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. 2024 Nov;33(11):e70042.
doi: 10.1002/pds.70042.

Standardised and Reproducible Phenotyping Using Distributed Analytics and Tools in the Data Analysis and Real World Interrogation Network (DARWIN EU)

Francesco Dernie  1   2 , George Corby  1   2 , Abigail Robinson  1   2 , James Bezer  1   2 , Nuria Mercade-Besora  2 , Romain Griffier  3 , Guillaume Verdy  3 , Angela Leis  4 , Juan Manuel Ramirez-Anguita  5 , Miguel A Mayer  4   5 , James T Brash  6 , Sarah Seager  6 , Rowan Parry  7 , Annika Jodicke  2 , Talita Duarte-Salles  7   8 , Peter R Rijnbeek  7 , Katia Verhamme  7 , Alexandra Pacurariu  9 , Daniel Morales  9   10 , Luis Pinheiro  9 , Daniel Prieto-Alhambra  2   7 , Albert Prats-Uribe  2
Affiliations

Standardised and Reproducible Phenotyping Using Distributed Analytics and Tools in the Data Analysis and Real World Interrogation Network (DARWIN EU)

Francesco Dernie et al. Pharmacoepidemiol Drug Saf. 2024 Nov.

Abstract

Purpose: The generation of representative disease phenotypes is important for ensuring the reliability of the findings of observational studies. The aim of this manuscript is to outline a reproducible framework for reliable and traceable phenotype generation based on real world data for use in the Data Analysis and Real-World Interrogation Network (DARWIN EU). We illustrate the use of this framework by generating phenotypes for two diseases: pancreatic cancer and systemic lupus erythematosus (SLE).

Methods: The phenotyping process involves a 14-steps process based on a standard operating procedure co-created by the DARWIN EU Coordination Centre in collaboration with the European Medicines Agency. A number of bespoke R packages were utilised to generate and review codelists for two phenotypes based on real world data mapped to the OMOP Common Data Model.

Results: Codelists were generated for both pancreatic cancer and SLE, and cohorts were generated in six OMOP-mapped databases. Diagnostic checks were performed, which showed these cohorts had broadly similar incidence and prevalence figures to previously published literature, despite significant inter-database variability. Co-occurrent symptoms, conditions, and medication use were in keeping with pre-specified clinical descriptions based on previous knowledge.

Conclusions: Our detailed phenotyping process makes use of bespoke tools and allows for comprehensive codelist generation and review, as well as large-scale exploration of the characteristics of the resulting cohorts. Wider use of structured and reproducible phenotyping methods will be important in ensuring the reliability of observational studies for regulatory purposes.

Keywords: pancreatic cancer; phenotyping; systemic lupus erythematosus.

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References

    1. G. Hripcsak and D. J. Albers, “High‐Fidelity Phenotyping: Richness and Freedom From Bias,” Journal of the American Medical Informatics Association 25, no. 3 (2017): 289–294.
    1. G. Hripcsak, J. D. Duke, N. H. Shah, et al., “Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers,” Studies in Health Technology and Informatics 216 (2015): 574–578.
    1. J. M. Overhage, P. B. Ryan, C. G. Reich, A. G. Hartzema, and P. E. Stang, “Validation of a Common Data Model for Active Safety Surveillance Research,” Journal of the American Medical Informatics Association 19, no. 1 (2012): 54–60.
    1. R. A. DeFronzo, E. Ferrannini, L. Groop, et al., “Type 2 Diabetes Mellitus,” Nature Reviews Disease Primers 1, no. 1 (2015): 15019.
    1. S. Lanes, J. S. Brown, K. Haynes, M. F. Pollack, and A. M. Walker, “Identifying Health Outcomes in Healthcare Databases,” Pharmacoepidemiology and Drug Safety 24, no. 10 (2015): 1009–1016.

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