Prediction of Chronic Kidney Disease by Simulation Modeling to Improve the Health of Minority Populations (R01 MD014712; PI: Nicholas, Bui)
Significant health disparities exist in chronic kidney disease (CKD), CKD progression, and end stage renal disease (ESRD) in ethnically diverse populations. African Americans (AAs) have approximately 25% higher prevalence of CKD, and 3x higher rates of ESRD compared to their counterparts. Moreover, a portion of these individuals exhibit rapid decline, progressing much faster than other comparable patients. Using a unique repository build from the electronic health records of >10 million individuals seen at UCLA and Providence Health System, we are building new predictive models to better understand CKD progression and issues related to AAs. Using machine learning (ML)-based methods, we are developing CKD and eGFR trajectory models over time, stratified across age, gender, and different racial/ethnic groups. These models will provide insight into the factors that influence CKD, resulting in worsening outcomes. To ensure that these models translate into effective interventions, we are also conducting focus groups with primary care physicians to elicit their perspectives on existing and designed models to reduce CKD health disparities.