in two other public consortia, BigData@Heart and PIONEER, where The Hyve is responsible for the OMOP CDM conversion work and technical infrastructure. CDISC 2020 Europe Interchange | #CDISCEurope #ClearDataClearImpact2 Maxim Moinat, EHDEN WP4 co-lead, The Hyve
  3. 3. Nigel Hughes, EHDEN Project Leader, Janssen Nigel Hughes has a thirty-five year career spanning the NHS in the UK (16 years), NGOs and patient organisations (10 years) and within the pharmaceutical industry (17 years). He has worked clinically in HIV and viral hepatitis, liver disease, and in sales & marketing, medical affairs, market access and health economics, R&D, precision medicine, advanced diagnostics, health IT and Real World Data/Real World Medicine. His experience covers clinical, education, as an advisor, consulting, communications and lobbying over the years. He is currently the Project Lead for the IMI2 European Health Data & Evidence Network (EHDEN), and was Platform Co-Lead for the IMI1 European Medical Information Framework (EMIF), as well as consulting on numerous projects and programmes in the domain of RWD/RWE CDISC 2020 Europe Interchange | #CDISCEurope #ClearDataClearImpact3
  4. 4. Disclaimer and Disclosures • The views and opinions expressed in this presentation are those of the author(s) and do not necessarily reflect the official policy or position of CDISC. 4 • The author(s) have no real or apparent conflicts of interest to report.
  5. 5. Enabling Large-Scale Analysis of Electronic Health Records in Europe April 2nd 2020 Session 11
  6. 6. Agenda 1. What is EHDEN? Goals and Achievements 2. What is OHDSI? Mission, Community and Data Standards 3. EHDEN Study-a-thon. The power of collaborative science 4. Clinical Trials. Setting new OHDSI conventions 5. RWD in Regulatory Setting
  7. 7. 7 The challenge of Real World Data
  8. 8. What is EHDEN? Goals and Achievements
  9. 9. 9 European Network: EHDEN
  10. 10. EHDEN Consortium Start date: 1 Nov 2018 End date: 30 Apr 2024 Duration: 66 months Non-for-profit organisations Small to medium-sized companies EFPIA & Associated partners Universities, public bodies and research organisations Almost €29 million Academic coordinator EFPIA Lead 22 partners Innovative Medicines Initiative Project 10
  11. 11. A sustainable ecosystem: call process overview 12
  12. 12. A sustainable ecosystem: call process overview Tailored for project objectives and sustainability Data sources Open calls Evaluated via a pre- defined set of criteria by the Data source prioritisation committee Grant awarding Max. 100k 13 First workplans created
  13. 13. A sustainable ecosystem: call process overview Tailored for project objectives and sustainability Data sources Supporting SMEs Open calls Focusing on SMEs able to support mapping and sustainability Open calls Evaluated via a pre- defined set of criteria by the Data source prioritisation committee Grant awarding Training & Certification SME certification committee prioritizes SMEs for training and certification Max. 100k 14 First two groups certified!
  14. 14. A sustainable ecosystem: call process overview Tailored for project objectives and sustainability Data sources Supporting SMEs Open calls Focusing on SMEs able to support mapping and sustainability Open calls Workshop Source Data Evaluation Share of Mapping Process Mapping Audit Mapping Cycle Evaluated via a pre- defined set of criteria by the Data source prioritisation committee Harmonisation fund Data sources can choose the SME from the pool of EHDEN certified SMEs SMEs are paid via grants from the harmonisation fund Payments are milestone based Mapped data sources are encouraged to be active members of the EHDEN community, participating in research studies. Grant awarding Training & Certification SME certification committee prioritizes SMEs for training and certification Max. 100k 15
  15. 15. EHDEN hit the ground running OMOP CDM EHR Catalogue Private remote research environment Accessible InteroperableFindable Reusable ATLAS Ethical code of practice 17 Main developers of many of the OHDSI tools are EHDEN partners Collaborators
  16. 16. What is OHDSI? Mission, Challenges and the Community
  17. 17. 19 OHDSI Vision A world in which observational research produces a comprehensive understanding of health and disease. OHDSI Mission To improve health by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care. https://www.ohdsi.org/who-we-are/mission-vision-values/
  18. 18. Large-scale observational research is feasible “Characterizing treatment pathways at scale using the OHDSI network.” George Hripcsak et al. - PNAS (2016)27:7329–7336 11 Data sources 4 Countries > 250 million patients T2 Diabetes Mellitus Hypertension Depression 21
  19. 19. Common Data Model to enable Standardised Analytics 22
  20. 20. OMOP Common Data Model 24
  21. 21. 25 OMOP standard vocabularies Standardised globally Analytical standards: SNOMED, RxNorm, LOINC More than 100 ‘source’ vocabularies mapped to the standards 7.4 million concepts 3.0 million standard + 0.5 million classification Comprehensive hierarchy: ~45 million relationships Publically available: https://athena.ohdsi.org
