The European Medicines Agency (EMA) and the European Medicines Regulatory Network established a coordination centre to provide timely and reliable evidence on the use, safety and effectiveness of medicines for human use, including vaccines, from real world healthcare databases across the European Union (EU).

This capability is called the Data Analysis and Real World Interrogation Network (DARWIN EU®).

How does DARWIN EU make health data count?
Cover of the fourth annual report on regulatory-led studies using RWD
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Fourth annual report on regulatory-led studies using RWD published

EMA has published the fourth annual report on regulatory-led studies using RWD. This report outlines the progress in integrating real-world evidence (RWE) into regulatory decision-making, aligned with the European Medicines Regulatory Network (EMRN) strategy to 2028 and in anticipation of the provisions from the new pharmaceutical legislation. It comprises two RWE generation pathways coordinated by the European Medicines Agency (EMA): DARWIN EU®, and studies commissioned via framework contracts.

Highlighted Studies

Visual abstracts for ten key DARWIN EU® studies

Patient-level characterisation

Study Type/s

Patient-level characterisations are classified as ‘off the shelf’ 

Study Design

Cohort analysis.

Participant/s

Patient-level characterisation studies will include one or more cohort/s of people newly diagnosed with 1 or more pre-specified condition/s and with some amount of data visibility before diagnosis, and with no record of the same condition/s in the previous year (or in all previous history).

Additional eligibility criteria could apply as follows, to be incorporated as sensitivity analyses:

  • Additional restriction/s could apply based on socio-demographics, e.g., people aged 18 or older at the time of diagnosis
  • Additionally, people with a competing (differential) diagnosis could also be excluded (e.g., people with rheumatoid arthritis with a history of psoriatic arthritis could be excluded to minimise misclassification)

Follow-up

Participants will be followed up from their date of new diagnosis (index date) until the earliest of the following: loss to follow-up, end of data availability, a pre-specified time period (e.g. 1 year after index date) or death.

Analyses

Details will be discussed during programming of pipelines, but it is likely that patient-level characterisation will include:

  • Automated large-scale characterisation, including all recorded baseline characteristics available in the data before or on index date, based on code/s, and classified into conditions (medical history), medicine/s use, and procedure/s
  • Pre-specified patient-level characteristics on and/or before index date, based on pre-existing code lists or definitions (e.g., history of type 2 diabetes, or Charlson comorbidity index)
  • Pre-specified patient-level characteristics on and/or before index date, based on concepts and descendants where no previously validated algorithms are available
  • Incidence rate/s of pre-specified outcome/s within a pre-specified time period (e.g. 1 year)
  • Prognosis / progression to a pre-specified outcome within a pre-specified time, e.g., cumulative incidence of certain events or mortality within 1- or 5-years after diagnosis
  • Standard care description, including n (%) receiving each of a pre-specified list of medicine/s, device/s or procedure/s, and combinations within a pre-specified time window after diagnosis
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