Towards the Best Kidney Failure Prediction Tool

A Systematic Review and Selection Aid

Chava L. Ramspek; Ype de Jong; Friedo W. Dekker; Merel van Diepen


Nephrol Dial Transplant. 2020;35(9):1527-1538. 

In This Article


Chronic kidney disease (CKD) may lead to kidney failure, although the rates of progression vary substantially between individuals.[1] Prediction tools that can identify patients at high risk of developing kidney failure could have great clinical value. They could be used to inform individualized decision making, employed in determining the appropriate time for referral to nephrologists and used in the planning and preparation of renal replacement therapy (RRT). Prediction tools might also offer opportunities for risk stratification in research and improvement of health policies.[2]

Multiple prediction models have been developed to identify individuals at high risk of kidney failure and have been previously described in two systematic reviews.[3,4] Many of these models showed good predictive abilities in development. However, despite nephrologists and patients acknowledging a lack of prognosis discussions in practice, clinical uptake of these tools is still limited.[5] Policymakers also seem hesitant in endorsing prediction tools. The most recent Kidney Disease: Improving Global Outcomes guideline recommends the use of prediction models for timely referral for planning RRT.[6] However, the guideline, fails to provide guidance on which risk prediction tool should be used to do so. The lack of uptake by clinicians and policymakers has been partly attributed to substandard methodology, a lack of external validation and a shortage of easy calculation options.[7]

The last two published reviews in 2012 and 2013 included eight studies each on prediction of kidney failure in CKD patients.[3,4] Since then the number of available models has greatly increased. A new systematic review of the available models is the first step towards the use and recommendation of robust prognostic tools. The aim of the current study is therefore to systematically review all available models predicting kidney failure in CKD patients, organize empirical evidence on their validity and ultimately provide guidance in the selection of the best prediction tool for various settings.