Developing Treatment Policies for Complex Patients Using Modeling and Data Mining

使用建模和数据挖掘为复杂患者制定治疗策略

• 批准号: 7534280
• 负责人: Paul E. Johnson
• 金额: $ 14.75万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-08 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7534280
• 关键词: Developing; Treatment; Policies; Complex; Patients

DESCRIPTION (provided by applicant): Patients with type 2 diabetes mellitus have high risk for cardiovascular events, and the risk derives from multiple sources including elevated glucose, blood pressure, lipids, and other factors. Prior studies have assessed cardiovascular risk in diabetes patients and several evidence-based clinical goals have been identified that independently reduce risks of future adverse cardiovascular events. Prior studies have estimated projected risks of future events for patients with type 2 diabetes, but these studies do not systematically evaluate strategies, risks, and treatment costs for diabetes patients. However, prior research has not provided usable information needed to compare the relative risks and benefits of multiple treatment policies that are available for diabetes care at successive points in time. Specifically, no research is available that estimates the relative impact on cardiovascular events or on costs of competing clinical policies that differentially emphasize glucose, BP, or lipid control, or the relative merits and drawbacks of a "feedforward" versus the more typical "feedback" clinical policy that typically characterizes care of complex patients. The research proposed here addresses these critical gaps in knowledge using modeling and data mining technologies to discover and structure clinical policies that most effectively reduce risk of cardiovascular events in complex patients with diabetes. The work will proceed in two steps: (a) Develop modeling methodology to identify physician treatment strategies (combinations of pharmaceutical agents, timing of clinical interventions, complexity of regimen, risky prescribing events) that minimize cost or risk of major cardiovascular complications in complex patients with diabetes, and (b) Apply computational modeling and data mining techniques to identify the optimal combinations of pharmaceutical agents to minimize pharmaceutical costs while achieving pre-specified degrees of reduction in risk of major cardiovascular complications in complex patients with diabetes. Specific objectives will examine the relative merits of clinical policies that prioritize different clinical domains, and the relative merits of "feedforward" versus "feedback" clinical strategies. Results will contribute to the important ongoing debate about comparative effectiveness of alternative clinical policies for complex patients with diabetes, including cost data needed to inform the development of clinical guidelines and public policy for the care of complex patients, whose needs are not well addressed by existing clinical guidelines. Moreover, the methods used in this project will provide a useful prototype for comparative effectiveness research that can be applied to diverse clinical domains and patient populations.

描述（由申请人提供）：2型糖尿病患者具有心血管事件的高风险，风险来自多种来源，包括血糖升高、血压、血脂和其他因素。先前的研究已经评估了糖尿病患者的心血管风险，并且已经确定了几个基于证据的临床目标，这些目标独立地降低了未来不良心血管事件的风险。先前的研究估计了2型糖尿病患者未来事件的预计风险，但这些研究没有系统地评估糖尿病患者的策略，风险和治疗费用。然而，先前的研究并没有提供有用的信息，需要比较的相对风险和利益的多种治疗政策，可用于糖尿病护理在连续的时间点。具体而言，没有研究可以估计对心血管事件的相对影响或对差异强调葡萄糖、BP或脂质控制的竞争性临床政策的成本的相对影响，或者“前馈”与更典型的“反馈”临床政策的相对优点和缺点，该临床政策通常表征复杂患者的护理。 本文提出的研究使用建模和数据挖掘技术来解决这些关键的知识差距，以发现和构建最有效地降低复杂糖尿病患者心血管事件风险的临床政策。这项工作将分两步进行：（a）制定建模方法，以确定医生的治疗策略（药物组合、临床干预的时机、方案的复杂性、有风险的处方事件），使复杂的糖尿病患者的主要心血管并发症的成本或风险最小化，和（B）应用计算建模和数据挖掘技术来识别药剂的最佳组合，以使药物成本最小化，同时实现预处理。在复杂的糖尿病患者中，主要心血管并发症风险降低的特定程度。具体的目标将检查临床政策的相对优点，优先考虑不同的临床领域，和“前馈”与“反馈”的临床策略的相对优点。 研究结果将有助于正在进行的关于复杂糖尿病患者替代临床政策的比较有效性的重要辩论，包括为制定临床指南和复杂患者护理公共政策提供信息所需的成本数据，这些患者的需求没有得到现有临床指南的很好解决。此外，本项目中使用的方法将为比较有效性研究提供有用的原型，可应用于不同的临床领域和患者人群。