Standardised and Reproducible Phenotyping Using Distributed Analytics and Tools in the Data Analysis and Real World Interrogation Network (DARWIN EU). Dernie, F., Corby, G., Robinson, A., Bezer, J., Mercade-Besora, N., Griffier, R., Verdy, G., Leis, A., Ramirez-Anguita, J. M., Mayer, M. A., Brash, J. T., Seager, S., Parry, R., Jodicke, A., Duarte-Salles, T., Rijnbeek, P. R., Verhamme, K., Pacurariu, A., Morales, D., Pinheiro, L., Prieto-Alhambra, D., & Prats-Uribe, A. Pharmacoepidemiol Drug Saf, 33(11):e70042, 2024. 1099-1557 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 Orcid: 0000-0002-0063-8069 Pinheiro, Luis Orcid: 0000-0002-6253-7175 Prieto-Alhambra, Daniel Orcid: 0000-0002-3950-6346 Prats-Uribe, Albert NIHR Oxford Biomedical Research Centre/ European Medicines Agency/ Journal Article Research Support, Non-U.S. Gov't England 2024/11/13 Pharmacoepidemiol Drug Saf. 2024 Nov;33(11):e70042. doi: 10.1002/pds.70042.doi abstract bibtex 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.
@article{RN297,
author = {Dernie, F. and Corby, G. and Robinson, A. and Bezer, J. and Mercade-Besora, N. and Griffier, R. and Verdy, G. and Leis, A. and Ramirez-Anguita, J. M. and Mayer, M. A. and Brash, J. T. and Seager, S. and Parry, R. and Jodicke, A. and Duarte-Salles, T. and Rijnbeek, P. R. and Verhamme, K. and Pacurariu, A. and Morales, D. and Pinheiro, L. and Prieto-Alhambra, D. and Prats-Uribe, A.},
title = {Standardised and Reproducible Phenotyping Using Distributed Analytics and Tools in the Data Analysis and Real World Interrogation Network (DARWIN EU)},
journal = {Pharmacoepidemiol Drug Saf},
volume = {33},
number = {11},
pages = {e70042},
note = {1099-1557 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 Orcid: 0000-0002-0063-8069 Pinheiro, Luis Orcid: 0000-0002-6253-7175 Prieto-Alhambra, Daniel Orcid: 0000-0002-3950-6346 Prats-Uribe, Albert NIHR Oxford Biomedical Research Centre/ European Medicines Agency/ Journal Article Research Support, Non-U.S. Gov't England 2024/11/13 Pharmacoepidemiol Drug Saf. 2024 Nov;33(11):e70042. 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.},
keywords = {Humans
*Phenotype
*Pancreatic Neoplasms/epidemiology/diagnosis
*Lupus Erythematosus, Systemic/epidemiology/diagnosis
Reproducibility of Results
*Databases, Factual/statistics & numerical data/standards
Data Analysis
Observational Studies as Topic/methods
pancreatic cancer
phenotyping
systemic lupus erythematosus},
ISSN = {1053-8569},
DOI = {10.1002/pds.70042},
year = {2024},
type = {Journal Article}
}
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{"_id":"kEDmnZYtZp2dGJuRQ","bibbaseid":"dernie-corby-robinson-bezer-mercadebesora-griffier-verdy-leis-etal-standardisedandreproduciblephenotypingusingdistributedanalyticsandtoolsinthedataanalysisandrealworldinterrogationnetworkdarwineu-2024","author_short":["Dernie, F.","Corby, G.","Robinson, A.","Bezer, J.","Mercade-Besora, N.","Griffier, R.","Verdy, G.","Leis, A.","Ramirez-Anguita, J. M.","Mayer, M. A.","Brash, J. T.","Seager, S.","Parry, R.","Jodicke, A.","Duarte-Salles, T.","Rijnbeek, P. R.","Verhamme, K.","Pacurariu, A.","Morales, D.","Pinheiro, L.","Prieto-Alhambra, D.","Prats-Uribe, A."],"bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Dernie"],"firstnames":["F."],"suffixes":[]},{"propositions":[],"lastnames":["Corby"],"firstnames":["G."],"suffixes":[]},{"propositions":[],"lastnames":["Robinson"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Bezer"],"firstnames":["J."],"suffixes":[]},{"propositions":[],"lastnames":["Mercade-Besora"],"firstnames":["N."],"suffixes":[]},{"propositions":[],"lastnames":["Griffier"],"firstnames":["R."],"suffixes":[]},{"propositions":[],"lastnames":["Verdy"],"firstnames":["G."],"suffixes":[]},{"propositions":[],"lastnames":["Leis"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Ramirez-Anguita"],"firstnames":["J.","M."],"suffixes":[]},{"propositions":[],"lastnames":["Mayer"],"firstnames":["M.","A."],"suffixes":[]},{"