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  3. CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research
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Research Methods & Reporting

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

BMJ 2022; 378 doi: https://doi.org/10.1136/bmj-2021-069048 (Published 29 August 2022) Cite this as: BMJ 2022;378:e069048
  1. Dipak Kotecha, professor of cardiology13,
  2. Folkert W Asselbergs, professor of precision medicine35,
  3. Stephan Achenbach, professor of cardiology6,
  4. Stefan D Anker, professor of tissue homeostasis in cardiology and metabolism7,
  5. Dan Atar, professor of cardiology8 9,
  6. Colin Baigent, professor of epidemiology10 11,
  7. Amitava Banerjee, professor of clinical data science5 12,
  8. Birgit Beger, patient advocacy13,
  9. Gunnar Brobert, epidemiologist14,
  10. Barbara Casadei, professor of cardiovascular medicine15,
  11. Cinzia Ceccarelli, project manager16,
  12. Martin R Cowie, professor of cardiology1718,
  13. Filippo Crea, editor19 20,
  14. Maureen Cronin, industry representative21,
  15. Spiros Denaxas, professor of biomedical informatics5 22 23,
  16. Andrea Derix, industry representative24,
  17. Donna Fitzsimons, professor of nursing25,
  18. Martin Fredriksson, industry representative26,
  19. Chris P Gale, professor of cardiovascular medicine2729,
  20. Georgios V Gkoutos, chair of clinical bioinformatics2 30,
  21. Wim Goettsch, associate professor of health technology assessment31 32,
  22. Harry Hemingway, professor of clinical epidemiology5,
  23. Martin Ingvar, professor of neurophysiology and integrative medicine33 34,
  24. Adrian Jonas, strategic adviser35,
  25. Robert Kazmierski, US FDA lead reviewer36,
  26. Susanne Løgstrup, patient advocacy13,
  27. R Thomas Lumbers, principal research fellow5 37,
  28. Thomas F Lüscher, professor of cardiology3840,
  29. Paul McGreavy, patient advocacy41,
  30. Ileana L Piña, professor of cardiology and US FDA medical officer42 43,
  31. Lothar Roessig, industry representative24,
  32. Carl Steinbeisser, project manager24 44,
  33. Mats Sundgren, industry representative45,
  34. Benoît Tyl, industry representative46,
  35. Ghislaine van Thiel, associate professor of medical ethics47,
  36. Kees van Bochove, industry representative48,
  37. Panos E Vardas, professor of cardiology49 50,
  38. Tiago Villanueva, editor51,
  39. Marilena Vrana, patient advocacy13,
  40. Wim Weber, editor51,
  41. Franz Weidinger, professor of cardiology52,
  42. Stephan Windecker, professor of medicine53,
  43. Angela Wood, professor of health data science54,
  44. Diederick E Grobbee, professor of clinical epidemiology55
  45. on behalf of the Innovative Medicines Initiative BigData@Heart Consortium, European Society of Cardiology, CODE-EHR international consensus group
  1. 1Institute of Cardiovascular Sciences, University of Birmingham, Medical School, Birmingham, UK
  2. 2University Hospitals Birmingham NHS Foundation Trust and Health Data Research UK Midlands, Birmingham, UK
  3. 3Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, University of Utrecht, Utrecht, Netherlands
  4. 4Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
  5. 5Health Data Research UK and Institute of Health Informatics, University College London, London, UK
  6. 6Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
  7. 7Department of Cardiology and Berlin Institute of Health Centre for Regenerative Therapies, German Centre for Cardiovascular Research (DZHK) partner site Berlin; Charité Universitätsmedizin Berlin, Germany
  8. 8Department of Cardiology, Oslo University Hospital, Ulleval, Oslo, Norway
