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About the Audit

Radical cystectomy is the removal of the bladder for cancer with urinary diversion or bladder reconstruction.  The operative technique used may be open, laparoscopic or laparoscopic with robotic assistance. 

Although the BAUS Section of Oncology has been running a radical cystectomy audit since 2004, this is only the fourth year that individual outcomes data have been made available to the public.  

National reports and cumulative data from the previous years' BAUS audits are available on this site by clicking here. Please note: these reports are copyright-protected so, if you wish to use the information contained in them, you must contact BAUS to obtain permission.

The Section has decided to keep the published data simple.  We have, therefore, only listed the number of operations performed by technique, the transfusion rate split by open and minimally invasive procedures, length of stay (LOS) along with the 30-day and 90-day mortality rates. In addition we have included an average patient profile by presenting the proportion of a number of parameters for each individual surgeon/unit, as compared to the total dataset. BAUS is, of course, aware that there are considerable gaps in these data.

This is still the early stage of a lengthy project, and we accept that it may be flawed. However, in the long term, this audit will deliver good quality data and will be a valuable tool in improving care. The BAUS Section of Oncology, in consultation with the wider membership, will continue to review the analysed data to identify any additional outcome indicators which may have potential uses in future years.

Interpretation of the data

The data presented are surgeon-reported, by entry into the BAUS Data & Audit System. There is, therefore, no method for reliably validating the data other than by comparing with the latest Hospital Episode Statistics (HES).

There are no financial incentives (or sanctions) for hospitals and Trusts to support collection of radical cystectomy data, and this may also account for the data being incomplete.