Online Program

Testing of Prediction Models for End Stage Kidney Disease Patient Nonadherence to Renal Replacement Treatment Regimens (303440)

In patients with end stage kidney disease (ESKD), renal replacement via hemodialysis (HD) treatments three times per week assumes some functions of the diseased kidney by filtering the body’s toxins from the blood, and is required to sustain life. Nonadherence with HD regimens is known to be associated with increased morbidity and mortality. This endeavor aimed to utilize clinical and nonclinical data to develop and test predictive models (PMs) that identify patients with a high probability of not attending their HD treatments within the following week. Data from Fresenius Medical Care patients during 2014 was analyzed. Various PMs were investigated including generalized linear model, partitioning and regression trees, artificial neural networks, and generalized additive model (GAM). 1,554,833 clinical records stratified in weekly intervals on 60 variables from 172,854 patients was utilized for development and testing. The GAM had the highest performance, with an average area under the curve (AUC) of 0.87 for five multitier models utilizing a 30% test data set. This analysis demonstrates that PMs can assist in identifying patients with a high probability for missing HD treatments.