  9. 9University of Oslo, Institute of Clinical Medicine, Oslo, Norway
  10. 10MRC Population Health Research Unit, Nuffield Department of Population Health, Oxford, UK
  11. 11Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, UK
  12. 12University College London Hospitals NHS Trust, London, UK
  13. 13European Heart Network, Brussels, Belgium
  14. 14Bayer AB, Stockholm, Sweden
  15. 15Division of Cardiovascular Medicine, John Radcliffe Hospital, University of Oxford NIHR Oxford Biomedical Research Centre, Oxford, UK
  16. 16European Society of Cardiology, Sophia Antipolis, France
  17. 17Royal Brompton Hospital, Division of Guy’s St Thomas’ NHS Foundation Trust, London, UK
  18. 18School of Cardiovascular Medicine Sciences, King’s College London, London, UK
  19. 19Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
  20. 20European Heart Journal, Oxford University Press, University of Oxford, Oxford, UK
  21. 21Vifor Pharma, Glattbrugg, Switzerland and Ava AG, Zurich, Switzerland
  22. 22Alan Turing Institute, London, UK
  23. 23British Heart Foundation Data Science Centre, London, UK
  24. 24Bayer AG, Leverkusen, Germany
  25. 25School of Nursing and Midwifery, Queen’s University Belfast, Northern Ireland
  26. 26Late Clinical Development, Cardiovascular, Renal and Metabolism (CVRM), Biopharmaceuticals RD, AstraZeneca, Gothenburg, Sweden
  27. 27Leeds Institute of Cardiovascular and Metabolic Medicine and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
  28. 28Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
  29. 29Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
  30. 30College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
  31. 31National Health Care Institute (ZIN), Diemen, Netherlands
  32. 32Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands
  33. 33Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
  34. 34Department of Neuroradiology, Karolinska University Hospital Stockholm, Stockholm, Sweden
  35. 35Data and Analytics Group, National Institute for Health and Care Excellence, London, UK
  36. 36Office of Cardiovascular Devices, US Food and Drug Administration, Silver Spring, MD, USA
  37. 37Barts Health NHS Trust and University College London Hospitals NHS Trust
  38. 38Centre for Molecular Cardiology, University of Zurich, Zurich, Switzerland
  39. 39Research, Education & Development, Royal Brompton and Harefield Hospitals, London, UK
  40. 40Faculty of Medicine, Imperial College London, London, UK
  41. 41European Society of Cardiology Patient Forum, European Society of Cardiology, Brussels, Belgium
  42. 42Central Michigan University College of Medicine, Midlands, MI, USA
  43. 43Centre for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
  44. 44Steinbeisser Project Management, Munich, Germany
  45. 45Data Science AI, Biopharmaceuticals RD, AstraZeneca, Gothenburg, Sweden
  46. 46Centre for Therapeutic Innovation, Cardiovascular and Metabolic Disease, Institut de Recherches Internationales Servier, Suresnes, France
  47. 47Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
  48. 48The Hyve, Utrecht, Netherlands
  49. 49Hygeia, Mitera, Hospitals Hellenic Health Group, Athens, Greece
  50. 50European Heart Agency, European Society of Cardiology, Brussels, Belgium
  51. 51The BMJ, London, UK
  52. 52Rudolfstiftung Hospital, Vienna, Austria
  53. 53Department of Cardiology, Inselspital, University Hospital Bern, Bern, Switzerland
  54. 54Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
  55. 55Department of Epidemiology, University Medical Centre Utrecht, Division Julius Centrum, Utrecht, Netherlands
  1. Correspondence: D Kotecha d.kotecha@bham.ac.uk
  • Accepted 21 June 2022