  22. 22. EHDEN Study-a-thon The power of collaborative science
  23. 23. 27 Two successful Study-a-thons https://youtu.be/X5yuoJoL6xs Results published as dashboards PLEE: http://data.ohdsi.org/UkaTkaSafetyEffecIveness/ PLP: http://data.ohdsi.org/oxfordMortalityExternalValidaIon/ "From question to publication in 5 days”
  24. 24. Results “To compare the risk of post-operative complications (infection, revision, and venous thrombo-embolism) between Unicompartmental (UKR) vs Total Knee Replacement (TKR).” On multiple, distinct datasets
  25. 25. COVID-19 study-a-thon • Virtual event • >300 collaborators • Four timezones • Three focus areas • Nine concurrent network studies 29
  26. 26. Converting Clinical Trial to the OMOP CDM Setting new OMOP CDM conventions
  27. 27. Use cases at the intersection of OHDSI and CT Using RWD for Clinical Trial Design ● Patient enrichment phase ● Trial planning and recruitment optimization ● Virtual patient cohorts ● Predict trial adherence 31 Linking RWD and CT data ● Standardize cohort definitions across CT and RWD ● Use CT data as source for RWE ● Perform cross clinical trial study analysis. ● Enabling RWD assets to support interventional analyses
  28. 28. 32 Derive person id Measurement date measurement concept 4232915 - Sitting systolic blood pressure (LOINC) measurement value Vital Signs -> Measurement Derive Visit id measurement unit
  29. 29. Initial Findings: differences OMOP and SDTM 1. Trial visits 2. Study and arm assignment 3. Measurement Modifiers 4. Include new vocabularies for e.g.: a. Biomarkers b. Novel drug assets c. ... 33 New conventions needed!
  30. 30. OHDSI Clinical Trials Working Group Next step: apply these conventions to available data sources: • CSDR • Datasphere • C-path • Vivli • PHUSE 34
  31. 31. Using RWD for Regulatory Purposes Accelerating Clinical Research
  32. 32. For Regulatory & HTA Bodies, Real World Data is now very real…. 36CDISC 2020 Europe Interchange | #CDISCEurope #ClearDataClearImpact “Post-launch evidence is not a means of replacing randomized clinical trials, but should be seen as complementary knowledge”2 “Increase the access to and analysis of real- world data in EU so that it supports robust decision-making, noting however that Post Licensing Evidence Generation (PLEG) includes experimental and observational data”2 1. HMA-EMA Joint Big Data Taskforce; Phase II report – Evolving Data-Driven Regulation; Amsterdam, 20th January 2020 2. Moseley J, et al; Regulatory and health technology assessment advice on Post-licensing and Post-Launch Evidence Generation is a foundation for lifecycle data collection for medicines; British Journal of Clinical Pharmacology 2020; online, 11 March “Big Data is not necessarily the solution to all the challenges faced by regulators in reaching appropriate decisions. While randomised, double-blind, controlled clinical trials will remain the reference standard for most regulatory use cases, the complementary evidence that new Big Data sources generate may facilitate, inform and improve our decisions. It is clear that the data landscape is evolving and that the regulatory system needs to evolve as well. In this way we can realise opportunities for public health and innovation through better evidence for decisions on the development, authorisation and on-market safety and effectiveness monitoring of medicines. If we work now, smartly and collaboratively, and embrace change we can evolve to deliver better regulation for patients and establish the EU medicines regulatory network as a reference for data-driven decision-making”1 HMA-EMA Big Data Taskforce, 20th January 2020
  33. 33. The poster child of RWD/adaptive studies…. 37CDISC 2020 Europe Interchange | #CDISCEurope #ClearDataClearImpact 1. http://www.gsk.com/en-gb/media/press-releases/2016/salford-lung-study-results-show-copd-patients-treated-with-relvar-ellipta-achieve-superior-reduction-in- exacerbations-compared-with-usual-care/; accessed 23/06/16
  34. 34. Current use cases on e.g. synthetic controls exist…. 38CDISC 2020 Europe Interchange | #CDISCEurope #ClearDataClearImpact 1. https://dcricollab.dcri.duke.edu/sites/NIHKR/KR/GR-Slides-06-15-18.pdf; accessed 24th March 2020 Roche met EU coverage requirements for marketing alectinib in 20 European markets using a synthetic control arm Rather than waiting for phIII data, Roche used a synthetic control arm of 67 patients to provide the necessary evidence of relative performance. This accelerated coverage by 18 months
  35. 35. Thank You!
  36. 36. ohdsi.org | ohdsi-europe.org github.com/ohdsi forums.ohdsi.org ohdsi.org/2019-ohdsi-symposium-materials/ ehden.eu github.com/ehden enquiries@ehden.eu More Information thehyve.nl github.com/thehyve office@thehyve.nl blog.thehyve.nl/blog/topic/omop-ohdsi 💡 💡 40
  37. 37. The Book of OHDSI 41 book.ohdsi.org

×