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Understanding the Data
Outcomes following paediatric cardiac surgery have long been the subject of clinical, regulatory, media and public scrutiny.
There are several reasons for this. The work is among the most technically challenging, resource intensive and emotionally charged clinical activity undertaken.
In the UK, past events, public inquiries and intentions to reduce the number of centres performing this surgery provide a rich source of back-stories and a level of public awareness that make paediatric cardiac surgery ripe for political comment and productive journalism.
In this context, collection and open reporting of outcome data at a national level is as fraught with difficulties as it is inescapable. Chief among these is a reasonable expectation from the profession that audit will be ‘fair’ to clinical teams. This translates to a view that, in the reporting of outcomes, account should be taken of the hugely diverse set of diagnoses and comorbid conditions that patients present with, the wide range of surgical procedures performed, differences in case mix between centres and the impact of the relatively small numbers of patients on what can reliably be inferred from data.
These characteristics of the specialty make risk-adjustment in outcomes analysis deemed essential, but they also make it very difficult to achieve.
There have been a number of proposed models to stratify the risks (i.e. make the results of different procedures comparable between surgical units). Currently data is sent from the various surgical centres to an independent monitor - NICOR (National Institute for Cardiovascular Outcomes Research) where the data is analysed using PRAiS2 methodology and reported.
Surgical outcomes in children continue to have excellent outcomes for under 16s undergoing cardiac surgery with the 30-day unadjusted post-surgical mortality rate at its lowest level, at 1.4% for 2018/19, outcomes that are amongst the best reported worldwide. All 12 centres reported had survival rates for children under 16 years of age that were better than the lower alert and alarm limits.
To understand the complexities of PRAiS2 methodology in more detail read this PRAIS2 Article