Using a Computer Adaptive Test to Identify Depressive Disorders in Primary Care

描述（由申请人提供）：精神障碍是美国最普遍的疾病之一，其对个人和社会的负担是巨大的。然而，在以社区为基础的卫生保健系统中，他们仍然被广泛地诊断不足。帮助初级保健医生识别和监测心理健康问题的一种方法是使用自我管理的患者问卷。有一些完善的仪器可用，但整合到临床实践很少实现。虽然许多工具的心理测量特征很好，但它们仍然不能满足临床需要，并且仍然缺乏一个比较不同工具结果的通用度量。此外，纸笔问卷必须手动评分，这是临床实践的一个关键障碍，因为高风险患者的提供者报告必须及时和有选择性才能有效。针对这些问题，我们最近建立了一个基于项目反应理论的计算机适应测试来评估社区卫生保健机构（MH-CAT）患者的心理健康状况。报告可以立即打印出来，显示抑郁症状的严重程度、自我报告的治疗情况和依从性。第一个证据表明，该工具被广泛接受，并且提供了非常高的测量精度，几乎没有下限和上限问题，仅使用3-4个项目评估从情绪高涨到抑郁症状的整个连续体。基础项目库允许比较已建立的抑郁工具，如流行病学研究中心抑郁量表和心理健康清单5。在一个为期两年半的项目中，我们建议：(1)建立一个MH-CAT自适应算法，以高灵敏度和特异性识别抑郁症；(2)证明其作为临床常规筛查工具的可行性；(3)评估其对病例识别和临床决策的影响。为了达到这些目的，我们将对来自印第安纳波利斯初级保健研究网络的抑郁症患者进行评估。MH-CAT将与患者健康问卷（PHQ-9）和贝克抑郁量表（BDI）进行比较。位于布朗克斯区服务不足社区的纽约市研究和改进网络小组的两家大型保健中心将把MH-CAT引入其日常护理。在一项随机交叉研究中，我们将与PHQ-9比较，评估筛查的成功及其对临床决策的影响。所有以前未被确认的阳性筛查患者将通过综合国际诊断访谈进行评估，以确认诊断分类，并随访3个月，以评估已采取哪些行动。大约2500名患者将被纳入这项研究，由阿尔伯特·爱因斯坦医学院、Regenstrief研究所、兰德公司、哈佛大学和QualityMetric公司的科学家共同进行。公共卫生相关性：引入心理健康计算机化适应测试（MH-CAT）将有助于初级保健医生识别和监测有心理健康问题的患者。该项目将证明，在患者和提供者负担最小的情况下，引入MH-CAT是可能的。它将有可能改善成千上万患有未确诊精神健康问题的患者的生活。

DESCRIPTION (provided by applicant): Mental disorders are among the most prevalent conditions in the United States, and their burden for the individual and society is tremendous. Nevertheless, they are still widely under-diagnosed in community- based health care systems. One way to help primary care physicians identify and monitor mental health problems is to use self-administered patient questionnaires. There are a number of well developed instruments available, but integration into clinical practice has rarely been achieved. Although psychometric characteristics of many tools are good, they still do not meet clinical needs, and a common metric to compare results from different tools is still missing. In addition, paper-pencil questionnaires have to be scored manually, which impose a key barrier for clinical practice, as provider reports for high risk patients must be timely and selective to be effective. Responding to those problems we recently built a Computer Adaptive Test based on the Item Response Theory to assess the mental health status of patients in community based health care settings (MH-CAT). Reports can be printed instantly showing severity of depressive symptoms, self reported treatment and adherence. First evidence has demonstrated that the tool is well accepted, and provides very high measurement precision with almost no floor and ceiling problems assessing the entire continuum from elevated mood to depressive symptoms using only 3-4 items. The underlying item bank allows comparing established depression tools, like the Center for Epidemiologic Studies-Depression Scale and Mental Health Inventory-5. Within a 21/2 -year project we propose to: (1) establish an adaptive algorithm for the MH-CAT to identify depressive disorders with high sensitivity and specificity, (2) demonstrate its feasibility as a routine screening instrument in clinical practice, and (3) assess its impact for case recognition and clinical decision making. To achieve these aims we will evaluate depressed patients from a Primary Care Research Network in Indianapolis. The MH-CAT will be compared to the Patient Health Questionnaire (PHQ-9), and the Beck Depression Inventory (BDI). Two large health centers of the New York City Research and Improvement Networking Group located in underserved communities in the Bronx will introduce the MH-CAT into their routine care. Within a randomized cross-over study we will evaluate the screening success and impact on clinical decision making in comparison with the PHQ-9. All previously not-recognized positive screened patients will be assessed with the Composite International Diagnostic Interview to confirm the diagnostic classification and followed-up for three month to assess which actions have been taken. Approx. 2,500 patients will be included in the study, being carried out together with scientists from the Albert Einstein College of Medicine, Regenstrief Institute, RAND Corporation, Harvard University, and QualityMetric Inc. PUBLIC HEALTH RELEVANCE: The introduction of the Mental Health Computerized Adaptive Test (MH-CAT) will help primary care physicians to identify and monitor patients with mental health problems. The project will demonstrate that an introduction of the MH-CAT is possible with minimal burden for patients and providers. It will have the potential to improve lives of thousands of patients suffering from undiagnosed mental health problems.