TY - JOUR

T1 - Blueprint for harmonising unstandardised disease registries to allow federated data analysis

T2 - prepare for the future

AU - Kroes, Johannes A.

AU - Bansal, Aruna T.

AU - Berret, Emmanuelle

AU - Christian, Nils

AU - Kremer, Andreas

AU - Alloni, Anna

AU - Gabetta, Matteo

AU - Marshall, Chris

AU - Wagers, Scott

AU - Djukanovic, Ratko

AU - Porsbjerg, Celeste

AU - Hamerlijnck, Dominique

AU - Fulton, Olivia

AU - ten Brinke, Anneke

AU - Bel, Elisabeth H.

AU - Sont, Jacob K.

N1 - Publisher Copyright: © The authors 2022.

PY - 2022

Y1 - 2022

N2 - 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.

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.

U2 - 10.1183/23120541.00168-2022

DO - 10.1183/23120541.00168-2022

M3 - Journal article

C2 - 36199590

AN - SCOPUS:85139418373

VL - 8

JO - ERJ Open Research

JF - ERJ Open Research

SN - 2312-0541

IS - 4

M1 - 00168-2022

ER -