Development and Evaluation of a Depression Risk Calculator

描述（由申请人提供）：数百万老年人患有抑郁症。患者参与有效治疗的一个关键障碍是难以量化抑郁症的严重程度和预测未来抑郁的时间。更精确的抑郁风险模型将为患者提供有关其未来进程的更好信息，并允许更有效的共同决策。我提出了一个为期四年的指导研究计划，以开发和评估“抑郁风险计算器”。该工具将有助于识别有抑郁风险的患者，为他们提供有关抑郁轨迹的更多信息，识别与抑郁相关的其他症状，并加强患者和提供者之间的共同决策。为了实现这一目标，我将参加纵向数据分析和定性方法的课程，并获得在初级保健中应用共享决策的经验。使用三个大型纵向数据库的老年人（CHS，HRS和IMPACT），我将开发一个抑郁症风险计算器的算法。我将从五名曾接受过抑郁症治疗的患者那里寻求输入，了解计算器中的信息如何对他们最有益，并将相应地调整输出。然后，我将确定抑郁风险计算器的反馈对患者水平结果的影响，特别是患者参与和治疗的使用。我将向20名患者提供来自计算器的反馈，并将他们的经验与20名患者的对照组进行比较，随访一年。这项研究将产生假设，并为使用风险模型改善老年抑郁症治疗的大型研究产生初步结果。

DESCRIPTION (provided by applicant): Millions of older adults suffer from depression. A key barrier to patient engagement in application of effective treatments is the difficulty in quantifying depression severity and anticipating future time spent depressed. More refined models of depression risk would give patients better information about their future course, and allow more effective shared decision making. I propose a four-year, mentored research plan to develop and evaluate a "depression risk calculator". This tool will be useful for identifying patients at risk for depression, giving them more information about their depression trajectory, identifying other symptoms associated with depression, and enhancing shared decision making between patients and providers. In order to accomplish this end, I will take courses in longitudinal data analysis and qualitative methods, and gain experience in applying shared decision making in primary care. Using three large longitudinal databases of older adults (CHS, HRS, and IMPACT) I will develop algorithms for a depression risk calculator. I will seek input from five patients previously treated for depression about how information from the calculator would have the most benefit for them, and will tailor the outputs accordingly. I will then ascertain the effects of feedback from a depression risk calculator on patient-level outcomes, particularly patient engagement and use of treatments. I will provide feedback from the calculator to 20 patients, and compare their experiences with those of a control group of 20 patients, followed for one year. This research will generate hypotheses and produce preliminary results for larger studies of using risk models to improve treatment for geriatric depression.