Evidence map›Paper›PMID 36208161›Full record

ArticleEuropean heart journal2022

CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research.

Dipak Kotecha, Folkert W Asselbergs, Stephan Achenbach, Stefan D Anker, Dan Atar, Colin Baigent, Amitava Banerjee, Birgit Beger, Gunnar Brobert, Barbara Casadei and 35 more

Open access · hybridAbstract read
In one paragraph

Article in European heart journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
9.4field-weighted citation impact, top 2% of its field
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

13 citing papers in PubMed, 35 citations in OpenAlex.

  1. Article
  2. Article
  3. Observational
  4. Article
  5. Review
  6. Embedding routine health care data in clinical trials: with great power comes great responsibility.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2024
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Machine learning and disease prediction in obstetrics.Current research in physiology · 2023
    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

45 authors at 20 institutions in 12 countries.

Dipak KotechaInstitute of Cardiovascular Sciences, University of Birmingham, Medical School, Birmingham, UK.ORCID 0000-0002-2570-9812
Folkert W AsselbergsDepartment of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, University of Utrecht, Utrecht, Netherlands.
Stephan AchenbachFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Stefan D AnkerDepartment of Cardiology and Berlin Institute of Health Centre for Regenerative Therapies, German Centre for Cardiovascular Research (DZHK) partner site Berlin; Charité Universitätsmedizin Berlin, Germany.
Dan AtarDepartment of Cardiology, Oslo University Hospital, Ulleval, Oslo, Norway.
Colin BaigentMRC Population Health Research Unit, Nuffield Department of Population Health, Oxford, UK.
Amitava BanerjeeHealth Data Research UK and Institute of Health Informatics, University College London, London, UK.
Birgit BegerEuropean Heart Network, Brussels, Belgium.
Gunnar BrobertBayer AB, Stockholm, Sweden.
Barbara CasadeiDivision of Cardiovascular Medicine, John Radcliffe Hospital, University of Oxford NIHR Oxford Biomedical Research Centre, Oxford, UK.
Cinzia CeccarelliEuropean Society of Cardiology, Sophia Antipolis, France.
Martin R CowieRoyal Brompton Hospital, Division of Guy's St Thomas' NHS Foundation Trust, London, UK.
Filippo CreaDepartment of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy.
Maureen CroninVifor Pharma, Glattbrugg, Switzerland and Ava AG, Zurich, Switzerland.
Spiros DenaxasHealth Data Research UK and Institute of Health Informatics, University College London, London, UK.
Andrea DerixBayer AG, Leverkusen, Germany.
Donna FitzsimonsSchool of Nursing and Midwifery, Queen's University Belfast, Northern Ireland.
Martin FredrikssonLate Clinical Development, Cardiovascular, Renal and Metabolism (CVRM), Biopharmaceuticals RD, AstraZeneca, Gothenburg, Sweden.
Chris P GaleLeeds Institute of Cardiovascular and Metabolic Medicine and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK.
Georgios V GkoutosUniversity Hospitals Birmingham NHS Foundation Trust and Health Data Research UK Midlands, Birmingham, UK.
Wim GoettschNational Health Care Institute (ZIN), Diemen, Netherlands.
Harry HemingwayHealth Data Research UK and Institute of Health Informatics, University College London, London, UK.
Martin IngvarDepartment of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden.
Adrian JonasData and Analytics Group, National Institute for Health and Care Excellence, London, UK.
Robert KazmierskiOffice of Cardiovascular Devices, US Food and Drug Administration, Silver Spring, MD, USA.
Susanne LøgstrupEuropean Heart Network, Brussels, Belgium.
R Thomas LumbersHealth Data Research UK and Institute of Health Informatics, University College London, London, UK.
Thomas F LüscherCentre for Molecular Cardiology, University of Zurich, Zurich, Switzerland.
Paul McGreavyEuropean Society of Cardiology Patient Forum, European Society of Cardiology, Brussels, Belgium.
Ileana L PiñaCentral Michigan University College of Medicine, Midlands, MI, USA.
Lothar RoessigBayer AG, Leverkusen, Germany.
Carl SteinbeisserBayer AG, Leverkusen, Germany.
Mats SundgrenData Science AI, Biopharmaceuticals RD, AstraZeneca, Gothenburg, Sweden.
Benoît TylCentre for Therapeutic Innovation, Cardiovascular and Metabolic Disease, Institut de Recherches Internationales Servier, Suresnes, France.
Ghislaine van ThielJulius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands.
Kees van BochoveThe Hyve, Utrecht, Netherlands.
Panos E VardasHygeia, Mitera, Hospitals Hellenic Health Group, Athens, Greece.
Tiago VillanuevaThe BMJ, London, UK.
Marilena VranaEuropean Heart Network, Brussels, Belgium.
Wim WeberThe BMJ, London, UK.
Franz WeidingerRudolfstiftung Hospital, Vienna, Austria.
Stephan WindeckerDepartment of Cardiology, Inselspital, University Hospital Bern, Bern, Switzerland.
Angela WoodCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Diederick E GrobbeeDepartment of Epidemiology, University Medical Centre Utrecht, Division Julius Centrum, Utrecht, Netherlands.
Innovative Medicines Initiative BigData@Heart Consortium, European Society of Cardiology, CODE-EHR international consensus group
European Heart Network · BEBayer (Germany) · DEUtrecht University · NLAstraZeneca (Sweden) · SEUnited States Food and Drug Administration · USUniversity Hospitals Birmingham NHS Foundation Trust · GBBarts Health NHS Trust · GBBritish Heart Foundation · GBEuropean Society of Cardiology · FRFriedrich-Alexander-Universität Erlangen-Nürnberg · DEGerman Centre for Cardiovascular Research · DEHealth Data Research UK · GBJohn Radcliffe Hospital · GBKarolinska University Hospital · SEKing's College London · GBMitera Hospital · GRNational Institute for Health and Care Excellence · GBOslo University Hospital · NOServier (France) · FRThe Hyve (Netherlands) · NL

Funding

British Heart Foundation CH/12/2/29428British Heart Foundation RG/13/13/30194British Heart Foundation RG/18/13/33946Medical Research Council MC_PC_20030Medical Research Council MC_PC_20051Medical Research Council MC_PC_20059Medical Research Council MR/S003754/1
6 · The paper itself

Abstract

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes.

Indexed as

COVID-19Electronic Health RecordsDelivery of Health CareElectronicsHumansPandemics

Identifiers

PMID36208161
PMCPMC9452067
OpenAlexW4303685093

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.