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.

In the context of ageing populations and increasing multimorbidity in all disease areas,123 large scale, real world data provide an opportunity to better understand the epidemiology of rare and common conditions, and to improve prevention strategies and treatment stratification.4 Tailored management for individual patients has become even more essential to constrain healthcare costs and provide patient centred care that can improve a patient’s quality of life and prognosis. Embedding controlled trials within the real world setting, either within registries or routine clinical practice, is now possible and could provide more generalisable results to the population at large.5

Health data science has undergone rapid development in the past decade, including the common adoption of electronic healthcare record (EHR) systems that condense clinical episodes into a set of coded, structured labels.6 However, concerns over quality, data privacy, transparency, and comparability of these systems have limited the use of the evidence generated with structured healthcare data. These issues have also restricted acceptance by regulators, reimbursement authorities, and guideline task forces. Despite the availability of numerous reporting standards, consensus has not been met on how to realise the Findable, Accessible, Interoperable, and Reusable (FAIR) principles7 in the context of structured healthcare data. Existing reporting checklists ask authors to indicate where in their paper particular design issues have been discussed. For example, STROBE (Strengthening the Reporting of Observational studies in Epidemiology) for observational studies,8 RECORD (REporting of studies Conducted using Observational Routinely-collected Data) for routinely collected health data,9 and CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) for artificial intelligence interventions.10 However, these checklists are often lengthy, no minimum standards are specified, and adherence does not relate to study quality or even the quality of transparency for that domain.11 Although checklists can benefit research quality, they are often used for box ticking to facilitate journal publication. In a study of radiology journals, only 15% (120/821) of surveyed authors used the reporting guideline when designing their study.12 With a proliferation of reporting checklists for every scenario, authors and readers of such reports are increasingly confused about the value of these checklists. As of 14 February 2022, 488 reporting checklists were registered with EQUATOR (Enhancing the QUAlity and Transparency Of health Research) and 111 were in development.

In the case of observational and randomised clinical research using EHRs and other structured data, the source of data, its manipulation, and underpinning governance are of critical importance to extrapolating results. Clarity is needed from a broad stakeholder perspective, providing a quality framework to enhance the design and application of clinical research that increasingly depends on these crucial new sources of data. This article reflects the joint work of a wide range of international stakeholders with a remit to improve the use of structured healthcare data. The programme was coordinated by the European Society of Cardiology, a non-profit organisation of healthcare professionals, and the BigData@Heart Consortium, a public-private partnership funded by the European Union Innovative Medicines Initiative. Our aim was to navigate opportunities and limitations, and to develop a framework for a broad audience of global stakeholders across all disease areas. The CODE-EHR framework seeks to realise the exciting opportunity that digitisation of health data affords to increase efficiency of healthcare systems, and improve the lives and wellbeing of patients.

Summary points

  • Research using routinely collected structured healthcare data has the potential for major clinical impact but this requires a clear and transparent approach to describe data sources, linkage protocols, coding definitions, and validation of methods and results

  • A social license and public mandate are essential components of big data research that can provide societal benefit, addressing the concerns of participants, and ensuring data privacy and integrity

  • This paper describes the output of international stakeholder meetings for the use of structured healthcare data for research purposes, including patient representatives, clinicians, scientists, regulators, journal editors, and industry representatives

  • The CODE-EHR checklist provides a minimum standards framework to enhance research design and enable more effective use and dissemination of routine healthcare data for clinical research

Stakeholder development of the CODE-EHR framework

A full range of stakeholders participated, including regulators (US Food and Drug Administration, European Medicines Agency), governmental agencies (European Commission, the UK National Institute for Health and Care Excellence, Innovative Medicines Initiative), leading medical journals (The BMJ, European Heart Journal, The Lancet, The Lancet Digital Health), and patient advocacy groups (European Heart Network, ESC Patient Forum), in addition to representatives from the pharmaceutical industry, payers, leading academic institutions, and professional societies (see acknowledgments). Development of the CODE-EHR framework was centred on two stakeholder meetings (7 July 2020 and 26 October 2020), consisting of presentations from key opinion leaders, followed by breakout sessions and plenaries to formulate statements on key topic areas. An iterative process with virtual work was used to achieve consensus positions, with a further meeting on 10 March 2022to finalise this report.

We aimed to develop pragmatic advice for the use of structured healthcare data within trials and observational studies that is not dependent on particular diseases, and that meets the expectations of stakeholders and the general public. Our objectives were to provide new direction to this increasingly important field in medicine, thereby enhancing the value of routinely collected data to improve future patient wellbeing. Detailed text on the current state-of-the-art for research using healthcare data, in addition to key challenges and limitations, was developed by the stakeholder group, supported by a writing committee. See appendix 1 in which we address the need for common standards to appraise the digital landscape (e.g, coding systems and the vital aspect of linkage), expand on current and future opportunities for the use of structured healthcare data, and show how a social license can lead to co-creation of research with a public health benefit. The key challenges and pathways for improvement are outlined in figure 1, which presents the process of structured healthcare data from initial notation to their potential use to enhance research and subsequently improve clinical practice.

