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Chronic kidney disease (CKD) represents a slowly progressive disorder thatcan eventually require renal replacement therapy (RRT) including dialysis orrenal transplantation. Early identification of patients who will require RRT(as much as 1 year in advance) improves patient outcomes, for example byallowing higher-quality vascular access for dialysis. Therefore, earlyrecognition of the need for RRT by care teams is key to successfully managingthe disease. Unfortunately, there is currently no commonly used predictive toolfor RRT initiation. In this work, we present a machine learning model thatdynamically identifies CKD patients at risk of requiring RRT up to one year inadvance using only claims data. To evaluate the model, we studied approximately3 million Medicare beneficiaries for which we made over 8 million predictions.We showed that the model can identify at risk patients with over 90%sensitivity and specificity. Although additional work is required before thisapproach is ready for clinical use, this study provides a basis for a screeningtool to identify patients at risk within a time window that enables earlyproactive interventions intended to improve RRT outcomes.

Machine learning for dynamically predicting the onset of renal replacement therapy in chronic kidney disease patients using claims data