RT Journal Article
SR Electronic
T1 Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future
JF ERJ Open Research
JO erjor
FD European Respiratory Society
SP 00168-2022
DO 10.1183/23120541.00168-2022
VO 8
IS 4
A1 Johannes A. Kroes
A1 Aruna T. Bansal
A1 Emmanuelle Berret
A1 Nils Christian
A1 Andreas Kremer
A1 Anna Alloni
A1 Matteo Gabetta
A1 Chris Marshall
A1 Scott Wagers
A1 Ratko Djukanovic
A1 Celeste Porsbjerg
A1 Dominique Hamerlijnck
A1 Olivia Fulton
A1 Anneke ten Brinke
A1 Elisabeth H. Bel
A1 Jacob K. Sont
YR 2022
UL http://openres.ersjournals.com/content/8/4/00168-2022.abstract
AB 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.Harmonising real-world patient data from diverse registries to allow federated analyses is a complex process that requires thorough preparation but is above all a valuable investment, especially in view of emerging applications of artificial intelligence https://bit.ly/3NEKKnV