title: Dimensional diagnosis of depression: adding the dimension of course to severity, and comparison to the DSM.
abstract: It has long been debated whether depression is best classified with a categorical or dimensional diagnostic system. There has been surprisingly little discussion, however, of what the contents of a dimensional classification should include, with most studies employing a single dimension based on symptom severity. The present study explored whether a dimension based on prior course of depression increases the validity of a dimensional model based on symptom severity alone and whether the two dimensions combined improve upon the present categorical system (DSM). The sample consisted of 133 patients with a broad spectrum of depressive diagnoses. External validators included family history of mood disorder, assessed using the family history method, and course over a 6-month prospective follow-up period. Prior course contributed significant incremental validity over and above symptom severity in predicting subsequent course and family history of mood disorder. However, the linear combination of symptom severity and prior course provided only a minimal increase in predictive power over and above the DSM diagnoses of major depressive disorder (MDD) and dysthymia.
authors.affiliation: Department of Psychology, State University of New York at Stony Brook, Stony Brook, NY 11794-2500, USA.
authors.forename: Stewart A
authors.initials: SA
authors.lastname: Shankman
authors.forename: Daniel N
authors.initials: DN
authors.lastname: Klein
mesh_terms: D000328:Adult; D017668:Age of Onset; D003863:Depression / Q000145:classification* / Q000175:diagnosis* / Q000235:genetics; D005260:Female; D006801:Humans; D007406:Interview, Psychological; D016015:Logistic Models; D008297:Male; D008960:Models, Psychological; D019964:Mood Disorders / Q000175:diagnosis / Q000235:genetics; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012494:Sampling Studies; D012720:Severity of Illness Index; D013997:Time Factors