Fig 1
Fig 1

From structured healthcare data to improved patient care. Key challenges and the paths to improvement leading to sustainable impact from EHR-based research studies. EHR=electronic healthcare record

The output of the stakeholder meetings and iterative discussions were condensed into four core central themes: technical process and data stewardship; data security and privacy; publications using structured healthcare data; and addressing the needs of regulators, reimbursement authorities, and clinical practice guidelines. Key statements and advisories from the consensus meetings are summarised in table 1.

Table 1

Output from the stakeholder consensus meetings

View this table:

Patient and public involvement

The CODE-EHR consensus approach has benefited from patient and public engagement throughout the development process, including representation from the European Society of Cardiology Patient Council and the European Heart Network, an alliance of foundations and associations supporting patients and representing patient interests. We describe a potential method for engagement of the public in future research that can constructively benefit research using big data (fig 2).

Fig 2
Fig 2

Patient and public engagement to improve clinical research. POSITIVE steps leading to co-creation with patients and the public, and better research using big data sources. Content adapted from the Consensus Statement on Public Involvement and Engagement with Data-Intensive Health Research36 as used in the DaRe2THINK trial programme.27 Adapted from Bunting et al.37 PPI=patient and public involvement

CODE-EHR reporting framework

The path from structured healthcare data to clinical research output is complex. To support further development in a transparent way, stakeholder delegates reached consensus of the need for a set of minimum standards that authors could use as a tool to enhance design, reporting, and research output. The CODE-EHR Minimum Standards Framework presented in table 2 allows authors to report on how structured healthcare data were used in their research study (either in patient identification, disease phenotyping, or outcome derivation). Preferred standards indicate high level attainment of quality and can be used as a tool to improve the future trajectory of research. The checklist was created through an iterative process based on the stakeholder proposals and covers five key areas of enhanced transparency: how and why coding was performed; the process of constructing and linking datasets; clear definitions of both diseases and outcomes; the approach to analysis, including any computational methods; and demonstrating good data governance.

Table 2

CODE-EHR framework: best practice checklist to report on the use of structured electronic healthcare records in clinical research

View this table:

The framework aims to improve the quality of studies using structured healthcare data and to give confidence in their use for clinical decision making. See appendix 2 for a step-by-step approach to completion of the CODE-EHR reporting checklist, with relevant best practice examples. We also present a detailed description of the workflow that led to the checklist in appendix 3. Form versions of the checklist are provided in appendix 4 (word version) and appendix 5 (pdf version).

Discussion

Technological progress has led to rapid evolution in heath data systems with immediate impact in daily clinical practice. The potential for improving patient care and outcomes are clear, as are the challenges and limitations to achieving this objective.22 Big data analytics now support large scale (and cost efficient) clinical research, with trials based within registries or the EHR itself now heralding a new era in evidence generation. These processes can be further developed by an accompanying social license and upskilling of knowledge for all stakeholders. Co-creation and shared decision making with patients and the public23 is an important way to ensure appropriate data stewardship and privacy, leading to clinical impact through robust publications, regulatory decision making, and practice guidelines. In this paper, we have reported on a global multistakeholder process to develop a framework for researchers to use in the design and reporting of studies that include structured or coded healthcare data.

Digital health records are confusing for most researchers, with varying access to a myriad of different coding systems and classifications, and considerable differences across (and within) countries. Linkage of different health sources is often a core component of research based on structured healthcare data, and yet, this aspect is frequently overlooked when reporting such studies. Data privacy and the license for research can be severely compromised if linkage is not secure; hence, our focus is on transparency about how data are coded and linked, and how these approaches are openly discussed and documented. The stakeholder consensus meetings highlighted this area as a key concern for future research, supported by evidence that very few studies have provided sufficient detail to understand the research process.2425 The advent of registry and EHR-based randomised controlled trials212627 reinforces the imperative to see improvements in these areas, and to define new concepts for quality research. With the development of robust analytics supported by machine learning algorithms,28 similar approaches have already been used to support artificial intelligence in healthcare.29

