A SYSTEM DYNAMICS APPROACH TO OPTIMIZING RESOURCE ALLOCATION IN KIDNEY DIALYSIS VERSUS TRANSPLANTATION

Purpose:

To provide clinicians and health care administrators with a greater understanding of the combined costs associated with the many critical care pathways associated with End Stage Renal Disease (ESRD).

Method:

A system dynamics model was designed to simulate over time the total expenses of a multitude of ESRD treatments in the United States, including incidence and mortality rates associated with different critical care pathways: kidney transplant, hemodialysis, peritoneal dialysis, and conservative care.  Calibrated to United States Renal Data System (USRDS) 2013 Annual and Historical Data Report and the U.S Census Bureau for the years 2005 to 2010, encompassing all ESRD patients under treatment in the US from 2005 to 2010, the ESRD Population Model predicted the growth and costs of ESRD treatment populations using historical patterns that were, in turn, projected over a specified future time horizon.

Result:

The model was calibrated to and compared against the output of the USRD’s own predictions for the year 2020, and tested by running historic scenarios and comparing the output to existing data. Using a web interface designed to allow users to alter certain combinations of parameters using "sliders", alternative scenarios were demonstrated to run to predict future spending, incidence and mortalities according to defined causal links and combinations of critical care pathway permutations.  These scenarios included: a doubling of kidney donations and transplant rates, a marked increased in the offering of peritoneal dialysis, and an increase in conservative care routes for patients over 65. 

Conclusion:

System dynamics modeling allows for a complexity of causal links to be incorporated into a predictive cost-effectiveness analysis based on survival, treatments, retreatments and total cost. With the high cost of dialysis and rising interest in early transplantation, this method may be useful to informing clinical practice, public policy and future modeling research.