Development and Validation of Mortality Risk Scores for Persons with End-Stage Kidney Disease

Fitting a mixture cure survival model results in two sets of estimated coefficients and standard errors. Summarizing this model geographically, such as across zip codes or counties, may benefit practitioners and policymakers. For instance, these summaries may be used to show spatial trends via visualizations. Summarizing the model output geographically involves two parts: (1) condensing a dataset spatially and (2) encapsulating a survival function via a single number resulting in the development of risk scores. In this work, several methods are explored to accomplish these two tasks. Estimating the concordance statistic for each model allows for comparison of these methods. The risk scores were developed for the United States Renal Data System data composed of 2,228,693 patients who received their first end-stage kidney disease (ESKD) treatment between the years 2000 and 2020. The developed risk scores are shown using maps of the United States and validated using the clinical measurements found within the ESKD dataset. Acknowledgments: The research reported in this abstract was supported by South Dakota State University, AIM-AHEAD Coordinating Center, award number OTA-21-017, and was, in part, funded by the National Institutes of Health Agreement No. 1OT2OD032581. The work is solely the responsibility of the authors and does not necessarily represent the official view of AIM-AHEAD or the National Institutes of Health. The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the U.S. government.