BACKGROUND: There is an increasing demand for routine assessment of surgical outcomes. However, for assessments to be valid, it is essential to understand the influence of @entity1 characteristics on outcomes. The VF-14 visual function instrument offers a possible means of monitoring outcomes of @entity231 surgery. This article examines lessons learned in the course of an attempt to do this. METHODS: One thousand and ninety-eight @entity1 (41 per cent male and 59 per cent female) undergoing @entity231 extraction in 29 private hospitals within the British United Provident Association (BUPA) chain in the United Kingdom completed the VF-14 before and at 4 months following surgery. Outcome was measured in terms of absolute post-operative score achieved. The demographic factors associated with post-operative VF-14 score were examined using multivariate analysis, adjusting for baseline score. RESULTS: @entity1 undergoing @entity231 extraction had worse pre-operative visual function than @entity1 . Post-operative VF-14 score was stable as the age of the @entity1 increased, until age 85, after which it declined. The reduction in final outcome in older @entity1 was explained in part by their worse post-operative scores but also was associated with increasing age independent of pre-operative score. CONCLUSION: First, although many @entity1 undergoing @entity231 extraction at an advanced age will achieve excellent results, older age does reduce the outcome that can be expected. Consequently, comparisons of performance of units or surgeons undertaking @entity231 surgery must take account of the age spectrum being treated. It is not enough simply to add a health status measure to an existing administrative database. Further research is needed to understand the reasons for the poorer outcomes at older ages, in particular the role of co-existing @entity2194 and development of data systems must take account of the additional information required to make appropriate adjustments.

Comparing outcomes of XXXX surgery: challenges and opportunities.

@entity1 :: ('9606', 'Species') :: ['patients', 'Women', 'patient', 'people', 'men'] @entity2194 :: ('MESH:D005128', 'Disease') :: ['eye disease'] @entity231 :: ('MESH:D002386', 'Disease') :: ['cataract']
@entity231 :: (MESH:D002386,Disease) :: ['cataract']
