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

使用计算机自适应测试来识别初级保健中的抑郁症

基本信息

批准号：
8121550
负责人：
MATTHIAS ROSE
金额：
$ 13.51万
依托单位：
UNIV OF MASSACHUSETTS MED SCH WORCESTER
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-18 至 2013-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8121550
关键词：
Adverse effects Algorithms Attention Beck depression inventory Caring Characteristics Classification Clinical Clinical Research Communities Computer software Computers Cross-Over Studies Depressed mood Depressive disorder Detection Diagnosis Diagnostic Effectiveness Epidemiologic Studies Equipment and supply inventories Floor Grant Health Health Status Healthcare Healthcare Systems Indiana Individual Institutes International Interview Length Measurement Measures Medical Medicine Mental Depression Mental Health Mental Health Services Mental disorders Meta-Analysis Methods Metric Monitor Moods National Institute of Mental Health New York City Paper Patient Monitoring Patient Self-Report Patients Persons Phase Physicians Primary Care Physician Primary Health Care Printing Process Provider Psyche structure Psychometrics Questionnaires Randomized Reporting Research Scientist Screening procedure Self-Administered Sensitivity and Specificity Severities Small Business Innovation Research Grant Societies Solutions Span 20 System Technology Testing United States Universities base clinical care clinical decision-making clinical practice college computerized depressive symptoms high risk improved instrument meetings member primary care setting psychologic response routine care success theories therapy adherence tool treatment adherence

项目摘要

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.

描述（由申请人提供）：精神障碍是美国最普遍的疾病之一，对个人和社会的负担是巨大的。然而，在社区卫生保健系统中，他们仍然普遍诊断不足。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。有一些开发良好的工具，但很少能融入临床实践。虽然许多工具的心理测量特征是好的，他们仍然不满足临床需要，一个共同的指标来比较不同的工具的结果仍然是缺失的。此外，纸笔问卷必须手动评分，这对临床实践构成了关键障碍，因为高风险患者的提供者报告必须及时且有选择性才能有效。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自适应测试（MH-CAT），以评估社区医疗机构患者的心理健康状况。报告可以立即打印，显示抑郁症状的严重程度，自我报告的治疗和坚持。第一个证据表明，该工具被广泛接受，并提供了非常高的测量精度，几乎没有下限和上限的问题，评估整个连续体从情绪升高到抑郁症状，只使用3-4个项目。底层的项目库允许比较已建立的抑郁工具，如流行病学中心抑郁量表和心理健康量表-5。在一个为期21/2年的项目中，我们建议：（1）建立一个自适应算法的MH-CAT识别抑郁症具有高灵敏度和特异性，（2）证明其作为一个常规的筛选工具在临床实践中的可行性，（3）评估其对病例识别和临床决策的影响。为了实现这些目标，我们将评估来自印第安纳波利斯初级保健研究网络的抑郁症患者。将MH-CAT与患者健康问卷（PHQ-9）和贝克抑郁量表（BDI）进行比较。位于布朗克斯服务不足社区的纽约市研究和改善网络集团的两个大型健康中心将把MH-CAT引入他们的日常护理中。在一项随机交叉研究中，我们将评估与PHQ-9相比的筛选成功率和对临床决策的影响。所有先前未识别的阳性筛选患者将通过复合国际诊断访谈进行评估，以确认诊断分类，并随访3个月，以评估已采取的措施。约这项研究将包括2,500名患者，与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公司的科学家一起进行。公共卫生相关性：心理健康计算机化自适应测试（MH-CAT）的引入将帮助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，引入MH-CAT是可能的，对患者和提供者的负担最小。它将有可能改善数千名患有未确诊精神健康问题的患者的生活。