ReviewERJ open research2022
Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future.
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.
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.
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.
Who cites it
12 citing papers in PubMed.
- Managing multimorbidity in severe asthma: comparison of resources and perspectives between severe asthma specialists and general respiratory physicians across Europe.ERJ open research · 2026Article
- Multimorbidity phenotypes and associated characteristics in severe asthma: an observational study of European severe asthma registries.The Lancet regional health. Europe · 2026Article
- Federated analytics for non-communicable disease surveillance in the European health data space: a scoping review and conceptual framework.Frontiers in public health · 2026Article
- A maturity model framework for federated networks of trusted research environments.Frontiers in digital health · 2026Article
- Economic impact of steroid-induced comorbidities in severe asthma: a SHARP CRC simulation.ERJ open research · 2025Article
- Article
- Data Interoperability in Context: The Importance of Open-Source Implementations When Choosing Open Standards.Journal of medical Internet research · 2025Article
- Real-world datasets for the International Registry for Alzheimer's Disease and Other Dementias (InRAD) and other registries: An international consensus.The journal of prevention of Alzheimer's disease · 2025Article
- Centralized and Federated Models for the Analysis of Clinical Data.Annual review of biomedical data science · 2024Review
- Article
- Evaluation of real-world mepolizumab use in severe asthma across Europe: the SHARP experience with privacy-preserving federated analysis.ERJ open research · 2023Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
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
What OpenQuestion holds
Registered trials
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.