Machine learning for dynamically predicting the onset of renal replacement therapy in chronic kidney disease patients using claims data
This work addresses the lack of predictive tools for RRT initiation in CKD patients, potentially improving patient outcomes through early intervention, though it is incremental as it applies existing machine learning methods to a new clinical application.
The authors tackled the problem of predicting the need for renal replacement therapy (RRT) in chronic kidney disease patients up to one year in advance using claims data, achieving over 90% sensitivity and specificity in a study of approximately 3 million Medicare beneficiaries.
Chronic kidney disease (CKD) represents a slowly progressive disorder that can eventually require renal replacement therapy (RRT) including dialysis or renal transplantation. Early identification of patients who will require RRT (as much as 1 year in advance) improves patient outcomes, for example by allowing higher-quality vascular access for dialysis. Therefore, early recognition of the need for RRT by care teams is key to successfully managing the disease. Unfortunately, there is currently no commonly used predictive tool for RRT initiation. In this work, we present a machine learning model that dynamically identifies CKD patients at risk of requiring RRT up to one year in advance using only claims data. To evaluate the model, we studied approximately 3 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 this approach is ready for clinical use, this study provides a basis for a screening tool to identify patients at risk within a time window that enables early proactive interventions intended to improve RRT outcomes.