Prediction models for the mortality risk in chronic dialysis patients: a systematic review and independent external validation study

摘要

Objective: In medicine, many more prediction models have been developed than are implemented or used in clinical practice. These models cannot be recommended for clinical use before external validity is established. Though various models to predict mortality in dialysis patients have been published, very few have been validated and none are used in routine clinical practice. The aim of the current study was to identify existing models for predicting mortality in dialysis patients through a review and subsequently to externally validate these models in the same large independent patient cohort, in order to assess and compare their predictive capacities. Methods: A systematic review was performed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. To account for missing data, multiple imputation was performed. The original prediction formulae were extracted from selected studies. The probability of death per model was calculated for each individual within the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD). The predictive performance of the models was assessed based on their discrimination and calibration. Results: In total, 16 articles were included in the systematic review. External validation was performed in 1,943 dialysis patients from NECOSAD for a total of seven models. The models performed moderately to well in terms of discrimination, with C -statistics ranging from 0.710 (interquartile range 0.708–0.711) to 0.752 (interquartile range 0.750–0.753) for a time frame of 1 year. According to the calibration, most models overestimated the probability of death. Conclusion: Overall, the performance of the models was poorer in the external validation than in the original population, affirming the importance of external validation. Floege et?al’s models showed the highest predictive performance. The present study is a step forward in the use of a prediction model as a useful tool for nephrologists, using evidence-based medicine that combines individual clinical expertise, patients’ choices, and the best available external evidence.