Bortolussi, G; McNulty, D; Waheed, H; Mawhinney, JA; Freemantle, N; Pagano, D; (2019) Identifying cardiac surgery operations in hospital episode statistics administrative database, with an OPCS-based classification of procedures, validated against clinical data. BMJ Open , 9 (3) , Article e023316. 10.1136/bmjopen-2018-023316.

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Abstract

OBJECTIVES: 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. DESIGN: A comparative study of the incidence of cardiac procedures in administrative and clinical databases. SETTING: Data from all National Health Service Trusts in England, performing cardiac surgery. PARTICIPANTS: 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. RESULTS: 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%). CONCLUSIONS: 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.

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