propositions":[],"lastnames":["Brash"],"firstnames":["J.","T."],"suffixes":[]},{"propositions":[],"lastnames":["Seager"],"firstnames":["S."],"suffixes":[]},{"propositions":[],"lastnames":["Parry"],"firstnames":["R."],"suffixes":[]},{"propositions":[],"lastnames":["Jodicke"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Duarte-Salles"],"firstnames":["T."],"suffixes":[]},{"propositions":[],"lastnames":["Rijnbeek"],"firstnames":["P.","R."],"suffixes":[]},{"propositions":[],"lastnames":["Verhamme"],"firstnames":["K."],"suffixes":[]},{"propositions":[],"lastnames":["Pacurariu"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Morales"],"firstnames":["D."],"suffixes":[]},{"propositions":[],"lastnames":["Pinheiro"],"firstnames":["L."],"suffixes":[]},{"propositions":[],"lastnames":["Prieto-Alhambra"],"firstnames":["D."],"suffixes":[]},{"propositions":[],"lastnames":["Prats-Uribe"],"firstnames":["A."],"suffixes":[]}],"title":"Standardised and Reproducible Phenotyping Using Distributed Analytics and Tools in the Data Analysis and Real World Interrogation Network (DARWIN EU)","journal":"Pharmacoepidemiol Drug Saf","volume":"33","number":"11","pages":"e70042","note":"1099-1557 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 Orcid: 0000-0002-0063-8069 Pinheiro, Luis Orcid: 0000-0002-6253-7175 Prieto-Alhambra, Daniel Orcid: 0000-0002-3950-6346 Prats-Uribe, Albert NIHR Oxford Biomedical Research Centre/ European Medicines Agency/ Journal Article Research Support, Non-U.S. Gov't England 2024/11/13 Pharmacoepidemiol Drug Saf. 2024 Nov;33(11):e70042. 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.","keywords":"Humans *Phenotype *Pancreatic Neoplasms/epidemiology/diagnosis *Lupus Erythematosus, Systemic/epidemiology/diagnosis Reproducibility of Results *Databases, Factual/statistics & numerical data/standards Data Analysis Observational Studies as Topic/methods pancreatic cancer phenotyping systemic lupus erythematosus","issn":"1053-8569","doi":"10.1002/pds.70042","year":"2024","bibtex":"@article{RN297,\r\n author = {Dernie, F. and Corby, G. and Robinson, A. and Bezer, J. and Mercade-Besora, N. and Griffier, R. and Verdy, G. and Leis, A. and Ramirez-Anguita, J. M. and Mayer, M. A. and Brash, J. T. and Seager, S. and Parry, R. and Jodicke, A. and Duarte-Salles, T. and Rijnbeek, P. R. and Verhamme, K. and Pacurariu, A. and Morales, D. and Pinheiro, L. and Prieto-Alhambra, D. and Prats-Uribe, A.},\r\n title = {Standardised and Reproducible Phenotyping Using Distributed Analytics and Tools in the Data Analysis and Real World Interrogation Network (DARWIN EU)},\r\n journal = {Pharmacoepidemiol Drug Saf},\r\n volume = {33},\r\n number = {11},\r\n pages = {e70042},\r\n note = {1099-1557 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 Orcid: 0000-0002-0063-8069 Pinheiro, Luis Orcid: 0000-0002-6253-7175 Prieto-Alhambra, Daniel Orcid: 0000-0002-3950-6346 Prats-Uribe, Albert NIHR Oxford Biomedical Research Centre/ European Medicines Agency/ Journal Article Research Support, Non-U.S. Gov't England 2024/11/13 Pharmacoepidemiol Drug Saf. 2024 Nov;33(11):e70042. doi: 10.1002/pds.70042.},\r\n 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.},\r\n keywords = {Humans\r\n*Phenotype\r\n*Pancreatic Neoplasms/epidemiology/diagnosis\r\n*Lupus Erythematosus, Systemic/epidemiology/diagnosis\r\nReproducibility of Results\r\n*Databases, Factual/statistics & numerical data/standards\r\nData Analysis\r\nObservational Studies as Topic/methods\r\npancreatic cancer\r\nphenotyping\r\nsystemic lupus erythematosus},\r\n ISSN = {1053-8569},\r\n DOI = {10.1002/pds.70042},\r\n year = {2024},\r\n type = {Journal Article}\r\n}\r\n\r\n","author_short":["Dernie, F.","Corby, G.","Robinson, A.","Bezer, J.","Mercade-Besora, N.","Griffier, R.","Verdy, G.","Leis, A.","Ramirez-Anguita, J. M.","Mayer, M. A.","Brash, J. T.","Seager, S.","Parry, R.","Jodicke, A.","Duarte-Salles, T.","Rijnbeek, P. 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