Tuesday, 6 July 2010

Predicting kidney disease – the QKidney scores

“Planning will always get it wrong” my good friend Colin Short, Consultant Renal Physician (Central Manchester NHS Foundation Trust), frequently observed when we worked together on the Greater Manchester Renal Project in the late 1990s; “it’s just a matter of by how much”. It’s true and using USA or global data on, for instance, diabetes to predict the future burden of kidney disease is more likely to get it wrong by a bigger margin than by using information from the UK that may also provide knowledge about local variation. The recent National Diabetes Audt Report is quite alarming from a kidney disease perspective. We know kidney disease in England is set to increase but by how much and what can we do about it?

Julia Hippesley-Cox and the QResearch Team in Nottingham have just published 2 new risk algorithms for estimating individual 5-year risk of developing moderate-severe kidney disease (Stage 3B, eGFR < href="http://www.qkidney.org/">QKidney. QKidney can be used within the GP or practice nurse consultation with an individual patient and risk displayed to the patient so that potential interventions can be discussed or used at the population level for the practice or even whole health economy.

This is the first generally applicable software tool that can be used for individual risk assessment. It isn’t perfect. The study population is large and the fact that ethnicity and social deprivation along with traditional risk parameters and co-morbidity are included is a major plus. The ethnicity data are particularly interesting, unexpected they show lower rates of CKD in both black males and females.

The study period 2001-2008 was a time of transformational change. By 2001 much of the electronic patient record was in place and lab links, that automatically transfer eGFR results into primary care computer systems, were beginning to be the norm. Only 20% of the Qkidney population had a serum creatinine prior to the baseline whereas 55% had a creatinine during the follow up period. CKD only became recognised by the wider clinical community after the publication of Part 2 of the Renal NSF in 2005, the move to automatic eGFR reporting and inclusion of a CKD domain in the Quality and Outcomes Framework for primary care in April 2006. After this, detection of CKD shot up and management, still far from ideal, has improved year on year. These changing circumstances almost certainly had some effect on the modelling.

That said, the prospect of being able to routinely identify and discuss with individual patients their own kidney risk in every general practice in the land is a major milestone. Activating patients, to use the chronic disease management jargon, will almost certainly be even more effective when their own laboratory data of risk, serum creatinine for eGFR and urinary albumin creatinine ratio are also available for the patient and the clinician. This a vital part of realising the “preventative dividend”. It sets the scene for the hard work of turning individuals’ knowledge and understanding of their kidney disease risks into the actions and behavioural changes to minimise those risks. Beginning the conversation about risk is the first part of the patient journey to the confidence to solve problems and taking control. The earlier that is started the better. QKidney also offers the prospect of integration with QRisk, the vascular risk predictor and the QD score for diabetes risk. That substantially increases the chances it would be taken up by busy GPs.

Is it a flight of fancy to think QKidney could be linked to individually held medical records in Google health or some other type of my-healthspace, perhaps through NHS Choices so that the public can look up their own risk profiles on the net at a time that suits them? And, if the risk is significant and the individual wants, they may be able to register for online coaching and support? Could we use the expertise that has been gained with Renal Patient View to make such a future a reality?