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

通过模拟模型预测慢性肾脏病以改善少数民族人群的健康

基本信息

批准号：
10087957
负责人：
ALEX BUI
金额：
$ 37.49万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-23 至 2023-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10087957
关键词：
Accounting Address Affect African American Area Behavior Behavioral Big Data Chronic Kidney Failure Clinical Data Data Set Databases Development Diabetes Mellitus Discipline Disease Management Disease Progression Economic Burden Education Educational Background Electronic Health Record Emergency Care End stage renal failure Engineering Ensure Ethnic group Focus Groups Foundations Future Geography Glomerular Filtration Rate Goals Health Health Planning Health Policy Health system Healthcare High Prevalence Hypertension Individual Institution Insurance Coverage Intervention Intervention Studies Joints Laboratories Life Expectancy Machine Learning Medicare Methods Minority Groups Modeling Outcome Patient Care Patients Pattern Performance Pharmaceutical Preparations Physical environment Population Population Heterogeneity Population Statistics Predictive Factor Prevalence Primary Care Physician Quality of life Race Registries Renal function Research Resources Risk Risk Factors Social Environment Socioeconomic Status System Techniques Testing Urban Health Validation base beneficiary clinical decision support clinical implementation clinical translation cohort combinatorial cost data registry design disease disparity disease registry disorder risk disparity reduction ethnic difference ethnic diversity ethnic minority population health data health disparity high risk improved individualized medicine innovation insight large datasets minority health model development models and simulation modifiable risk mortality risk novel population health precision medicine racial and ethnic

项目摘要

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.

项目总结/文摘