4.7 Article

Accuracy of specific symptoms in the diagnosis of major depressive disorder in psychiatric out-patients: data from the MIDAS project

Journal

PSYCHOLOGICAL MEDICINE
Volume 39, Issue 7, Pages 1107-1116

Publisher

CAMBRIDGE UNIV PRESS
DOI: 10.1017/S0033291708004674

Keywords

Clinical utility; diagnostic accuracy; major depression; sensitivity; specificity

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Background. There is uncertainty about the diagnostic significance of specific symptoms of major depressive disorder (MDD). There is also interest in using one or two specific symptoms in the development of brief scales. Our aim was to elucidate the best possible specific symptoms that would assist in ruling in or ruling out a major depressive episode in a psychiatric out-patient setting. Method. A total of 1523 psychiatric out-patients were evaluated in the Methods to Improve Diagnostic Assessment and Services (MIDAS) project. The accuracy and added value of specific symptoms from a comprehensive item bank were compared against the Structured Clinical Interview for DSM-IV (SCID). Results. The prevalence of depression in our sample was 54.4%. In this high prevalence setting the optimum specific symptoms for ruling in MDD were psychomotor retardation, diminished interest/pleasure and indecisiveness. The optimum specific symptoms for ruling out MDD were the absence of depressed mood, the absence of diminished drive and the absence of loss of energy. However, some discriminatory items were relatively uncommon. Correcting for frequency, the most clinically valuable rule-in items were depressed mood, diminished interest/pleasure and diminished drive. The most clinically valuable rule-out items were depressed mood, diminished interest/pleasure and poor concentration. Conclusions. The study supports the use of the questions endorsed by the two-item Patient Health Questionnaire (PHQ-2) with the additional consideration of the item diminished drive as a rule-in test and poor concentration as a rule-out test. The accuracy of these questions may be different in primary care studies where prevalence differs and when they are combined into multi-question. tests or algorithmic models.

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