Using machine learning to predict optimal renal replacement therapy starts in patients with advanced renal function loss
Using machine learning to predict optimal renal replacement therapy starts in patients with advanced renal function loss
Authors: Ollie Fielding, Chris Kipers, Jung Hoon Son, Edward Lee, Daniel Levine, Thomas Parker, Barry H. Smith, Jeffrey I. Silberzweig
Institutions: pulseData, Inc. New York, NY, United States. The Rogosin Institute, New York, NY, United States.
Background
Building on our previous work using machine learning techniques to identify patients at risk of progression to End Stage Renal Disease (ESRD), we focused this model more precisely on identifying patients with advanced kidney function loss who should plan for optimal renal replacement therapy. The most recent USRDS data indicates that more than 80% of patients begin hemodialysis with a catheter and only 2.5% receive preemptive renal transplants.
Methods
Using longitudinal patient data of 109,028 patients from The Rogosin Institute, we identified a cohort of patients with advanced kidney function loss, defined as an eGFR
Results
Between 2014 and 2016, only 17 of 214 patients who progressed within a six month period received an AV fistula prior to their decline to an eGFR
Conclusion
We demonstrate improved ability to identify patients who will need renal replacement therapy using an advanced machine learning model incorporating longitudinal data commonly available in EHRs. We plan to augment clinical decision making with machine learning tools.
Using machine learning to predict optimal renal replacement therapy starts in patients with advanced renal function loss
Building on our previous work using machine learning techniques to identify patients at risk of progression to End Stage Renal Disease (ESRD), we focused this model more precisely on identifying patients with advanced kidney function loss who should plan for optimal renal replacement therapy. The most recent USRDS data indicates that more than 80% of patients begin hemodialysis with a catheter and only 2.5% receive preemptive renal transplants.
Using machine learning to predict optimal renal replacement therapy starts in patients with advanced renal function loss
Authors: Ollie Fielding, Chris Kipers, Jung Hoon Son, Edward Lee, Daniel Levine, Thomas Parker, Barry H. Smith, Jeffrey I. Silberzweig
Institutions: pulseData, Inc. New York, NY, United States. The Rogosin Institute, New York, NY, United States.
Background
Building on our previous work using machine learning techniques to identify patients at risk of progression to End Stage Renal Disease (ESRD), we focused this model more precisely on identifying patients with advanced kidney function loss who should plan for optimal renal replacement therapy. The most recent USRDS data indicates that more than 80% of patients begin hemodialysis with a catheter and only 2.5% receive preemptive renal transplants.
Methods
Using longitudinal patient data of 109,028 patients from The Rogosin Institute, we identified a cohort of patients with advanced kidney function loss, defined as an eGFR
Results
Between 2014 and 2016, only 17 of 214 patients who progressed within a six month period received an AV fistula prior to their decline to an eGFR
Conclusion
We demonstrate improved ability to identify patients who will need renal replacement therapy using an advanced machine learning model incorporating longitudinal data commonly available in EHRs. We plan to augment clinical decision making with machine learning tools.
