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

TL;DR

This study develops a machine learning model that predicts the need for renal replacement therapy in CKD patients up to one year in advance using claims data, aiming to enable early intervention and improve patient outcomes.

Contribution

The paper introduces a novel dynamic machine learning approach for predicting RRT onset in CKD patients using claims data, filling a gap in existing predictive tools.

Findings

Model achieves over 90% sensitivity and specificity.

Predictions made for approximately 3 million Medicare beneficiaries.

Provides a foundation for early screening and intervention.

Abstract

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.