Comparison of identifiable and non-identifiable data linkage: health technology assessment of MitraClip using registry, administrative and mortality datasets.


Journal

BMJ health & care informatics
ISSN: 2632-1009
Titre abrégé: BMJ Health Care Inform
Pays: England
ID NLM: 101745500

Informations de publication

Date de publication:
Apr 2021
Historique:
received: 11 08 2020
revised: 21 01 2021
accepted: 08 03 2021
entrez: 6 4 2021
pubmed: 7 4 2021
medline: 10 11 2021
Statut: ppublish

Résumé

The UK MitraClip registry was commissioned by National Health Service (NHS) England to assess real-world outcomes from percutaneous mitral valve repair for mitral regurgitation using a new technology, MitraClip. This study aimed to determine longitudinal patient outcomes by linking to routine datasets: Hospital Episode Statistics (HES) Admitted Patient Care (APC) and Office of National Statistics. Two methods of linkage were compared, using identifiable (NHS number, date of birth, postcode, gender) and non-identifiable data (hospital trust, age in years, admission, discharge and operation dates, operation and diagnosis codes). Outcome measures included: matching success, patient demographics, all-cause mortality and subsequent cardiac intervention. A total of 197 registry patients were eligible for matching with routine administrative data. Using identifiable linkage, a total of 187 patients (94.9%) were matched with the HES APC dataset. However, 21 matched individuals (11.2%) had inconsistencies across the datasets (eg, different gender) and were subsequently removed, leaving 166 (84.3%) for analysis. Using non-identifiable data linkage, a total of 170 patients (86.3%) were uniquely matched with the HES APC dataset.Baseline patient characteristics were not significantly different between the two methods of data linkage. The total number of deaths (all causes) identified from identifiable and non-identifiable linkage methods was 37 and 40, respectively, and the difference in subsequent cardiac interventions identified between the two methods was negligible. Patients from a bespoke clinical procedural registry were matched to routine administrative data using identifiable and non-identifiable methods with equivalent matching success rates, similar baseline characteristics and similar 2-year outcomes.

Identifiants

pubmed: 33820808
pii: bmjhci-2020-100223
doi: 10.1136/bmjhci-2020-100223
pmc: PMC8030467
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: The Newcastle upon Tyne Hospitals NHS Foundation Trust, the employing institution of KK, PC, SU, JB, HC and AS, is contracted as External Assessment Centre to the NICE Medical Technologies Evaluation Programme (MTEP) and is contracted by Academic Health Science Network North East and North Cumbria to develop methodologies and case studies relating to‘evaluation in practice’ in the context of using routine healthcare datasets and, where appropriate, clinical registries, to assess outcomes and adoption of novel interventions. AS reports grants from NIHR and Wellcome Trust and outside the submitted work. KK reports grants from NIHR outside the submitted work. HP and LB are employed by NICE and were contracted by NHS England to oversee the Commissioning through Evaluation scheme. SG is employed by NHS Digital. HP and LB are employed by NICE and were contracted by NHS England to oversee the Commissioning through Evaluation scheme. No other financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

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Auteurs

Kim Keltie (K)

Northern Medical Physics and Clinical Engineering, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle Upon Tyne, Tyne and Wear, UK.
Translational and Clinical Research Institute, University of Newcastle upon Tyne, Newcastle upon Tyne, UK.

Paola Cognigni (P)

Northern Medical Physics and Clinical Engineering, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle Upon Tyne, Tyne and Wear, UK.

Sam Gross (S)

Data Management Services, NHS Digital, Leeds, Leeds, UK.

Samuel Urwin (S)

Translational and Clinical Research Institute, University of Newcastle upon Tyne, Newcastle upon Tyne, UK.

Julie Burn (J)

Northern Medical Physics and Clinical Engineering, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle Upon Tyne, Tyne and Wear, UK.

Helen Cole (H)

The Northern Health Science Alliance, Manchester, UK.

Lee Berry (L)

Observational Data Unit, National Institute for Health and Care Excellence, London, London, UK.

Hannah Patrick (H)

Observational Data Unit, National Institute for Health and Care Excellence, London, London, UK.

Andrew Sims (A)

Northern Medical Physics and Clinical Engineering, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle Upon Tyne, Tyne and Wear, UK andrew.sims5@nhs.net.
Translational and Clinical Research Institute, University of Newcastle upon Tyne, Newcastle upon Tyne, UK.

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