Develop T2D Patient-Centered Treatment Suggestion Rule using EMR data

使用 EMR 数据制定以 T2D 患者为中心的治疗建议规则

• 批准号: 9330849
• 负责人: Jin Zhou
• 金额: $ 13.07万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-11 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9330849
• 关键词: Adopted; Adverse effects; Adverse event; Affect; Agonist; Algorithms; American; Amputation; Big Data; Blindness; Caring; Characteristics; Chronic; Combination Medication; Complement; Complex; Computerized Medical Record; Data; Data Analyses; Databases; Decision Making; Diabetes Mellitus; Diagnostic; Diet; Effectiveness; Expert Opinion; Failure; GLP-I receptor; Glucose; Guidelines; Health Personnel; Healthcare Systems; Heterogeneity; Incidence; Individual; Injectable; Insulin; Life; Life Style; Medical; Medicine; Mentors; Methods; Modeling; Morbidity - disease rate; Non-Insulin-Dependent Diabetes Mellitus; Observational Study; Oral; Outcome; Outcomes Research; Pathology; Pathway interactions; Patient-Focused Outcomes; Patients; Pattern; Performance; Pharmaceutical Preparations; Physicians; Publishing; Randomized Clinical Trials; Recommendation; Records; Regimen; Research; Risk; Scheme; Societies; Standardization; Subgroup; Suggestion; Testing; Toxic effect; Treatment Effectiveness; Treatment Protocols; United States; United States Department of Veterans Affairs; Update; Validation; basal insulin; base; clinical practice; cohort; cost; design; diabetes management; effectiveness measure; follow-up; health care service utilization; inclusion criteria; individualized medicine; medical specialties; mortality; patient oriented; personalized care; personalized medicine; programs; response; standard care; tool; treatment center; treatment effect; treatment response; user friendly software

ABSTRACT Develop T2D Patient-Centered Treatment Suggestion Rule using EMR data In clinical practice, physicians and health care providers often follow the treatment guidance based on published research and experts' opinions. The American Diabetes Association (ADA) annually publishes updated recommendations for Type 2 Diabetes (T2D) management. Although the standardized diabetes management approach has resulted in substantial improvement in overall diabetes care, different patients often respond to treatments differently (treatment heterogeneity effects). This proposal aims to develop methods to facilitate personalized treatment recommendations using information from electronic medical records (EMR) for T2D. We will first evaluate the real world effectiveness of different treatments when diabetes patients follow the treatment guidance. We will then assess the treatment differences, identify the baseline information that has predictive ability for the treatment differences, and develop treatment recommendation rules for a single individual or subgroups of individuals. As EMR is a type of observational study, the propensity score matching method will be adopted to determine causal relationships. Finally, we will use cross validation and independent data to validate our results. Methods proposed in this research will be implemented in an efficient and user-friendly software package to further facilitate easy patient-centered treatment decision-making. The proposed method uses information from EMR data, which contains comprehensive baseline information for approximately 20 million US patients and “real world” drug effectiveness. Therefore our method bridges the gap of using “big and generalizable” data for patient-centered outcomes research.

摘要 使用EMR数据制定T2D以患者为中心的治疗建议规则 在临床实践中，医生和卫生保健提供者经常遵循基于以下的治疗指导： 发表的研究和专家的意见。美国糖尿病协会（ADA）每年发布 2型糖尿病（T2D）治疗的最新建议虽然标准化的糖尿病 管理方法导致整体糖尿病护理的实质性改善，不同患者 通常对治疗的反应不同（治疗异质性效应）。该提案旨在发展 使用来自电子医疗设备的信息促进个性化治疗建议的方法 T2D的EMR记录。我们将首先评估不同治疗方法的真实的效果， 糖尿病患者遵循治疗指导。然后，我们将评估治疗差异， 对治疗差异具有预测能力的基线信息，并制定治疗方案 针对单个个体或个体的子组的推荐规则。由于EMR是一种观察性的 在研究中，我们会采用倾向评分配对法来确定因果关系。最后我们将 使用交叉验证和独立数据来验证我们的结果。本研究提出的方法将 在一个高效和用户友好的软件包中实现，以进一步促进以患者为中心的 治疗决策。所提出的方法使用来自EMR数据的信息，其中包含 约2000万美国患者和“真实的世界”药物的全面基线信息 有效性因此，我们的方法弥补了以患者为中心的“大而可概括”数据的差距。 成果研究。