OHDSI Denmark

Background 

The vision for establishing a Danish national OHDSI node is to help improve patient care through reliable evidence facilitated through the OMOP CDM with best practice methods and tools. The node will serve as a national forum where stakeholders can jointly identify needs and develop solutions needed to advance the OHDSI mission and vision and coordinate activities between national and international collaborators. A special emphasis will be made to identify common areas of expertise that will facilitate quality control processes that can ensure the highest possible health data quality for research and clinical implementation.  

Objectives 

  • Build the Danish OHDSI Denmark community
  • Establish national standards for data transformation into the OMOP CDM to support clinical grade evidence and support the use of OMOP standardization for the deployment of data-driven medicine
  • Advance the use of the OMOP CDM in Denmark and facilitate international collaboration

Leading Organisation(s)

Center for Surgical Science,
Department of Surgery,
Zealand University Hospital,
Lykkebækvej 1,
4600 Køge,
Denmark

Members

Name

Organisation

Ismail Gögenur

Center for Surgical Science, Zealand University Hospital & University of Copenhagen

Martin Zahle Larsen

Data Analytics Centre, Danish Medicines Agency

Susanne Bruun

Data Analytics Centre, Danish Medicines Agency

Elvira Bräuner

Data Analytics Centre, Danish Medicines Agency

Stine Hasling Mogensen

Data Analytics Centre, Danish Medicines Agency

Carsten Utoft Niemann

Chronic Lymphocytic Leukemia Laboratory, Rigshospitalet & University of Copenhagen

Anton Pottegård

University of Southern Denmark

Christian Fynbo Christiansen

Department of Clinical Epidemiology & Center for Clinical and Genomic Data, Aarhus University Hospital & Aarhus University

Ulrik Lassen

Phase 1 Unit, Rigshospitalet & University of Copenhagen

Andreas Bjerrum

Department of Clinical Oncology, Rigshospitalet

Anders Riis-Jensen

Data unit, Center for Økonomi, Region Hovedstaden

Charles Vesteghem

Clinical Data Science, Aalborg Universitetshospital

Martin Høyer Rose

Center for Surgical Science, Zealand University Hospital

Andi Tsouchnika

Center for Surgical Science, Zealand University Hospital

Andreas Weinberger Rosen

Center for Surgical Science, Zealand University Hospital

Benjamin Skov Kaas-Hansen

Department of Intensive Care, Rigshospitalet

Davide Placido

Department of Intensive Care, Rigshospitalet

Hans-Christian Thorsen-Meyer

Department of Intensive Care, Rigshospitalet

Data Partners

Data Source Name

Organisation

Data Type

Link

DCCG

CSS

Registry

https://www.rkkp-dokumentation.dk/Public/Databases.aspx?db2=1000000650

RLRR

CSS

Registry

https://sundhedsdatastyrelsen.dk/da/registre-og-services/om-de-nationale-sundhedsregistre/doedsaarsager-og-biologisk-materiale/laboratoriedatabasen

DNPR

CSS

Registry

https://sundhedsdatastyrelsen.dk/da/registre-og-services/om-de-nationale-sundhedsregistre/sygdomme-laegemidler-og-behandlinger/landspatientregisteret

NPR

CSS

Registry

https://sundhedsdatastyrelsen.dk/da/registre-og-services/om-de-nationale-sundhedsregistre/sygdomme-laegemidler-og-behandlinger/laegemiddelstatistikregisteret

Publications

  • Vogelsang RP, Bojesen RD, Hoelmich ER, Orhan A, Buzquurz F, Cai L, et al. Prediction of 90-day mortality after surgery for colorectal cancer using standardized nationwide quality-assurance data. BJS open. 2021;5(3).
  • Lin V, Tsouchnika A, Allakhverdiiev E, Rosen AW, Gögenur M, Clausen JSR, et al. Training prediction models for individual risk assessment of postoperative complications after surgery for colorectal cancer. Tech Coloproctol. 2022;26(8):665–75.
  • Hartwig M, Bräuner KB, Vogelsang R, Gögenur I. Preoperative prediction of lymph node status in patients with colorectal cancer. Developing a predictive model using machine learning. Int J Colorectal Dis. 2022;37(12):2517–24
  • Bräuner KB, Rosen AW, Tsouchnika A, Walbech JS, Gögenur M, Lin VA, et al. Developing prediction models for short-term mortality after surgery for colorectal cancer using a Danish national quality assurance database. Int J Colorectal Dis. 2022 Aug 1;37(8):1835–43.
  • Justesen TF, Gögenur M, Clausen JSR, Mashkoor M, Rosen AW, Gögenur I. The impact of time to surgery on oncological outcomes in stage I-III dMMR colon cancer – A nationwide cohort study. Eur J Surg Oncol. 2023;49(9).
  • Gögenur I. Introducing machine learning-based prediction models in the perioperative setting. Br J Surg. 2023;110(5):533–5.

How to contribute?

If you would like to join OHDSI Denmark, please fill out this form. If you would like to join the OHDSI MS Teams environment, please register at link and link (Please select “Europe” under the chapters section).