A lack of transparency has a direct impact on the value of research using coded records, with issues arising for medical journals, regulators, clinical guideline writers, and more generally clinicians and the public. Bringing together the full range of these stakeholders, we aimed to take full advantage of recent technical developments to use structured healthcare data for research, to approach limitations directly, and to provide a framework across all medical fields where coded data can be used to improve patient care. A number of other overlapping themes emerged from the discussions, including the generation and retainment of public trust and confidence, and the need for coherent plans to deal with data security failures. Forethought about dealing with the harmonisation of data and the requirement for embedded validation methods were highlighted as key factors for future successful research. Similarly, education and communication are crucial for patients, citizens and healthcare professionals to effectively use the results from structured healthcare data studies.

The covid-19 pandemic has illustrated the need for rapid access to routine healthcare data to guide and monitor clinical care, and a clinical trial infrastructure to allow for immediate deployment. The digitalisation of healthcare, in particular the use of EHRs, offered the clinical community a unique opportunity to develop a learning healthcare system that could efficiently address the effects of covid-19. For example, information about the relationship between covid-19 and cardiovascular disease through linked EHR data that has combined primary care data, hospital data, death records, and covid-19 testing in more than 54 million people.30 However, the pandemic also made clear the obstacles within various systems that restricted the sharing of data in almost real time that could direct care and help design clinical trials. Established governance, security, interoperability (system architecture that spans different EHR systems and healthcare providers), and phenotype definition, among other issues, limited access to routine EHR data especially in the first period of the pandemic.

The CODE-EHR framework is intended to complement available reporting checklists.31323334 Although existing checklists are aimed at transparency in the reporting of important methodological components of clinical research, the CODE-EHR framework is designed to ensure that a common set of minimum standards are applied across all research using structured healthcare data. This range includes observational studies and controlled trials, with the preferred standards giving the direction of research design for all future EHR studies. Additionally, the framework supports the wider implementation of good quality real world data research based on the FAIR data principles.13 Researchers are advised to use the checklist during the design phase of their study to ensure that key criteria for successful research and research impact are already embedded. The process will aid journal editors, regulators, guideline writers, clinicians, and patients to better appreciate the underpinning value, and also the limitations of the study. Dissemination plans for CODE-EHR include discussion with journals to request authors complete the checklist when submitting relevant research, attaining full registration with the EQUATOR network,35 and outreach via international digital health groups to engage their members and other relevant stakeholder organisations. After publication, the CODE-EHR framework will undergo a two year evaluation, including discussion with researchers using the approach, with a plan for iterative improvements to adapt to this rapidly developing field of medical research.

Conclusion

The CODE-EHR framework was designed by a multistakeholder panel to improve design and reporting of research studies using structured electronic healthcare data. Research using these data sources is a vital component of future healthcare evaluation and delivery and will take an increasingly important role in decisions by regulatory, governmental, and healthcare agencies, as well as clinicians and patients in every medical specialty. The CODE-EHR checklist asks for clarity on reporting and defines a set of minimum and preferred standards on the processes that underpin coding, dataset construction and linkage, disease and outcome definitions, analysis, and research governance. Iterative updates to this framework are expected to enhance research quality and value and to generate new pathways for impact using routinely collected healthcare data.

