Developing Treatment Policies for Complex Patients Using Modeling and Data Mining

描述(申请人提供)：2型糖尿病患者心血管事件的风险很高，风险来自多种来源，包括血糖、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，并确定了几个以证据为基础的临床目标，这些目标独立地降低了未来不良心血管事件的风险。以前的研究已经估计了2型糖尿病患者未来事件的预测风险，但这些研究没有系统地评估糖尿病患者的策略、风险和治疗成本。然而，先前的研究没有提供必要的有用信息来比较在连续时间点可用于糖尿病护理的多种治疗政策的相对风险和好处。具体地说，没有研究估计对心血管事件的相对影响，或对不同地强调血糖、血压或血脂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”临床政策的相对优缺点，后者通常是复杂患者护理的特征。 这里提出的研究解决了知识中的这些关键差距，使用建模和数据挖掘技术来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)开发建模方法，以确定医生的治疗策略(药物的组合、临床干预的时机、方案的复杂性、高风险的处方事件)，以最大限度地减少复杂糖尿病患者的主要心血管并发症的成本或风险，以及(B)应用计算建模和数据挖掘技术来确定最佳的药物组合，以最大限度地减少药物成本，同时实现预先指定的降低复杂糖尿病患者主要心血管并发症风险的程度。具体目标将审查优先考虑不同临床领域的临床政策的相对优点，以及“前馈”和“反馈”临床策略的相对优点。 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辩论，包括为制定临床指南和护理复杂患者的公共政策提供信息所需的成本数据，现有的临床指南没有很好地解决这些患者的需求。此外，该项目中使用的方法将为可应用于不同临床领域和患者群体的比较有效性研究提供一个有用的原型。

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.