Machine Learning and Longitudinal Analyses of Metformin Response Among Veterans

退伍军人二甲双胍反应的机器学习和纵向分析

• 批准号: 10657444
• 负责人: SRIDHARAN RAGHAVAN
• 金额: --
• 依托单位: VA EASTERN COLORADO HEALTH CARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10657444
• 关键词: Achievement; Address; Affect; American; Anticoagulation; Area; Atrial Fibrillation; Calibration; Cardiovascular Diseases; Caring; Characteristics; Cholesterol; Chronic Disease; Clinical; Clinical Data; Colorado; Complications of Diabetes Mellitus; Computing Methodologies; Data; Decision Making; Development Plans; Diabetes Mellitus; Diagnosis; Discrimination; Disease; Disease Management; Electronic Health Record; Ensure; Environment; Epidemiologic Methods; Evaluation; Eye diseases; Future; Genetic; Glucose; Glycosylated hemoglobin A; Goals; Growth; Guidelines; Health; Healthcare; Individual; Intervention; K-Series Research Career Programs; Kidney Diseases; Longitudinal Studies; Longitudinal cohort; Machine Learning; Measurement; Measures; Mentorship; Metformin; Methodology; Methods; Modeling; Newly Diagnosed; Non-Insulin-Dependent Diabetes Mellitus; Observational Study; Oral; Outcome; Patient risk; Patients; Pattern; Performance; Pharmaceutical Preparations; Population; Population Heterogeneity; Prediction of Response to Therapy; Professional Organizations; Provider; Recommendation; Reproducibility; Research; Research Personnel; Resources; Risk; Risk Estimate; Risk Reduction; Selection for Treatments; Testing; Time; Training; Universities; Variant; Veterans; cardiovascular disorder risk; care burden; career; career development; clinical care; clinical database; clinical predictive model; clinical risk; clinically relevant; cohort; critical period; data repository; data resource; design; diabetes management; diabetes risk; epidemiology study; evidence base; glycemic control; heart disease risk; improved; individual patient; individualized medicine; innovation; longitudinal analysis; machine learning method; medical schools; medication compliance; military veteran; model development; optimal treatments; patient stratification; patient subsets; precision medicine; predict clinical outcome; predicting response; predictive modeling; predictive tools; randomized trial; response; risk prediction; stroke risk; tool; treatment choice; treatment effect; treatment response

Type 2 diabetes mellitus is a chronic disease that may be amenable to precision medicine approaches because it affects a large, diverse segment of the population. In fact, the VA and American Diabetes Association guidelines recommend individualization of diabetes management, yet initial diabetes treatment is rarely individualized in current routine clinical care. The vast majority of diabetes patients are initially treated with metformin, and over a quarter of these patients fail to respond to metformin alone, leading to delays in achievement of early glycemic control and potentially avoidable risk of diabetes complications. There is a paucity of validated strategies to individualize initial diabetes treatment. Thus, precision medicine approaches, which attempt to match optimal disease management strategies to characteristics of an individual patient, are ideal to address the evidence gap to systematically guide individualized diabetes care. Individualized or precision medicine treatment is common in cardiovascular disease care, where clinical risk prediction tools guide drug selection (e.g., anticoagulation in atrial fibrillation) and treatment intensity (e.g., cholesterol goals on statin therapy). We propose a similar approach for diabetes treatment individualization based on prediction tools that estimate risk of diabetes complications and glycemic response to metformin. The overall goals of this career development award (CDA) are to develop and validate prediction tools that inform individualized diabetes care. The first two aims of the proposal are designed to determine patient characteristics at the onset of treatment that predict diabetes-related complications (Aim 1) and glycemic response to metformin (Aim 2). In Aim 3, we will evaluate whether these prediction models can inform treatment approaches to achieve improvements in long-term diabetes complications. We will leverage large VA data repositories to create two independent cohorts of Veterans with type 2 diabetes to complete the aims of this proposal and form the basis of additional future observational studies. This study is innovative in that it leverages real-world clinical data from Veterans to generate evidence to guide precision medicine interventions; uses machine learning approaches and longitudinal methods to capture information from repeated measurements in routine clinical care to make maximal use of electronic health record data; and focuses on prediction tools based on data available at the time of diabetes diagnosis to guide initial treatment. The career development plan aligns research aims with training aims in order to prepare the applicant to undertake a research career focused on applications of precision medicine to improve Veteran health. The training goals of the proposal are focused in three areas that share a common theme of maximizing longitudinal VA clinical data for clinically-relevant observational research: 1) machine learning approaches applied to a clinical database; 2) longitudinal methods, including growth mixture models, that enable identification of patterns in repeated clinical measures; 3) methods for causal inference using large- scale observational data. Upon successful completion of the proposed scientific and training aims, the applicant will be prepared to pursue precision medicine studies focused on the use of newer diabetes drugs, incorporation of genetic data into clinical prediction models, and evaluation of the effect of prediction model use in routine diabetes care. The diverse mentorship team has content expertise in diabetes and cardiovascular disease, and methodological expertise in machine learning, longitudinal epidemiological studies, and causal inference. Direct mentorship will be paired with coursework and seminars to fill gaps in the applicant’s training and ensure progress towards independence as a clinician-investigator. Finally, the environment is ideal for the applicant’s career development, including resources available through the VA HSR&D Denver-Seattle Center of Innovation for Veteran-Centric and Value-Driven Care and the University of Colorado School of Medicine.

