Evidence map›Paper›PMID 36199590›Full record

ReviewERJ open research2022

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

Johannes A Kroes, Aruna T Bansal, Emmanuelle Berret, Nils Christian, Andreas Kremer, Anna Alloni, Matteo Gabetta, Chris Marshall, Scott Wagers, Ratko Djukanovic and 6 more

Abstract readReview
In one paragraph

Review in ERJ open research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Centralized and Federated Models for the Analysis of Clinical Data.Annual review of biomedical data science · 2024
    Review
  10. Article
  11. Article
  12. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Johannes A KroesDept of Clinical Pharmacy and Pharmacology, Medical Centre Leeuwarden, Leeuwarden, The Netherlands.ORCID https://orcid.org/0000-0002-9418-4861
Aruna T BansalAcclarogen Ltd, Cambridge, UK.
Emmanuelle BerretEuropean Respiratory Society, Lausanne, Switzerland.
Nils ChristianITTM SA, Esch-sur-Alzette, Luxembourg.
Andreas KremerITTM SA, Esch-sur-Alzette, Luxembourg.
Anna AlloniBiomeris SRL, Pavia, Italy.ORCID https://orcid.org/0000-0003-4708-4822
Matteo GabettaBiomeris SRL, Pavia, Italy.
Chris MarshallMetaseq Ltd, Malvern, UK.
Scott WagersBIOSCI Consulting, Maasmechelen, Belgium.
Ratko DjukanovicNIHR Southampton Respiratory Biomedical Research Unit, Faculty of Medicine, University of Southampton, Southampton, UK.
Celeste PorsbjergDept of Pulmonology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark.
Dominique HamerlijnckPatient Advisory Group, European Lung Foundation, Sheffield, UK.
Olivia FultonPatient Advisory Group, European Lung Foundation, Sheffield, UK.ORCID https://orcid.org/0000-0001-7358-0219
Anneke Ten BrinkeDept of Pulmonology, Medical Centre Leeuwarden, Leeuwarden, The Netherlands.
Elisabeth H BelAmsterdam Medical Centers, Location AMC, University of Amsterdam, Amsterdam, The Netherlands.
Jacob K SontDept of Biomedical Data Sciences, Medical Decision Making, Leiden University Medical Center, Leiden, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID36199590
PMCPMC9530887

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.