Acknowledgments

We are grateful for the work of the CODE-EHR international consensus. Delegates and attendees of the meetings were: Regulatory agencies: European Medicines Agency, Netherlands (Xavier Kurz); US Food and Drug Administration, USA (John Concato, Robert Kazmierski, Jose Pablo Morales, and Ileana Piña). Reimbursement authorities: National Health Care Institute, the Netherlands (Wim Goettsch); National Institute for Health and Care Excellence, UK (Adrian Jonas); Dental and Pharmaceutical Benefits Agency (TLV), Sweden (Niklas Hedberg). Medical Journal Editors: European Heart Journal, Switzerland (Filippo Crea and Thomas F Lüscher); The BMJ, UK (Wim Weber and Tiago Villanueva); The Lancet, UK (Stuart Spencer); The Lancet Digital Health, UK (Rupa Sarkar). Industry representatives: AstraZeneca (Martin Fredriksson and Mats Sundgren); Bayer (Andrea Derix, Gunnar Brobert, and Lothar Roessig); Servier (Benoit Tyl); The Hyve (Kees van Bochove); Vifor Pharma (Maureen Cronin). Funders: EU/EFPIA Innovative Medicines Initiative, Belgium (Colm Carroll); European Commission, Belgium (Ceri Thompson). Patient Advocacy: European Heart Network, Belgium (Birgit Beger, Susanne Løgstrup, and Marilena Vrana); European Society of Cardiology Patient Forum, France (Paul McGreavy). Clinical Academic and Professional Societies: European Society of Cardiology, France (Barbara Casadei, Stephan Achenbach, and Valentina Tursini); European Heart Agency, Belgium (Panos E Vardas); Oslo University Hospital and University of Oslo, Norway (Dan Atar); University of Oxford, UK (Colin Baigent); University of Leeds, UK (Chris P Gale); Queen's University Belfast, UK (Donna Fitzsimons); University Hospital Bern, Switzerland (Stephan Windecker); Charité-Universitätsmedizin Berlin, Germany (Stefan D Anker); Royal Brompton Hospital and Imperial College London, UK (Martin Cowie); University College London, UK (Amitava Banerjee, Harry Hemingway, R Tom Lumbers, Spiros Denaxas); University Medical Centre Utrecht, Netherlands (Folkert W Asselbergs, Rick Grobbee, and Ghislaine Van Thiel); University of Birmingham and University Hospitals Birmingham NHS Trust, UK (Dipak Kotecha and George V Gkoutos); University of Cambridge, UK (Angela Wood); Karolinska Institutet and Karolinska University Hospital Stockholm, Sweden (Martin Ingvar). Administrative: BigData@Heart Project Management, Germany (Carl Steinbeisser and Ana Petrova); European Society of Cardiology Project Management, France (Cinzia Ceccarelli, Katija Baljevic, and Polyxeni Vairami); Medical Writer, UK (Jennifer Taylor).

Footnotes

  • Contributors: DK and FWA are joint first authors with equal contribution, and act as guarantors of the work. Correspondence to FWA at f.w.asselbergs@umcutrecht.nl. Each subsequent listed author was involved in the CODE-EHR consensus process and wrote or revised sections of text and revised the final manuscript for intellectual content. The corresponding authors attest that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

  • Funding: The BigData@Heart project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 116074. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and European Federation of Pharmaceutical Industries and Associations. The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

