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

• Published in: Pharmacoepidemiology and drug safety 2024-11, Vol.33 (11), p.e70042-n/a
• Main Authors: Dernie, Francesco, Corby, George, Robinson, Abigail, Bezer, James, Mercade‐Besora, Nuria, Griffier, Romain, Verdy, Guillaume, Leis, Angela, Ramirez‐Anguita, Juan Manuel, Mayer, Miguel A., Brash, James T., Seager, Sarah, Parry, Rowan, Jodicke, Annika, Duarte‐Salles, Talita, Rijnbeek, Peter R., Verhamme, Katia, Pacurariu, Alexandra, Morales, Daniel, Pinheiro, Luis, Prieto‐Alhambra, Daniel, Prats‐Uribe, Albert
• Format: Article
• Language: English
• Subjects: Data Analysis; Databases, Factual - standards; Databases, Factual - statistics & numerical data; Humans; Lupus Erythematosus, Systemic - diagnosis; Lupus Erythematosus, Systemic - epidemiology; Observational studies; Observational Studies as Topic - methods; Pancreatic cancer; Pancreatic Neoplasms - diagnosis; Pancreatic Neoplasms - epidemiology; Phenotype; Phenotypes; Phenotyping; Reproducibility of Results; Systemic lupus erythematosus
• ISSN: 1053-8569; 1099-1557; 1099-1557
• DOI: 10.1002/pds.70042

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.