Center for Health Statistics, Department of Medicine, University of Chicago, 5841 S Maryland Ave, MC 2007 Office W260, Chicago, IL 60637, USA.
J Clin Psychiatry. 2013 Jul;74(7):669-74. doi: 10.4088/JCP.12m08338.
To develop a computerized adaptive diagnostic screening tool for depression that decreases patient and clinician burden and increases sensitivity and specificity for clinician-based DSM-IV diagnosis of major depressive disorder (MDD).
656 individuals with and without minor and major depression were recruited from a psychiatric clinic and a community mental health center and through public announcements (controls without depression). The focus of the study was the development of the Computerized Adaptive Diagnostic Test for Major Depressive Disorder (CAD-MDD) diagnostic screening tool based on a decision-theoretical approach (random forests and decision trees). The item bank consisted of 88 depression scale items drawn from 73 depression measures. Sensitivity and specificity for predicting clinician-based Structured Clinical Interview for DSM-IV Axis I Disorders diagnoses of MDD were the primary outcomes. Diagnostic screening accuracy was then compared to that of the Patient Health Questionnaire-9 (PHQ-9).
An average of 4 items per participant was required (maximum of 6 items). Overall sensitivity and specificity were 0.95 and 0.87, respectively. For the PHQ-9, sensitivity was 0.70 and specificity was 0.91.
High sensitivity and reasonable specificity for a clinician-based DSM-IV diagnosis of depression can be obtained using an average of 4 adaptively administered self-report items in less than 1 minute. Relative to the currently used PHQ-9, the CAD-MDD dramatically increased sensitivity while maintaining similar specificity. As such, the CAD-MDD will identify more true positives (lower false-negative rate) than the PHQ-9 using half the number of items. Inexpensive (relative to clinical assessment), efficient, and accurate screening of depression in the settings of primary care, psychiatric epidemiology, molecular genetics, and global health are all direct applications of the current system.
开发一种用于抑郁的计算机化自适应诊断筛查工具,以减轻患者和临床医生的负担,并提高基于临床医生的 DSM-IV 对重度抑郁症(MDD)诊断的敏感性和特异性。
从精神病诊所和社区心理健康中心以及通过公共通告(无抑郁的对照组)招募了 656 名患有和不患有轻度和重度抑郁症的个体。该研究的重点是基于决策理论方法(随机森林和决策树)开发计算机化自适应诊断测试用于重度抑郁障碍(CAD-MDD)的诊断筛查工具。该项目库由 73 种抑郁量表中的 88 个抑郁量表项目组成。预测基于临床医生的 DSM-IV 轴 I 障碍诊断的 MDD 的敏感性和特异性是主要结果。然后将诊断筛查准确性与患者健康问卷-9(PHQ-9)进行比较。
每位参与者平均需要 4 个项目(最多 6 个项目)。总体敏感性和特异性分别为 0.95 和 0.87。对于 PHQ-9,敏感性为 0.70,特异性为 0.91。
通过在不到 1 分钟的时间内平均使用 4 个自适应管理的自我报告项目,可以获得基于临床医生的 DSM-IV 抑郁诊断的高敏感性和合理的特异性。与当前使用的 PHQ-9 相比,CAD-MDD 在保持相似特异性的同时,大大提高了敏感性。因此,与 PHQ-9 相比,CAD-MDD 使用一半的项目数量将识别出更多的真正阳性(较低的假阴性率)。在初级保健、精神病流行病学、分子遗传学和全球健康等环境中,廉价(相对于临床评估)、高效且准确的抑郁筛查是当前系统的直接应用。