  • Competing interests: All authors have completed the ICMJE uniform disclosure form (www.icmje.org/coi_disclosure.pdf) and declare: DK reports grants from EU/EFPIA Innovative Medicines Initiative (BigData@Heart 116074), during the study; grants from National Institute for Health Research (NIHR CDF-2015-08-074 RATE-AF; NIHR130280 DaRe2THINK; NIHR132974 D2T-NeuroVascular); British Heart Foundation (PG/17/55/33087, AA/18/2/34218 and FS/CDRF/21/21032); and European Society of Cardiology supported by educational grants from Boehringer Ingelheim, BMS-Pfizer Alliance, Bayer, Daiichi Sankyo, Boston Scientific, the NIHR/University of Oxford Biomedical Research Centre, and British Heart Foundation/University of Birmingham Accelerator Award (STEEER-AF NCT04396418); Amomed Pharma and IRCCS San Raffaele/Menarini (Beta-blockers in Heart Failure Collaborative Group NCT0083244), outside of this work; and advisory board personal fees from Bayer, Amomed, Protherics Medicines Development and Myokardia, outside of this work. FWA reports grants from IMI BigData@Heart, during the study. SDA reports grants and personal fees from Vifor Int, Abbott, and Abbott Vascular; personal fees from Bayer, Boehringer Ingelheim, and Servier; personal fees from Cardiac Dimensions, Actimed, Astra Zeneca, Amgen, Bioventrix, Janssen, Respicardia, V-Wave, Brahms, Cordio, and Occlutech, outside of this work. CB reports grants from Medical Research Council, Boehringer Ingelheim, and NIHR, outside of this work. AB reports grants from Astra Zeneca, outside of this work. BB reports grants from European Commission, during the study. GB reports grants from European Commission (IMI project support), during the study; and other from Bayer AB, outside of this work. BC reports non-financial support from Roche Diagnostics and iRhythm, outside of this work. CC reports grants from European Commission, during the study. MRC reports personal fees from AstraZeneca, outside of this work. FC reports personal fees from Amgen, Astra Zeneca, Servier, and BMS; other from GlyCardial Diagnostics, outside of this work. MC reports personal fees from Vifor Pharma, during the study; and personal fees from Vifor Pharma, outside of this work. AD reports other from Bayer AG, outside of this work. MF reports other from AstraZeneca, during the study; other from AstraZeneca, outside of this work; and is employed by AstraZeneca. CPG reports personal fees from AstraZeneca, Amgen, Bayer, Boehringer-Ingelheim, Daiichi Sankyo, Vifor Pharma, Menarini, Wondr Medical, Raisio Group, and Oxford University Press; and grants from BMS, Abbott, British Heart Foundation, NIHR, and ESC, outside of this work. MI reports grants from World Economic Forum, Swedish Innovation Agency, and European Commission, during the study; and other collaboration with Frisq AB, outside of this work. SL reports grants from European Commission, during the study. TFL reports grants from Abbott, Amgen, Novartis, Boehringer Ingelheim, Servier, Vifor, Sanofi, and AstraZeneca; and personal fees from Daichi Sankyo, Pfizer, and Menarini, outside of this work. LR reports other from Bayer AG, during the study and other from Bayer AG, outside this work. CS reports personal fees from Bayer AG, during the study and personal fees from Bayer AG, outside of this work. BT reports personal fees from Servier, outside of this work. GvT reports grants from IMI, during the study. KvB reports grants from IMI BigData@Heart, during the study. PEV reports personal fees from Hygeia Hospitals Group, HHG group, European Society of Cardiology, and Servier International, outside of this work. TV is working as an editor at TheBMJ and Acta Médica Portuguesa and is vice president of the European Union of General Practitioners (UEMO). MV reports grants from European Commission, during the study. SW reports grants from Abbott, Amgen, Astra Zeneca, BMS, Bayer, Biotronik, Boston Scientific, Cardinal Health, CardioValve, CSL Behring, Daiichi Sankyo, Edwards Lifesciences, Guerbet, InfraRedx, Johnson & Johnson, Medicure, Medtronic, Novartis, Polares, OrPha Suisse, Pfizer, Regeneron, Sanofi-Aventis, Sinomed, Terumo, and V-Wave, outside of this work; and SW serves as unpaid advisory board member or unpaid member of the steering or executive group of trials funded by Abbott, Abiomed, Amgen, Astra Zeneca, BMS, Boston Scientific, Biotronik, Cardiovalve, Edwards Lifesciences, MedAlliance, Medtronic, Novartis, Polares, Sinomed, V-Wave, and Xeltis, but has not received personal payments by pharmaceutical companies or device manufacturers. SW is also member of the steering or executive committee group of several investigator-initiated trials that receive funding by industry without impact on his personal remuneration. SW is an unpaid member of the Pfizer Research Award selection committee in Switzerland and of the Women as One Awards Committee. SW is also member of the Clinical Study Group of the Deutsches Zentrum für Herz Kreislauf-Forschung and of the Advisory Board of the Australian Victorian Heart Institute. He is chairperson of the ESC Congress Program Committee, former chairperson of the ESC Clinical Practice Guidelines Committee and Deputy Editor of JACC CV Interventions. All other authors declare no competing interests.

  • Dissemination to participants and related patient and public communities: Output will be disseminated to relevant patient and public communities via the European Society of Cardiology, the European Heart Network, and through communication with other relevant international bodies.

  • Provenance and peer review: Not commissioned; externally peer reviewed.

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