Demonstrating the data integrity of routinely collected healthcare systems data for clinical trials (DEDICaTe): A proof-of-concept study

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Abstract

Introduction/aims

Healthcare systems data (also known as real-world or routinely collected health data) could transform the conduct of clinical trials. Demonstrating integrity and provenance of these data is critical for clinical trials, to enable their use where appropriate and avoid duplication using scarce trial resources. Building on previous work, this proof-of-concept study used a data intelligence tool, the “Central Metastore,” to provide metadata and lineage information of nationally held data.

Methods

The feasibility of NHS England’s Central Metastore to capture detailed records of the origins, processes, and methods that produce four datasets was assessed. These were England’s Hospital Episode Statistics (Admitted Patient Care, Outpatients, Critical Care) and the Civil Registration of Deaths (England and Wales). The process comprised: information gathering; information ingestion using the tool; and auto-generation of lineage diagrams/content to show data integrity. A guidance document to standardise this process was developed.

Results/Discussion

The tool can ingest, store and display data provenance in sufficient detail to support trust and transparency in using these datasets for trials. The slowest step was information gathering from multiple sources, so consistency in record-keeping is essential.

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Most cited references 31

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Dexamethasone in Hospitalized Patients with Covid-19 — Preliminary Report

Mark Peters (2021)

Abstract Background Coronavirus disease 2019 (Covid-19) is associated with diffuse lung damage. Glucocorticoids may modulate inflammation-mediated lung injury and thereby reduce progression to respiratory failure and death. Methods In this controlled, open-label trial comparing a range of possible treatments in patients who were hospitalized with Covid-19, we randomly assigned patients to receive oral or intravenous dexamethasone (at a dose of 6 mg once daily) for up to 10 days or to receive usual care alone. The primary outcome was 28-day mortality. Here, we report the preliminary results of this comparison. Results A total of 2104 patients were assigned to receive dexamethasone and 4321 to receive usual care. Overall, 482 patients (22.9%) in the dexamethasone group and 1110 patients (25.7%) in the usual care group died within 28 days after randomization (age-adjusted rate ratio, 0.83; 95% confidence interval [CI], 0.75 to 0.93; P<0.001). The proportional and absolute between-group differences in mortality varied considerably according to the level of respiratory support that the patients were receiving at the time of randomization. In the dexamethasone group, the incidence of death was lower than that in the usual care group among patients receiving invasive mechanical ventilation (29.3% vs. 41.4%; rate ratio, 0.64; 95% CI, 0.51 to 0.81) and among those receiving oxygen without invasive mechanical ventilation (23.3% vs. 26.2%; rate ratio, 0.82; 95% CI, 0.72 to 0.94) but not among those who were receiving no respiratory support at randomization (17.8% vs. 14.0%; rate ratio, 1.19; 95% CI, 0.91 to 1.55). Conclusions In patients hospitalized with Covid-19, the use of dexamethasone resulted in lower 28-day mortality among those who were receiving either invasive mechanical ventilation or oxygen alone at randomization but not among those receiving no respiratory support. (Funded by the Medical Research Council and National Institute for Health Research and others; RECOVERY ClinicalTrials.gov number, NCT04381936; ISRCTN number, 50189673.)

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The FAIR Guiding Principles for scientific data management and stewardship

Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg … (2017)

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

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Inhaled budesonide for COVID-19 in people at high risk of complications in the community in the UK (PRINCIPLE): a randomised, controlled, open-label, adaptive platform trial

Ly-Mee Yu, Mona Bafadhel, Jienchi Dorward … (2021)

Background A previous efficacy trial found benefit from inhaled budesonide for COVID-19 in patients not admitted to hospital, but effectiveness in high-risk individuals is unknown. We aimed to establish whether inhaled budesonide reduces time to recovery and COVID-19-related hospital admissions or deaths among people at high risk of complications in the community. Methods PRINCIPLE is a multicentre, open-label, multi-arm, randomised, controlled, adaptive platform trial done remotely from a central trial site and at primary care centres in the UK. Eligible participants were aged 65 years or older or 50 years or older with comorbidities, and unwell for up to 14 days with suspected COVID-19 but not admitted to hospital. Participants were randomly assigned to usual care, usual care plus inhaled budesonide (800 μg twice daily for 14 days), or usual care plus other interventions, and followed up for 28 days. Participants were aware of group assignment. The coprimary endpoints are time to first self-reported recovery and hospital admission or death related to COVID-19, within 28 days, analysed using Bayesian models. The primary analysis population included all eligible SARS-CoV-2-positive participants randomly assigned to budesonide, usual care, and other interventions, from the start of the platform trial until the budesonide group was closed. This trial is registered at the ISRCTN registry (ISRCTN86534580) and is ongoing. Findings The trial began enrolment on April 2, 2020, with randomisation to budesonide from Nov 27, 2020, until March 31, 2021, when the prespecified time to recovery superiority criterion was met. 4700 participants were randomly assigned to budesonide (n=1073), usual care alone (n=1988), or other treatments (n=1639). The primary analysis model includes 2530 SARS-CoV-2-positive participants, with 787 in the budesonide group, 1069 in the usual care group, and 974 receiving other treatments. There was a benefit in time to first self-reported recovery of an estimated 2·94 days (95% Bayesian credible interval [BCI] 1·19 to 5·12) in the budesonide group versus the usual care group (11·8 days [95% BCI 10·0 to 14·1] vs 14·7 days [12·3 to 18·0]; hazard ratio 1·21 [95% BCI 1·08 to 1·36]), with a probability of superiority greater than 0·999, meeting the prespecified superiority threshold of 0·99. For the hospital admission or death outcome, the estimated rate was 6·8% (95% BCI 4·1 to 10·2) in the budesonide group versus 8·8% (5·5 to 12·7) in the usual care group (estimated absolute difference 2·0% [95% BCI –0·2 to 4·5]; odds ratio 0·75 [95% BCI 0·55 to 1·03]), with a probability of superiority 0·963, below the prespecified superiority threshold of 0·975. Two participants in the budesonide group and four in the usual care group had serious adverse events (hospital admissions unrelated to COVID-19). Interpretation Inhaled budesonide improves time to recovery, with a chance of also reducing hospital admissions or deaths (although our results did not meet the superiority threshold), in people with COVID-19 in the community who are at higher risk of complications. Funding National Institute of Health Research and United Kingdom Research Innovation.

