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

项目概要/摘要 在慢性肾脏疾病（CKD）、CKD进展和终末期肾功能衰竭中存在显著的健康差异。 疾病（ESRD）在不同种族的人群。非裔美国人（AAs）的患病率高出约25%， CKD，ESRD的发生率高3倍，估计肾小球疾病的患者死亡风险最高。 滤过率（eGFR）45- 95 mL/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%和70%。非- 慢性肾脏病的传统风险因素，例如环境、文化行为因素、地理、教育， 保险覆盖率、社会经济地位和获得最佳医疗保健的不平等， 少数民族的CKD健康状况。这些因素在真实的世界中对CKD进展的独特组合 仍然定义不清。识别可能减少CKD差异的可改变的风险因素， 对提高生活质量、预期寿命和减轻经济负担非常宝贵。模拟模型具有 已成功应用于其他临床领域，但在CKD发展和CKD进展方面受到限制， 这是由于数据集较小以及缺乏使用纵向观察健康数据的建模技术。 此外，还没有模型在现实世界的少数群体中进行测试，以揭示干预的潜力。 研究将在更大范围内减少CKD差异。据我们所知，我们创造了最大的， 来自2006-2016年期间超过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（180万美元）和普罗维登斯圣约瑟夫健康（PSJH; 920万美元）系统之间的两年合作伙伴关系。 从UCLA登记研究人群中，我们发现AA之间eGFR轨迹下降存在显著差异 根据基线eGFR，非AA，表明eGFR从较高到较低、更陡的模式转变 轨迹表明，可能存在减少AA中CKD差异的干预措施的关键窗口。 来自所有种族队列的线性混合模型的种族/种族差异即使在控制了以下因素后仍然存在： 已知影响eGFR轨迹的人口统计学和临床变量。我们假设使用 UCLA PSJH CKD/高危CKD联合登记研究中的种族多样性人群可以识别一种新的组合 CKD风险因素;并改善现有模拟模型的性能，以预测CKD进展。的 具体目标是：1）开发和测试CKD和eGFR的基于机器学习的模拟模型 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）进行焦点小组， 加州大学洛杉矶分校的初级保健医生，谁管理种族/民族的病人，以了解他们对现有的和 设计模拟模型以减少CKD健康差异。这些创新的方法将有助于我们 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。

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.