Clinical governance and the vascular surgeon

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Abstract
Background: Audit of adverse outcome might allow identification of substandard surgical results. To test this hypothesis statistical modelling was applied to two indicator vascular procedures (elective abdominal aortic aneurysm repair and carotid endarterectomy) with accepted adverse event rates. Methods: Binomial statistical models for varying adverse event rates were constructed. A power calculation was used in an attempt to predict the case numbers required to determine substandard results for individual surgeons and vascular units. Two scenarios were considered: first a base adverse event rate of 6 per cent and surgical practice with 9, 12 and 24 per cent morbidity rates, and second a base adverse event rate of 3 per cent and surgical practice with 6, 9 and 12 per cent morbidity rates. Results: A mean of 57 elective abdominal aortic aneurysm repairs and 70 carotid endarterectomies were performed per annum. The adverse event rate for both operations was 4 per cent. Power calculations revealed that 130 patients would need to be studied to detect a surgeon with an adverse event rate twice 6 per cent and over 280 patients would be required with an adverse event rate twice 3 per cent. To gather this number of patients 2 years of unit data and between 3 and 22 years of individual data would need to be studied for a base adverse event rate of 6 per cent. A base rate of 3 per cent requires 7–47 years for an individual and 4–65 years for the unit. With a base adverse event rate of 6 per cent, detection of widely variant surgical practice (four times the morbidity rate as base) requires only 21 procedures. Conclusion: Statistical modelling demands assumptions about accepted adverse event rates, confidence criteria and what constitutes substandard results. Data from large numbers of patients are required even for common operations with accepted adverse event rates. These data raise serious questions as to the feasibility of performing clinical governance on the basis of morbidity and mortality event rates alone.