Cargando…
Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future
Real-world evidence from multinational disease registries is becoming increasingly important not only for confirming the results of randomised controlled trials, but also for identifying phenotypes, monitoring disease progression, predicting response to new drugs and early detection of rare side-eff...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
European Respiratory Society
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530887/ https://www.ncbi.nlm.nih.gov/pubmed/36199590 http://dx.doi.org/10.1183/23120541.00168-2022 |
_version_ | 1784801781647147008 |
---|---|
author | Kroes, Johannes A. Bansal, Aruna T. Berret, Emmanuelle Christian, Nils Kremer, Andreas Alloni, Anna Gabetta, Matteo Marshall, Chris Wagers, Scott Djukanovic, Ratko Porsbjerg, Celeste Hamerlijnck, Dominique Fulton, Olivia ten Brinke, Anneke Bel, Elisabeth H. Sont, Jacob K. |
author_facet | Kroes, Johannes A. Bansal, Aruna T. Berret, Emmanuelle Christian, Nils Kremer, Andreas Alloni, Anna Gabetta, Matteo Marshall, Chris Wagers, Scott Djukanovic, Ratko Porsbjerg, Celeste Hamerlijnck, Dominique Fulton, Olivia ten Brinke, Anneke Bel, Elisabeth H. Sont, Jacob K. |
author_sort | Kroes, Johannes A. |
collection | PubMed |
description | Real-world evidence from multinational disease registries is becoming increasingly important not only for confirming the results of randomised controlled trials, but also for identifying phenotypes, monitoring disease progression, predicting response to new drugs and early detection of rare side-effects. With new open-access technologies, it has become feasible to harmonise patient data from different disease registries and use it for data analysis without compromising privacy rules. Here, we provide a blueprint for how a clinical research collaboration can successfully use real-world data from existing disease registries to perform federated analyses. We describe how the European severe asthma clinical research collaboration SHARP (Severe Heterogeneous Asthma Research collaboration, Patient-centred) fulfilled the harmonisation process from nonstandardised clinical registry data to the Observational Medical Outcomes Partnership Common Data Model and built a strong network of collaborators from multiple disciplines and countries. The blueprint covers organisational, financial, conceptual, technical, analytical and research aspects, and discusses both the challenges and the lessons learned. All in all, setting up a federated data network is a complex process that requires thorough preparation, but above all, it is a worthwhile investment for all clinical research collaborations, especially in view of the emerging applications of artificial intelligence and federated learning. |
format | Online Article Text |
id | pubmed-9530887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | European Respiratory Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-95308872022-10-04 Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future Kroes, Johannes A. Bansal, Aruna T. Berret, Emmanuelle Christian, Nils Kremer, Andreas Alloni, Anna Gabetta, Matteo Marshall, Chris Wagers, Scott Djukanovic, Ratko Porsbjerg, Celeste Hamerlijnck, Dominique Fulton, Olivia ten Brinke, Anneke Bel, Elisabeth H. Sont, Jacob K. ERJ Open Res Reviews Real-world evidence from multinational disease registries is becoming increasingly important not only for confirming the results of randomised controlled trials, but also for identifying phenotypes, monitoring disease progression, predicting response to new drugs and early detection of rare side-effects. With new open-access technologies, it has become feasible to harmonise patient data from different disease registries and use it for data analysis without compromising privacy rules. Here, we provide a blueprint for how a clinical research collaboration can successfully use real-world data from existing disease registries to perform federated analyses. We describe how the European severe asthma clinical research collaboration SHARP (Severe Heterogeneous Asthma Research collaboration, Patient-centred) fulfilled the harmonisation process from nonstandardised clinical registry data to the Observational Medical Outcomes Partnership Common Data Model and built a strong network of collaborators from multiple disciplines and countries. The blueprint covers organisational, financial, conceptual, technical, analytical and research aspects, and discusses both the challenges and the lessons learned. All in all, setting up a federated data network is a complex process that requires thorough preparation, but above all, it is a worthwhile investment for all clinical research collaborations, especially in view of the emerging applications of artificial intelligence and federated learning. European Respiratory Society 2022-10-04 /pmc/articles/PMC9530887/ /pubmed/36199590 http://dx.doi.org/10.1183/23120541.00168-2022 Text en Copyright ©The authors 2022 https://creativecommons.org/licenses/by-nc/4.0/This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions@ersnet.org (mailto:permissions@ersnet.org) |
spellingShingle | Reviews Kroes, Johannes A. Bansal, Aruna T. Berret, Emmanuelle Christian, Nils Kremer, Andreas Alloni, Anna Gabetta, Matteo Marshall, Chris Wagers, Scott Djukanovic, Ratko Porsbjerg, Celeste Hamerlijnck, Dominique Fulton, Olivia ten Brinke, Anneke Bel, Elisabeth H. Sont, Jacob K. Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future |
title | Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future |
title_full | Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future |
title_fullStr | Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future |
title_full_unstemmed | Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future |
title_short | Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future |
title_sort | blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future |
topic | Reviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530887/ https://www.ncbi.nlm.nih.gov/pubmed/36199590 http://dx.doi.org/10.1183/23120541.00168-2022 |
work_keys_str_mv | AT kroesjohannesa blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT bansalarunat blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT berretemmanuelle blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT christiannils blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT kremerandreas blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT allonianna blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT gabettamatteo blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT marshallchris blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT wagersscott blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT djukanovicratko blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT porsbjergceleste blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT hamerlijnckdominique blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT fultonolivia blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT tenbrinkeanneke blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT belelisabethh blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture AT sontjacobk blueprintforharmonisingunstandardiseddiseaseregistriestoallowfederateddataanalysisprepareforthefuture |