Identifying cardiac surgery operations in hospital episode statistics administrative database, with an OPCS-based classification of procedures, validated against clinical data.


Journal

BMJ open
ISSN: 2044-6055
Titre abrégé: BMJ Open
Pays: England
ID NLM: 101552874

Informations de publication

Date de publication:
23 03 2019
Historique:
entrez: 25 3 2019
pubmed: 25 3 2019
medline: 1 4 2020
Statut: epublish

Résumé

Administrative databases with dedicated coding systems in healthcare systems where providers are funded based on services recorded have been shown to be useful for clinical research, although their reliability is still questioned. We devised a custom classification of procedures and algorithms based on OPCS, enabling us to identify open heart surgeries from the English administrative database, Hospital Episode Statistics, with the objective of comparing the incidence of cardiac procedures in administrative and clinical databases. A comparative study of the incidence of cardiac procedures in administrative and clinical databases. Data from all National Health Service Trusts in England, performing cardiac surgery. Patients classified as having cardiac surgery across England between 2004 and 2015, using a combination of procedure codes, age >18 and consultant specialty, where the classification was validated against internal and external benchmarks. We identified a total of 296 426 cardiac surgery procedures, of which majority of the procedures were coronary artery bypass grafting (CABG), aortic valve replacement (AVR), mitral repair and aortic surgery. The matching at local level was 100% for CABG and transplant, >90% for aortic valve and major aortic procedures and >80% for mitral. At national level, results were similar for CABG (IQR 98.6%-104%), AVR (IQR 105%-118%) and mitral valve replacement (IQR 86.2%-111%). We set up a process which can identify cardiac surgeries in England from administrative data. This will lead to the development of a risk model to predict early and late postoperative mortality, useful for risk stratification, risk prediction, benchmarking and real-time monitoring. Once appropriately adjusted, the system can be applied to other specialties, proving especially useful in those areas where clinical databases are not fully established.

Identifiants

pubmed: 30904838
pii: bmjopen-2018-023316
doi: 10.1136/bmjopen-2018-023316
pmc: PMC6475180
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e023316

Informations de copyright

© Author(s) (or their employer(s)) 2019. 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: None declared.

Références

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pubmed: 17452389
Int J Cardiol. 2016 Jan 15;203:196-203
pubmed: 26512837
Circulation. 2007 Mar 27;115(12):1518-27
pubmed: 17353447
Heart. 2014 May;100(9):702-10
pubmed: 24334377
Nurs Manag (Harrow). 2005 May 1;12(2):21
pubmed: 27724657

Auteurs

Giacomo Bortolussi (G)

Quality and Outcome Research Unit, University Hospital Birmingham, Birmingham, UK.

David McNulty (D)

Quality and Outcome Research Unit, University Hospital Birmingham, Birmingham, UK.

Hina Waheed (H)

Quality and Outcome Research Unit, University Hospital Birmingham, Birmingham, UK.

Jamie A Mawhinney (JA)

Quality and Outcome Research Unit, University Hospital Birmingham, Birmingham, UK.

Nick Freemantle (N)

Quality and Outcome Research Unit, University Hospital Birmingham, Birmingham, UK.
Institute of Clinical Trials and Methodology, University College London, London, UK.

Domenico Pagano (D)

Quality and Outcome Research Unit, University Hospital Birmingham, Birmingham, UK.
Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK.

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