Prediction of Chronic Kidney Disease by Simulation Modeling to Improve the Health of Minority Populations

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Project Summary/Abstract 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 ~25% higher prevalence of CKD, 3-fold higher rate of ESRD, and the highest risk of mortality among those with estimated glomerular filtration rate (eGFR) 45-95mL/min/1.73m2. The most significant traditional risk factors for CKD and ESRD are diabetes and hypertension accounting for >60% CKD and >70% of new ESRD cases, respectively. Non- traditional risk factors for CKD such as environmental, cultural-behavioral factors, geographic, education, insurance coverage, socioeconomic status and unequal access to optimal healthcare, disproportionately affect CKD health in ethnic minorities. The unique combination of these factors on CKD progression in the real world remains poorly defined. Identification of modifiable risk factors that may reduce CKD disparities would be invaluable to improve quality of life, life expectancy, and decrease economic burden. Simulation models have been successfully applied in other clinical domains, but are limited in CKD development and CKD progression, due to small datasets and the absence of modeling techniques using longitudinal observational health data. Further, no models have been tested in a real-world minority population to uncover the potential for interventional studies that would reduce CKD disparities on a larger scale. To our knowledge, we have created the largest, comprehensive database from electronic health records of >10 million individuals seen between 2006-2016 from a 2-year partnership between UCLA (1.8 million) and Providence St. Joseph Health (PSJH; 9.2 million) systems. From the UCLA Registry population, we identified significant differences in eGFR trajectory decline between AAs and non-AAs according to baseline eGFR, indicating a pattern shift from a higher to a lower, steeper eGFR trajectory suggesting there may be critical windows for interventions to reduce CKD disparities in AAs. Race/ethnicity differences from linear mixed models of all ethnic cohorts persisted even after controlling for demographic and clinical variables known to influence eGFR trajectories. We hypothesize that the use of ethnically diverse populations in the joint UCLA PSJH CKD/At-risk CKD Registry can identify a novel combination of CKD risk factors; and improve the performance of existing simulation models to predict CKD progression. The specific aims are to: 1) develop and test a machine learning-based simulation model for CKD and eGFR trajectories using the UCLA PSJH CKD/At-risk CKD Registry; and conduct internal validation of the models and comparisons with existing CKD risk models, 2) stratify and test simulation models based on different racial/ethnic groups, including external validation based on cross-institution comparisons, and 3) conduct focus groups with UCLA primary care physicians, who manage racial/ethnic patients, to elicit their perspectives on existing and designed simulation models to reduce CKD health disparities. These innovative approaches will facilitate our long-term goal to inform clinical decision support methods to reduce/eliminate CKD health disparities.