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New Risk Models for Diabetes Complications Using Electronic Health Records

使用电子健康记录的糖尿病并发症的新风险模型

基本信息

批准号：
10597118
负责人：
Claudia Leonie Nau
金额：
$ 69.14万
依托单位：
KAISER FOUNDATION RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-01 至 2026-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10597118
关键词：
African American African American population African Caribbean Amputation Asian Asian Americans Asian Indian Behavior Therapy Behavioral Biological Black race Blindness British California Cessation of life Characteristics Chronic Disease Complications of Diabetes Mellitus Congestive Heart Failure Cost Effectiveness Analysis Data Data Set Diabetes Mellitus Diabetic Retinopathy Diagnosis Disease Progression Disparity East Asian Education Electronic Health Record Equation Equity Ethnic Origin Ethnic Population Event Exercise Goals Health Health behavior Healthcare Systems Hispanic Hispanic Americans Incidence Individual Information Systems Integrated Health Care Systems Kidney Failure Left Methodology Modeling Myocardial Infarction Myocardial Ischemia Needs Assessment Non-Insulin-Dependent Diabetes Mellitus Outcome Outcome Study Patient Education Patient Self-Report Patients Persons Physical activity Play Population Population Group Population Heterogeneity Preparation Prevalence Race Reporting Risk Risk Estimate Role Sampling Sampling Studies Services Stroke Subgroup Time Ulcer United Kingdom United States Update Validation Weight maintenance regimen clinical decision support cohort cost diabetes education diabetes management diabetes risk diabetic ulcer diet and exercise experience follow-up health equity improved lifetime risk member models and simulation mortality nutrition outcome prediction prospective racial difference racial population risk stratification secondary analysis semiparametric social sociodemographics

项目摘要

Abstract Diabetes incidence and prevalence remain at record highs in the United States. Understanding diabetes disease progression and how it varies among America’s heterogeneous population is critical, given unequal risks and outcomes for individuals of different racial/ethnic groups. Diabetes outcome prediction and simulation models allow prediction of a person’s risk for diabetes complications and death. A recent review of 19 such models found that the majority—16 models—relied at least partly on transition functions developed by the United Kingdom Prospective Diabetes Study (UKPDS). The UKPDS draws on data from a trial that began in 1977 and involved 5100 patients who were followed for a total of 89,760 person years. The sample consisted of mostly white British citizens. Only 8% and 10% of the UKPDS sample were Indian Asian and Afro- Caribbean patients, respectively. The major racial/ethnic groups that make up the US population were not included, and the variables studied in the UKPDS did not include any behavioral data. Long term, longitudinal patient data on diabetes outcomes is costly to collect and all information on the UKPDS Outcomes Models has been transparently reported and made publicly available. This has left the UKPDS risk models as the best option for many risk engines, despite the small, dated and nondiverse sample that it is based on. Capitalizing on Kaiser Permanente Southern California (KPSC) Electronic Health Records (EHR) data and legacy data systems, we identified over 527,000 patients with incident diabetes that were diagnosed and treated at KPSC from 1993 to 2020. Our sample provides more than 4.4 million person-years of follow up. More than 34,000 patients could be followed up for 21 or more years. The incident diabetes cohort from KPSC is 34.4% Hispanic, 10.6% Asian and 12.7% African American or Black allowing us to update the risk equations for all UKPDS outcome models by major race-ethnicity groups directly relevant for the U.S population. These updated models will allow us to identify disparities in diabetes, assure statistical fairness, and improve prediction of diabetes outcomes for diverse population groups. Because diabetes outcomes are largely influenced by health behaviors, we will also analyze behavioral data captured in the EHR including data on exercise and referrals to diabetes and weight management education classes. We will use cutting edge parametric, semi-parametric and non-parametric models to re- estimate risk equations using standard split sample cross-validation. We will report our methodology and results transparently in the same format as the UKPDS. Our study will help to update existing simulation models and support more timely and equitable clinical decision support and patient education.

摘要在美国，糖尿病的发病率和患病率仍然处于历史最高水平。理解糖尿病疾病的进展及其在美国异质人群中的变化是至关重要的，不同种族/族裔群体的个人面临的风险和结果不平等。糖尿病预后预测和模拟模型可以预测一个人患糖尿病并发症和死亡的风险。最近检讨 19个这样的模型发现，大多数（16个模型）至少部分依赖于由英国糖尿病前瞻性研究（UKPDS）UKPDS利用了一项试验的数据，在1977年，有5100名患者被随访，总随访时间为89,760人年。样品主要由白色英国公民组成。只有8%和10%的UKPDS样本是印度亚洲人和非洲人，加勒比海患者。构成美国人口的主要种族/民族群体没有在UKPDS中研究的变量不包括任何行为数据。长期，纵向收集糖尿病患者结局数据的成本很高，UKPDS结局模型的所有信息透明地报告并公开。这使得UKPDS风险模型成为最好的许多风险引擎的选择，尽管它是基于小，过时和非多样化的样本。利用Kaiser Permanente Southern加州（KPSC）电子健康记录（EHR）数据和遗留数据系统，我们确定了超过527，000名被诊断患有糖尿病的患者，从1993年到2020年在KPSC接受治疗。我们的样本提供了超过440万人年的随访。超过34，000名患者可以随访21年或更长时间。来自KPSC的新发糖尿病队列是34.4%的西班牙裔，10.6%的亚洲和12.7%的非洲裔美国人或黑人，使我们能够更新风险方程，对于所有UKPDS结局模型，主要种族-民族组与美国人口直接相关。这些更新的模型将使我们能够识别糖尿病的差异，确保统计公平性，并改善预测不同人群的糖尿病结局。由于糖尿病的结果在很大程度上受健康行为的影响，我们还将分析行为 EHR中采集的数据，包括运动数据、糖尿病转诊数据和体重管理数据教育类。我们将使用最先进的参数，半参数和非参数模型，以重新使用标准分割样本交叉验证估计风险方程。我们将报告我们的方法，结果透明，格式与UKPDS相同。我们的研究将有助于更新现有的模拟模型和支持更及时和公平的临床决策支持和患者教育。