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Review
. 2022 Oct 4;8(4):00168-2022.
doi: 10.1183/23120541.00168-2022. eCollection 2022 Oct.

Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future

Johannes A Kroes  1 , Aruna T Bansal  2 , Emmanuelle Berret  3 , Nils Christian  4 , Andreas Kremer  4 , Anna Alloni  5 , Matteo Gabetta  5 , Chris Marshall  6 , Scott Wagers  7 , Ratko Djukanovic  8 , Celeste Porsbjerg  9 , Dominique Hamerlijnck  10 , Olivia Fulton  10 , Anneke Ten Brinke  11 , Elisabeth H Bel  12 , Jacob K Sont  13
Affiliations
Review

Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future

Johannes A Kroes et al. ERJ Open Res. .

Abstract

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.

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Conflict of interest statement

Conflict of interest: J.A. Kroes reports grants from AstraZeneca BV outside the submitted work. A.T. Bansal has nothing to disclose. E. Berret is an employee of the European Respiratory Society. N. Christian is an employee of ITTM SA. A. Kremer is an employee of ITTM SA. A. Alloni is an employee of ITTM SA. M. Gabetta is an employee of Biomeris SRL. C. Marshall has nothing to disclose. S. Wagers reports personal fees from King's College Hospital NHS Foundation Trust, Academic Medical Research, AMC Medical Research BV, Asthma UK, Athens Medical School, Boehringer Ingelheim International GmbH, CHU de Toulouse, CIRO, DS Biologicals Ltd, École Polytechnique Fédérale de Lausanne, European Respiratory Society, FISEVI, Fluidic Analytics Ltd, Fraunhofer IGB, Fraunhofer ITEM, GlaxoSmithKline R&D Ltd, Holland & Knight, Karolinska Institutet Fakturor, KU Leuven, Longfonds, National Heart and Lung Institute, Novartis Pharma AG, Owlstone Medical Ltd, PExA AB, UCB Biopharma SPRL, Umeå University, University Hospital Southampton NHS Foundation Trust, Università Campus Bio-Medico di Roma, Universita Cattolica del Sacro Cuore, Universität Ulm, University of Bern, University of Edinburgh, University of Hull, University of Leicester, University of Loughborough, University of Manchester, University of Nottingham, Vlaams Brabant, Dienst Europa, Imperial College London, Boehringer Ingelheim, Breathomix, Gossamer Bio, AstraZeneca, CIBER, OncoRadiomics, University of Leiden, University of Wurzburg, Chiesi Pharmaceutical, University of Liege, Teva Pharmaceuticals, Sanofi, Pulmonary Fibrosis Foundation and Three Lakes Foundation, outside the submitted work. R. Djukanovic has received a grant from Novartis for a CI-led project that the funder agreed to support without any restrictions or influence on its contents, analysis or publication; has received consultancy fees from Teva Pharmaceuticals, Sanofi, Boehringer, Novartis and Synairgen; has received grants paid to his institution from the IMI-funded EU project U-BIOPRED, the MERC-funded RASP-UK project, the EME/MRC-funded BEAT Severe Asthma project and NIHR BRC; payment for lectures on the mechanisms of action of Xolair from Novartis and mechanisms of asthma from Teva; and has stock in a University of Southampton company, Synairgen. C. Porsbjerg has received grants and consulting fees paid to her institution, and personal honoraria from AstraZeneca, GlaxoSmithKline, Novartis, Teva, Sanofi, Chiesi and ALK. D. Hamerlijnck has nothing to disclose. O. Fulton has nothing to disclose. A. ten Brinke has received grants paid to her institution from AstraZeneca, GlaxoSmithKline and Teva; and fees paid to her institution for advisory boards and lectures from AstraZeneca, GlaxoSmithKline, Novartis, Teva and Sanofi/Genzyme, all outside the submitted work. E.H. Bel has received grants paid to her institution from GlaxoSmithKline and Teva; and consulting fees from AstraZeneca UK Ltd, GlaxoSmithKline Services UnLtd, Sterna Biologicals, Chiesi Pharmaceuticals, Sanofi/Regeneron and Teva Pharmaceuticals. J.K. Sont has received a grant from GlaxoSmithKline, outside the submitted work.

Figures

FIGURE 1
FIGURE 1
Architecture of the federated analysis platform. Field names of the different national registries are mapped to concepts in the Observational Health Data Sciences and Informatics (OHDSI)/Observational Medical Outcomes Partnership (OMOP) Common Data Model. An Extract, Transform, Load (ETL) procedure is created to automate the mapping from the local database into a unified format; the harmonised data are made available for local analysis using the OHDSI toolset or R code; an identical analysis is run on each registry; the results are combined using federated analysis tools. SHARP: Severe Heterogeneous Asthma Research collaboration, Patient-centred.
FIGURE 2
FIGURE 2
Schematic summary of steps to be taken for a successful harmonisation process of local nonstandardised disease registries to the Observational Health Data Sciences and Informatics (OHDSI)/Observational Medical Outcomes Partnership (OMOP) Common Data Model for federated analyses. SME: small and medium-sized enterprise; IT: information technology; FAP: federated analysis platform.
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