Improved Mortality Prediction in Dialysis Patients Using Specific Clinical and Laboratory Data

Background: Risk prediction models can be used to inform patients undergoing renal replacement therapy about their survival chances. Easily available predictors such as registry data are most convenient, but their predictive value may be limited. We aimed to improve a simple prediction model based on registry data by incrementally adding sets of clinical and laboratory variables. Methods: Our data set includes 1,835 Dutch patients from the Netherlands Cooperative Study on the Adequacy of Dialysis. The potential survival predictors were categorized on availability. The first category includes easily available clinical data. The second set includes laboratory values like albumin. The most laborious category contains glomerular filtration rate (GFR) and Kt/V. Missing values were substituted using multiple imputation. Within 1,225 patients, we recalibrated the registry model and subsequently added parameter sets using multivariate Cox regression analyses with backward selection. On the other 610 patients, calibration and discrimination (C-index, integrated discrimination improvement (IDI) index and net reclassification improvement (NRI) index) were assessed for all models. Results: The recalibrated registry model showed adequate calibration and discrimination (C-index = 0.724). Adding easily available parameters resulted in a model with 10 predictors, with similar calibration and improved discrimination (C-index = 0.784). The IDI and NRI indices confirmed this, especially for short-term survival. Adding laboratory values resulted in an alternative model with similar discrimination (C-index = 0.788), and only the NRI index showed minor improvement. Adding GFR and Kt/V as candidate predictors did not result in a different model. Conclusion: A simple model based on registry data was enhanced by adding easily available clinical parameters.