Predicting Diabetes Risk Using Glucose Data

使用血糖数据预测糖尿病风险

• 批准号: 9313248
• 负责人: Michael Edward Bowen
• 金额: $ 17.54万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-25 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9313248
• 关键词: Adult; Advisory Committees; Age; American; Assessment tool; Automation; Blood Glucose; Body mass index; Caring; Center for Translational Science Activities; Clinic; Clinical; Clinical Research; Collection; Communities; Complex; Computerized Medical Record; Cost of Illness; Data; Detection; Development; Development Plans; Diabetes Mellitus; Diagnosis; Diagnostic; Diagnostic tests; Early Diagnosis; Early treatment; Educational workshop; Eligibility Determination; Enrollment; Environment; Epidemic; Evaluation; Evidence based intervention; Fasting; Foundations; Frequencies; Funding; Glucose; Glycosylated hemoglobin A; Goals; Gold; Guidelines; Health; Health system; Healthcare; Hospitals; Individual; Intervention; Low income; Measures; Medical; Medical Informatics; Medical center; Mentors; Minority; Modeling; Non-Insulin-Dependent Diabetes Mellitus; OGTT; Outcome; Outcomes Research; Patient risk; Patient-Focused Outcomes; Patients; Pattern; Performance; Policies; Population; Positioning Attribute; Prediabetes syndrome; Preventive service; Primary Health Care; Provider; Race; Randomized Controlled Trials; Recording of previous events; Research; Research Personnel; Research Training; Retrospective cohort study; Risk; Risk Assessment; Risk Factors; Sampling; Specificity; System; Testing; Texas; Time; Training; United States Agency for Healthcare Research and Quality; United States National Institutes of Health; Universities; base; career; career development; clinical care; clinical development; clinical efficacy; clinical practice; comparative effectiveness; computerized; diabetes risk; effectiveness research; ethnic diversity; experience; high risk; implementation science; improved; improved outcome; innovation; multilevel analysis; novel; novel strategies; patient oriented; point of care; population health; predictive modeling; prospective; public health relevance; research study; safety net; screening; secondary outcome; sex; support tools; symposium; tool

DESCRIPTION (provided by applicant): Over 7 million people in the US have undiagnosed diabetes, and an additional 73 million have undiagnosed prediabetes. Although early diagnosis and treatment can improve health outcomes in both conditions, only half of individuals eligible for diabetes screening have been screened in the past 3 years. Automated, electronic medical record (EMR)-based diabetes risk assessment and systematic approaches to diabetes screening may improve screening rates. Although automation of current screening guidelines within EMRs is challenging, data suggest that a single random glucose value is a better predictor of diabetes than national screening guidelines (which are based on age, sex, race, body mass index, and other health conditions). However, the sensitivity of this approach to diagnose both prediabetes and diabetes is poor. Utilization of multiple glucose values over time - a patient's glucose history - may improve the sensitivity of random glucose screening strategies to detect undiagnosed prediabetes and diabetes. This proposal describes a career development plan that will prepare the candidate to become a successful independent investigator and attain his long-term career goal of becoming a national leader in the development and implementation of EMR interventions to improve the diagnosis and health outcomes of patients with type 2 diabetes. This proposed research strategy will develop a novel, computerized glucose history-based diabetes risk assessment tool and then utilize this tool to develop and implement clinical decision support in the EMR to promote diabetes screening in routine clinical practice. The PI's immediate goal is to use the longitudinal glucose values available within the EMR - the "glucose history" - to develop a random blood glucose (RBG)-based diabetes risk assessment tool and clinical decision support. To meet this goal, he has proposed a career development plan that integrates didactic coursework, participation in local and national conferences and workshops, and a progression of mentored research studies within the supportive research environment at University of Texas Southwestern Medical Center and Parkland Hospital in Dallas, TX. This environment includes a NIH-funded CTSA and Clinical and Translational Research Center, an AHRQ-funded Center for Patient-Centered Outcomes Research, and the Parkland Center for Clinical Innovation, an entity that does advanced healthcare analytics and predictive modeling with EMR data. The research aims of this project are to: 1) characterize the glucose history of patients without known diabetes using glucose data available in the EMR and describe associations between an abnormal glucose history and diabetes screening; 2) conduct a prospective diabetes screening study to develop and optimize performance of a RBG risk tool to identify cases of undiagnosed diabetes and prediabetes using the EMR glucose history; and 3) develop and assess the feasibility of EMR-enabled diabetes screening clinical decision support using the RBG risk tool from Aim 2. These research aims will serve as the platform for the career development plan and training aims which include: 1) training in applied medical informatics; 2) advanced quantitative analyses; 3) implementation science; and 4) comparative effectiveness research. Together, the research and training aims of the K23 proposal will provide the training, experience, and preliminary data for two R01 applications. One R01 will be a larger diabetes screening study with additional oral glucose tolerance testing based on Aim 2. The other R01 will be based on the pilot in Aim 3 and propose a fully-powered, multisite randomized controlled trial to assess the efficacy of the EMR-based diabetes screening tool and clinical decision support to identify cases of diabetes and prediabetes in clinical practice. The proposed research and training aims will strategically position the PI to become a leader in the development, implementation, and evaluation of evidence-based interventions to improve outcomes in type 2 diabetes. The innovative approach to diabetes risk identification and screening outlined in this K23 proposal has the potential for very high impact on clinical care, population health management, and national screening guideline policies for this very common, serious, and costly disease.