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Author and article information

Contributors

Macey L Murray:

ORCID: https://orcid.org/0000-0001-6418-0854

Sharon B Love:

ORCID: https://orcid.org/0000-0002-6695-5390

Rebecca Lee

James R Carpenter

Marion Mafham

Mahesh KB Parmar:

ORCID: https://orcid.org/0000-0003-0166-1700

Heather Pinches

Matthew R Sydes:

ORCID: https://orcid.org/0000-0002-9323-1371

Journal

Journal ID (nlm-journal-id): 100883604

Journal ID (nlm-ta): Health Informatics J

Journal ID (iso-abbrev): Health Informatics J

Title: Health informatics journal

ISSN (Print): 1460-4582

ISSN (Electronic): 1741-2811

Publication date Nihms-submitted: 27 November 2024

Publication date (Print): 01 July 2024

Publication date PMC-release: 08 January 2025

Volume: 30

Issue: 3

Page: 14604582241276969

Affiliations

MRC Clinical Trials Unit at UCL (MRC CTU) ( https://ror.org/001mm6w73) , Institute of Clinical Trials and Methodology, UCL ( https://ror.org/02jx3x895) , London, UK; Health Data Research UK (HDR UK) ( https://ror.org/04rtjaj74) , London, UK; NHS DigiTrials and Research Products Services, Data & Analytics, NHS England (NHSE), Leeds, UK

Corporate Metadata Team, Transformation Directorate, NHS England, Leeds, UK

MRC Clinical Trials Unit at UCL ( https://ror.org/001mm6w73) , Institute of Clinical Trials and Methodology, UCL ( https://ror.org/02jx3x895) , London, UK; Health Data Research UK ( https://ror.org/04rtjaj74) , London, UK; Department of Medical Statistics, London School of Hygiene and Tropical Medicine ( https://ror.org/00a0jsq62) , London, UK

Nuffield Department of Population Health, University of Oxford ( https://ror.org/052gg0110) , Oxford, UK; Health Data Research UK ( https://ror.org/04rtjaj74) , London, UK

NHS DigiTrials and Research Products Services, Data & Analytics, NHS England, Leeds, UK

MRC Clinical Trials Unit at UCL ( https://ror.org/001mm6w73) , Institute of Clinical Trials and Methodology, UCL ( https://ror.org/02jx3x895) , London, UK; Health Data Research UK ( https://ror.org/04rtjaj74) , London, UK; British Heart Foundation Data Science Centre, HDR UK ( https://ror.org/04rtjaj74) , London, UK; Data for Research and Development Programme, Transformation Directorate, NHS England ( https://ror.org/00xm3h672) , London, UK

Author notes

Corresponding author: Macey L Murray, MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, 90 High Holborn, London, WC1V 6LJ, UK. macey.murray@ 123456ucl.ac.uk

Author information

Macey L Murray https://orcid.org/0000-0001-6418-0854

Sharon B Love https://orcid.org/0000-0002-6695-5390

Mahesh KB Parmar https://orcid.org/0000-0003-0166-1700

Matthew R Sydes https://orcid.org/0000-0002-9323-1371

Article

Manuscript ID: EMS201282

DOI: 10.1177/14604582241276969

PMC ID: 7617287

PubMed ID: 39291806

SO-VID: 20354114-1c42-4f42-b003-c75ce0ca4103

License:

This work is licensed under a BY 4.0 International license.

Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

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Demonstrating the data integrity of routinely collected healthcare systems data for clinical trials (DEDICaTe): A proof-of-concept study

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Abstract

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Related collections

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Most cited references 31

Dexamethasone in Hospitalized Patients with Covid-19 — Preliminary Report

The FAIR Guiding Principles for scientific data management and stewardship

Inhaled budesonide for COVID-19 in people at high risk of complications in the community in the UK (PRINCIPLE): a randomised, controlled, open-label, adaptive platform trial

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