Evidence Generation studies: long COVID and perinatal research (CC#13)

Our Community call #13 from 18 March, 2024 included two presentations:

1). Annika Jodicke presented Findings and Learnings from a Long COVID-19 study-a-thon that was conducted in April last year at Oxford with thirteen EHDEN Data Partners from nine countries, including three clinical experts. The study-a-thon was undertaken as long-term COVID-19 complications are on the rise and to improve the limited understanding of post-acute COVID-19 conditions.

The three primary objectives were to:

  • Describe the epidemiology of Long COVID and post-acute symptoms (PASC)
  • Characterise populations with Long COVID in PASC
  • Identify Long COVID subgroups

With more than 780,000 individuals included in the research, it's the largest study on post COVID symptoms to date. It was determined that there is substantial heterogeneity in the incidence of post-acute symptoms and complications across healthcare settings. It was also established that the current definitions of post-acute COVID conditions should be critically reviewed to reflect this variety in clinical presentation.

2). On behalf of the research team, Nhung TH Trinh presented on: Expanding the OMOP Common Data Model to support perinatal research in network studies. The research team was comprised of perinatal domain experts, epidemiologists and data scientists.

Key findings/results included:

  • PET helps to standardise perinatal data in the OMOP-CDM
  • Research undertaken will facilitate future network perinatal studies
  • Successful implementation of two large EHDEN DP databases (Spain & Norway)
  • PET Diagnostics R package: testing on a wider range of data types and source
  • PET Characterisation R package: standardised summary of information in expansion tables
  • There's a need of validated pregnancy algorithm