Tiffany C.E. Veinot

Predictive Analytic and Geospatial Approaches to Enable Targeted Prevention and Slowing of the Progression of Kidney Disease among US Veterans

Research Project Description
Rajiv Saran, Tiffany C.E. Veinot, Vahakn Bedig Shahinian, VG Vinod Vydiswaran, Karandeep Singh, Jennifer Lynn Bragg-Gresham, Brenda Wilson Gillespie

?) The overall goal of this proposal is to reduce the incidence and progression of kidney disease among veterans by leveraging and taking to the next level, a data system developed by our team (VA Renal Information System?VA REINS)1. This system is designed to facilitate surveillance, population health management, and quality improvement for kidney disease among veterans. Our team at the University of Michigan (UM) built the VA REINS from 2012-20161 (Contract No. VA118-12-C-0043). This system processes data in the VA's Corporate Data Warehouse (CDW) and links it to key non-VA data sources, e.g., Organ Procurement and Transplantation Network [OPTN], and CMS data, including CROWNWeb. Based upon this pioneering work, we now propose a novel, targeted population health approach to make this information system useful for both primary and secondary prevention of chronic kidney disease (CKD) among veterans. In this work we will develop predictive analytics (using traditional statistical, geospatial, and machine learning tools) to identify high risk individuals to better understand underlying risk and progression factors for kidney disease. This will set the stage for enabling targeted population health interventions to both prevent and slow progression of kidney disease among US veterans.

Veterans Affairs, Department of

Funding Period: 9/30/2018 to 9/29/2019

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