Validation of Survey Questions to Distinguish Type 1 and Type 2 Diabetes Among Adults With Diabetes

验证成人糖尿病患者中区分 1 型和 2 型糖尿病的调查问题

• 批准号: 9438672
• 负责人: Marian Sue Kirkman
• 金额: $ 49.9万
• 依托单位: WESTAT, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2019-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9438672
• 关键词: Validation; Survey; Questions; Distinguish; Type

PROJECT SUMMARY/ABSTRACT Type 1 and Type 2 diabetes are distinct clinical conditions with different etiologies, ages of onset, management strategies, risk factors, and outcomes. Currently, the data sources that the Centers for Disease Control and Prevention (CDC) relies upon to monitor trends in diabetes prevalence and incidence are unable to reliably distinguish between types of diabetes. Most of the large federal surveys used for diabetes surveillance have not included questions on diabetes type, and few studies have reported survey-based algorithms for identifying diabetes type. None have compared survey-based identification algorithms with a gold standard case ascertainment in order to validate survey-based assignment of diabetes type. Methods for distinguishing between diabetes types in electronic health records (EHR) data have been tested for children and adults, but additional validation work is needed. Our approach to improving diabetes surveillance in these two areas is based on an integrated study design whereby survey data for diabetes patients are linked with data from their EHR and a gold standard case ascertainment derived from chart review. We will select a diverse sample of diabetes patients that is designed and powered to assess algorithm validity for subpopulations defined by age, sex, and race/ethnicity. We will use rigorous questionnaire development methods to build on items used in previous surveys, cognitively test the new survey module to optimize wording and question order, field the survey using data collection methods similar to established CDC surveys, and analyze the responses relative to a gold standard classification of diabetes type. In addition, we will develop and validate against the gold standard EHR-based algorithms, including a modeling approach that produces a parsimonious rule-based algorithm for determining diabetes type based on the most important clinical variables, and a machine learning approach that uses the gold standard dataset as a starting point to identify implicit patterns that distinguish T1DM and T2DM.

项目摘要/摘要 1型糖尿病和2型糖尿病是两种不同的临床症状，具有不同的病因、发病年龄、治疗方法 战略、风险因素和结果。目前，疾病控制中心和 预防(CDC)依赖于监测糖尿病患病率和发病率的趋势无法可靠地 区分糖尿病的类型。大多数用于糖尿病监测的大型联邦调查都有 没有包括糖尿病类型的问题，而且很少有研究报告基于调查的算法来识别 糖尿病类型。没有人将基于调查的识别算法与黄金标准案例进行比较 以确认基于调查的糖尿病类型分配。区分的方法 电子健康记录(EHR)数据中的糖尿病类型已经针对儿童和成人进行了测试，但 还需要额外的验证工作。我们在这两个领域改善糖尿病监测的方法是 基于一项综合研究设计，将糖尿病患者的调查数据与其 电子健康记录和从图表审查中得出的黄金标准案例确定。我们将选择不同的样本 糖尿病患者被设计并支持评估按年龄定义的亚群的算法有效性， 性别，以及种族/民族。我们将使用严格的问卷开发方法来构建 以前的调查，认知测试新的调查模块，以优化措辞和问题顺序，现场 使用与已建立的疾控中心调查类似的数据收集方法进行调查，并分析相关答复 糖尿病类型的黄金标准分类。此外，我们将针对黄金进行开发和验证 标准的基于EHR的算法，包括一种建模方法，该方法产生基于简约规则的 基于最重要的临床变量确定糖尿病类型的算法和机器学习 一种使用黄金标准数据集作为起点来识别区分以下内容的隐式模式的方法 T1 DM和T2 DM。