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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：
10523518
负责人：
ALEX BUI
金额：
$ 37.44万
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 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10523518
关键词：
Accounting Address Affect African American population Area Behavior Behavioral Big Data Caring Chronic Kidney Failure Clinical Data Data Set Databases Development Diabetes Mellitus Discipline Disease Management Disease Progression Disparity Economic Burden Education Educational Status Electronic Health Record Emergency Care End stage renal failure Engineering Ensure Ethnic Origin Ethnic Population 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 Kidney Diseases 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 beneficiary clinical decision support clinical implementation clinical translation cohort combinatorial cost data registry design disease disparity disease registry disorder risk disparity reduction electronic health database ethnic difference ethnic diversity ethnic minority health data health disparity high risk improve minority health improved individualized medicine innovation insight large datasets machine learning method model development models and simulation modifiable risk mortality risk novel population health precision medicine racial difference racial population

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.

项目概要/摘要慢性肾脏病 (CKD)、CKD 进展和终末期肾病方面存在显着的健康差异不同种族人群中的疾病（ESRD）。非裔美国人 (AA) 的患病率高出约 25% CKD、ESRD 发生率高出 3 倍，估计肾小球肾病患者的死亡风险最高滤过率（eGFR）45-95mL/min/1.73m2。 CKD 和 ESRD 最重要的传统风险因素是糖尿病和高血压分别占新发 ESRD 病例的 60% 以上和 70% 以上。非- 慢性肾病的传统危险因素，如环境、文化行为因素、地理、教育、保险覆盖范围、社会经济地位以及获得最佳医疗保健的机会不平等，对少数民族的 CKD 健康状况。这些因素在现实世界中对 CKD 进展的独特组合仍然不明确。识别可能减少 CKD 差异的可改变风险因素对于提高生活质量、预期寿命和减轻经济负担具有不可估量的价值。仿真模型有已成功应用于其他临床领域，但在 CKD 发生和 CKD 进展方面受到限制，由于数据集较小且缺乏使用纵向观察健康数据的建模技术。此外，还没有在现实世界的少数群体中测试任何模型来揭示干预的潜力可以更大规模地减少 CKD 差异的研究。据我们所知，我们已经创建了最大的 2006 年至 2016 年间超过 1000 万人的电子健康记录的综合数据库加州大学洛杉矶分校 (UCLA)（180 万）和普罗维登斯圣约瑟夫健康中心（PSJH；920 万）系统之间建立了为期 2 年的合作伙伴关系。从 UCLA 登记人群中，我们发现 AA 之间 eGFR 轨迹下降存在显着差异和非AAs根据基线eGFR，表明eGFR从较高到较低、更陡峭的模式转变轨迹表明，干预措施可能存在关键窗口期，以减少 AA 中的 CKD 差异。即使在控制了已知影响 eGFR 轨迹的人口统计和临床变量。我们假设使用加州大学洛杉矶分校 PSJH CKD/高危 CKD 联合登记处的种族多样化人群可以识别出一种新的组合 CKD 危险因素；并提高现有模拟模型的性能以预测 CKD 进展。这具体目标是：1) 开发和测试基于机器学习的 CKD 和 eGFR 模拟模型使用 UCLA PSJH CKD/高危 CKD 登记处的轨迹；并对模型进行内部验证与现有CKD风险模型的比较，2）基于不同种族/民族的分层和测试模拟模型小组，包括基于跨机构比较的外部验证，以及 3) 开展焦点小组加州大学洛杉矶分校的初级保健医生负责管理种族/族裔患者，以征求他们对现有和设计模拟模型以减少 CKD 健康差异。这些创新方法将有利于我们长期目标是为临床决策支持方法提供信息，以减少/消除 CKD 健康差异。