描述（由申请人提供）：美国有超过700万人患有未诊断的糖尿病，另有7300万人患有未诊断的前驱糖尿病。虽然早期诊断和治疗可以改善这两种情况下的健康结果，但在过去3年中，只有一半符合糖尿病筛查条件的人接受了筛查。基于电子病历（EMR）的自动化糖尿病风险评估和糖尿病筛查的系统方法可能会提高筛查率。尽管EMR中当前筛查指南的自动化具有挑战性，但数据表明，单个随机血糖值比国家筛查指南（基于年龄，性别，种族，体重指数和其他健康状况）更能预测糖尿病。然而，这种方法诊断前驱糖尿病和糖尿病的灵敏度很差。随着时间的推移，利用多个葡萄糖值-患者的葡萄糖史-可以提高随机葡萄糖筛查策略的灵敏度，以检测未诊断的糖尿病前期和糖尿病。该提案描述了一个职业发展计划，该计划将使候选人成为一名成功的独立研究者，并实现他的长期职业目标，即成为EMR干预措施的开发和实施的国家领导者，以改善2型糖尿病患者的诊断和健康结果。这项研究策略将开发一种新的，计算机化的基于血糖史的糖尿病风险评估工具，然后利用这个工具来开发和实施临床决策支持的电子病历，以促进糖尿病筛查在常规临床实践。PI的近期目标是使用EMR中可用的纵向葡萄糖值-“葡萄糖历史”-开发基于随机血糖（RBG）的糖尿病风险评估工具和临床决策支持。为了实现这一目标，他提出了一个职业发展计划，整合教学课程，参加地方和国家会议和研讨会，并在德克萨斯州西南医学中心和达拉斯的帕克兰医院的支持性研究环境中进行指导研究。该环境包括NIH资助的CTSA和临床与转化研究中心，AHRQ资助的以患者为中心的成果研究中心，以及帕克兰临床创新中心，该中心是一个利用EMR数据进行高级医疗保健分析和预测建模的实体。该项目的研究目的是：1）使用EMR中可用的葡萄糖数据描述未患已知糖尿病的患者的葡萄糖史，并描述异常葡萄糖史与糖尿病筛查之间的关联; 2）进行前瞻性糖尿病筛查研究，以开发和优化RBG风险工具的性能，从而使用EMR葡萄糖史识别未诊断的糖尿病和前驱糖尿病病例;以及3）使用目标2的RBG风险工具开发和评估EMR使能的糖尿病筛查临床决策支持的可行性。这些研究目标将作为职业发展计划和培训目标的平台，包括：1）应用医学信息学培训; 2）高级定量分析; 3）实施科学; 4）比较有效性研究。K23项目的研究和培训目标将为两个R 01应用提供培训、经验和初步数据。一项R 01将是一项更大规模的糖尿病筛查研究，基于目标2进行额外的口服葡萄糖耐量试验。另一项R 01将基于目标3中的试点，并提出一项完全有效的多中心随机对照试验，以评估基于EMR的糖尿病筛查工具和临床决策支持在临床实践中识别糖尿病和前驱糖尿病病例的有效性。拟议的研究和培训目标将战略定位PI成为开发，实施和评估循证干预措施的领导者，以改善2型糖尿病的结局。K23提案中概述的糖尿病风险识别和筛查的创新方法有可能对这种非常常见，严重和昂贵的疾病的临床护理，人口健康管理和国家筛查指南政策产生非常大的影响。