ArticleEye (London, England)2011
The cataract national data set electronic multi-centre audit of 55,567 operations: case-mix adjusted surgeon's outcomes for posterior capsule rupture.
Article in Eye (London, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- The learning curve of a high-fidelity cataract surgical simulator.BMC medical education · 2026Article
- Refractive Outcomes After Cataract Surgery-The Impact of Preoperative Visual Acuity, the Intraocular Lens Model, and the Surgeon's Experience: An Empirical Analysis of Hungarian and Kosovan Patients.Journal of clinical medicine · 2024Article
- Article
- Graphical comparison of surgeon outcomes for the audit of a national corneal transplant registry (OTAG study 32).Eye (London, England) · 2023Article
- DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.Ophthalmology · 2022Article
- Simulation of advanced cataract surgery - validation of a newly developed test.Acta ophthalmologica · 2020Article
- Cataract complications study: an analysis of adverse effects among 14,520 eyes in relation to surgical experience.Annals of translational medicine · 2020Article
- Clinical outcomes of management of posterior capsule rupture with air bubble techniques.International journal of ophthalmology · 2020Article
- The evaluation of the risk factors for capsular complications in phacoemulsification.International ophthalmology · 2018Article
- Intraoperative head drift and eye movement: two under addressed challenges during cataract surgery.Eye (London, England) · 2018Article
- Contribution of Electronic Medical Records to the Management of Rare Diseases.BioMed research international · 2015Article
- United Kingdom National Ophthalmology Database Study: Diabetic Retinopathy; Report 1: prevalence of centre-involving diabetic macular oedema and other grades of maculopathy and retinopathy in hospital eye services.Eye (London, England) · 2013Article
- United Kingdom National Ophthalmology Database Study of Vitreoretinal Surgery: report 1; case mix, complications, and cataract.Eye (London, England) · 2013Article
- Cataract skills: how do we judge competency?Journal of the Royal Society of Medicine · 2013Article
- The Cataract National Dataset electronic multi-centre audit of 55,567 operations: risk indicators for monocular visual acuity outcomes.Eye (London, England) · 2012Article
- Consent for cataract surgery training: a national trainers' survey.Eye (London, England) · 2012Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsTo develop a methodology for case-mix adjustment of surgical outcomes for individual cataract surgeons using electronically collected multi-centre data conforming to the cataract national data set (CND).
methodsRoutinely collected anonymised data were remotely extracted from electronic patient record (EPR) systems in 12 participating NHS Trusts undertaking cataract surgery. Following data checks and cleaning, analyses were carried out to risk adjust outcomes for posterior capsule rupture rates for individual surgeons, with stratification by surgical grade.
resultsA total of 406 surgeons from 12 NHS Trusts submitted data on 55,567 cataract operations between November 2001 and July 2006 (86% from January 2004). In all, 283 surgeons contributed data on >25 cases, providing 54,319 operations suitable for detailed analysis. Case-mix adjusted results of individual surgeons are presented as funnel plots for all surgeons together, and separately for three different grades of surgeon. Plots include 95 and 99.8% confidence limits around the case-mix adjusted outcomes for detection of surgical outliers.
conclusionsRoutinely collected electronic data conforming to the CND provides sufficient detail for case-mix adjustment of cataract surgical outcomes. The validation of these risk indicators should be carried out using fresh data to confirm the validity of the risk model. Once validated this model should provide an equitable approach for peer-to-peer comparisons in the context of revalidation.
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