2型糖尿病是一种慢性疾病，可能适用于精确医学方法。 因为它影响了人口中的一大部分，而且是多样化的。事实上，退伍军人管理局和美国糖尿病 协会指南建议糖尿病管理个体化，但最初的糖尿病治疗是 在目前的常规临床护理中很少个性化。绝大多数糖尿病患者最初都得到了治疗。 这些患者中有超过四分之一的人对二甲双胍没有单独的反应，导致延迟 实现早期血糖控制和潜在的可避免的糖尿病并发症风险。有一个 缺乏有效的策略来个性化最初的糖尿病治疗。因此，精准医学即将到来， 它试图将最佳的疾病管理策略与单个患者的特征相匹配，包括 理想地解决证据差距，系统地指导个性化的糖尿病护理。个性化或 精准医学治疗在心血管疾病护理中很常见，临床风险预测工具 指导药物选择(例如，房颤的抗凝治疗)和治疗强度(例如， 他汀类药物治疗)。我们提出了基于预测的糖尿病个体化治疗的类似方法。 评估糖尿病并发症风险和对二甲双胍的血糖反应的工具。 这个职业发展奖(CDA)的总体目标是开发和验证预测 提供个性化糖尿病护理信息的工具。该提案的前两个目标旨在确定 治疗开始时预测糖尿病相关并发症的患者特征(目标1)和 对二甲双胍的血糖反应(目标2)。在目标3中，我们将评估这些预测模型是否能够 改善糖尿病长期并发症的治疗方法。我们将利用大型退伍军人管理局 创建两个独立的2型糖尿病退伍军人队列的数据存储库，以完成 这一提议构成了未来更多观测研究的基础。这项研究的创新之处在于它 利用退伍军人的真实临床数据生成证据来指导精准医疗 干预；使用机器学习方法和纵向方法从 在常规临床护理中重复测量，以最大限度地利用电子健康记录数据；以及 重点是基于糖尿病诊断时可用数据的预测工具，以指导最初的治疗。 职业发展计划使研究目标与培训目标保持一致，以便为 申请者从事专注于精准医学应用的研究生涯以提高 老兵健康。该提案的培训目标集中在三个领域，这三个领域有着共同的主题 最大化临床相关观察性研究的纵向VA临床数据：1)机器学习 应用于临床数据库的方法；2)纵向方法，包括生长混合模型， 能够在重复的临床测量中识别模式；3)使用大的- 对观测数据进行刻度。在圆满完成拟议的科学和培训目标后， 申请者将准备从事以使用较新的糖尿病药物为重点的精确医学研究， 将遗传数据纳入临床预测模型，并评估预测模型的使用效果 在常规的糖尿病护理中。多元化的导师团队在糖尿病和心血管疾病方面拥有丰富的专业知识 疾病，以及机器学习、纵向流行病学研究和因果关系方面的方法学专长 推论。直接指导将与课程作业和研讨会相结合，以填补申请者培训方面的空白 并确保作为临床医生-研究员的独立性取得进展。最后，这里的环境非常适合 申请人的职业发展，包括退伍军人事务部丹佛-西雅图中心提供的资源 以退伍军人为中心和价值驱动的医疗创新中心和科罗拉多大学医学院。