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A Bayesian hierarchical approach to comparative audit for carotid surgery
G Kuhan1, E C Marshall, A F Abidia
1Academic Vascular Unit, Hull Royal Infirmary, Anlaby Road, Hull, HU3 2JZ, UK.
Objectives:
the aim of this study was to illustrate how a Bayesian hierarchical modelling approach can aid the reliable comparison of outcome rates between surgeons.
Design:
retrospective analysis of prospective and retrospective data.
Materials:
binary outcome data (death/stroke within 30 days), together with information on 15 possible risk factors specific for CEA were available on 836 CEAs performed by four vascular surgeons from 1992-99. The median patient age was 68 (range 38-86) years and 60% were men.
Methods:
the model was developed using the WinBUGS software. After adjusting for patient-level risk factors, a cross-validatory approach was adopted to identify "divergent" performance. A ranking exercise was also carried out.
Results:
the overall observed 30-day stroke/death rate was 3.9% (33/836). The model found diabetes, stroke and heart disease to be significant risk factors. There was no significant difference between the predicted and observed outcome rates for any surgeon (Bayesian p -value>0.05). Each surgeon had a median rank of 3 with associated 95% CI 1.0-5.0, despite the variability of observed stroke/death rate from 2.9-4.4%. After risk adjustment, there was very little residual between-surgeon variability in outcome rate.
Conclusions:
Bayesian hierarchical models can help to accurately quantify the uncertainty associated with surgeons' performance and rank.