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

• 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
• 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

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.

抽象的 在美国，糖尿病的发病率和患病率仍然保持创纪录的高潮。理解 糖尿病疾病进展及其在美国异质种群中的变化至关重要 不同种族/种族群体个人的不平等风险和结果。糖尿病结果预测和 模拟模型可以预测一个人患糖尿病并发症和死亡的风险。最近对 19这样的模型发现，大多数（16个模型）至少部分地归于由 英国前瞻性糖尿病研究（UKPDS）。 UKPD借鉴了开始的试验数据 在1977年，涉及5100名患者，这些患者总计89,760年。样本 主要由白人英国公民组成。 UKPDS样本中只有8％和10％是印度亚洲和非洲人 加勒比患者分别。组成美国人口的主要种族/族裔不是 包括UKPDS中的变量不包括任何行为数据。长期，纵向 有关糖尿病结果的患者数据的收集成本很高，并且有关UKPDS结果的所有信息 被透明地报告并公开可用。这使UKPDS风险模型成为最好的 对于许多风险引擎，dospite的选项，其基于的小型，过时和非宇宙样本。 大写凯撒（Kaiser Permanente）南加州（KPSC）电子健康记录（EHR）数据 和传统数据系统，我们确定了527,000多名被诊断出和的患者 从1993年到2020年，在KPSC接受治疗。我们的样本提供了超过440万人的随访。 超过34,000名患者可以随访21岁以上。来自KPSC的事件糖尿病队列 是34.4％的西班牙裔，10.6％的亚洲人和12.7％的非裔美国人或黑人，使我们能够更新风险方程 对于所有与美国人口直接相关的主要种族种族群体的UKPDS结果模型。这些 更新的模型将使我们能够识别糖尿病中的分布，确保统计公平并改善 对潜水员人群群体的糖尿病结果的预测。 由于糖尿病结果在很大程度上受到健康行为的影响，因此我们还将分析行为 EHR中捕获的数据包括运动和转介到糖尿病和体重管理的数据 教育课。我们将使用尖端参数，半参数和非参数模型来重新 使用标准拆分样品交叉验证估算风险方程。我们将报告我们的方法论 结果与UKPD的格式透明。我们的研究将有助于更新现有的模拟 模型和支持更及时，公平的临床决策支持和患者教育。