Towards the best kidney failure prediction tool: a systematic review and selection aid

Abstract

Background: Prediction tools that identify chronic kidney disease (CKD) patients at a high risk of developing kidney failure have the potential for great clinical value, but limited uptake. The aim of the current study is to systematically review all available models predicting kidney failure in CKD patients, organize empirical evidence on their validity and ultimately provide guidance in the interpretation and uptake of these tools.

Methods: PubMed and EMBASE were searched for relevant articles. Titles, abstracts and full-text articles were sequentially screened for inclusion by two independent researchers. Data on study design, model development and performance were extracted. The risk of bias and clinical usefulness were assessed and combined in order to provide recommendations on which models to use.

Results: Of 2183 screened studies, a total of 42 studies were included in the current review. Most studies showed high discriminatory capacity and the included predictors had large overlap. Overall, the risk of bias was high. Slightly less than half the studies (48%) presented enough detail for the use of their prediction tool in practice and few models were externally validated.

Conclusions: The current systematic review may be used as a tool to select the most appropriate and robust prognostic model for various settings. Although some models showed great potential, many lacked clinical relevance due to being developed in a prevalent patient population with a wide range of disease severity. Future research efforts should focus on external validation and impact assessment in clinically relevant patient populations.

Keywords: kidney failure; prediction model; prognostic; systematic review.

FIGURE 1

PRISMA flow diagram of study inclusion.

FIGURE 2

Cumulative number of published development and validation studies for models that predict kidney failure in CKD patients (N = 42).

FIGURE 3

Predictors included in development studies (N = 35). The inclusion of a predictor is shown as ‘X’. The subscript under X (e.g. ‘X₂’) indicates the number of predictors included from that category.

FIGURE 4

(A) Risk of bias and usability of prediction models (N = 42). Assessed using the PROBAST. The five risk of bias domains were evaluated as low risk (+), unclear risk (?) or high risk (−). Usability was evaluated as yes (+) or no (−). (B) PROBAST risk of bias summary for all studies (N = 42).

FIGURE 5

Model selection guide for CKD patients. In this graph, only models that allow calculation of an individual’s prognosis and are therefore labelled as usable are included. This entails that these models provide either a full formula, score with absolute risk table or (currently working) web calculator for a specified prediction time frame. For categories containing multiple models, the risk of bias combined with evidence of external validity was weighed in determining the model order, starting with the most valid and least biased models. Nevertheless, many of the models listed have significant shortcomings and should be used with caution.