COMPARATIVE STUDY
JOURNAL ARTICLE
RANDOMIZED CONTROLLED TRIAL

Two items on the Hamilton Depression rating scale are effective predictors of remission: comparison of selective serotonin reuptake inhibitors with the combined serotonin/norepinephrine reuptake inhibitor, venlafaxine

P H Silverstone, R Entsuah, D Hackett
International Clinical Psychopharmacology 2002, 17 (6): 273-80
12409680
Recent studies have shown that the use of subscales derived from the Hamilton Depression (HAM-D) rating scale are just as reliable and enhance sensitivity for detecting response and remission after antidepressant treatment. The purpose of the present study was to determine if the responses on two items of the HAM-D scale, Depressed Mood (item 1) and Psychic Anxiety (item 10), were predictive of remission of depression in placebo-controlled studies following treatment with venlafaxine or selective serotonin reuptake inhibitors (SSRIs). Data from eight active- and placebo-controlled studies consisting of 2027 subjects who met the DSM-III-R/-IV criteria for major depressive disorder were analysed. Three treatment groups were compared: venlafaxine (n =843), SSRI (either fluoxetine, paroxetine or fluvoxamine, n=743) and placebo (n=441). Treatment duration was 6-8 weeks. Patients who scored zero on the depressed mood and the psychic anxiety items of the HAM-D17 scale were designated as responders. These two scores were also combined to create an Absence of Depressive and Anxious Mood (ADAM) score. Between-group rate comparisons in outcome measures were carried out using Fisher's exact test and logistic regression models. Venlafaxine treatment improved depressed mood, psychic anxiety and ADAM scores after 2 weeks with greater efficacy than treatment with SSRIs or placebo. ADAM scores could also predict the odds ratio of a patient achieving a clinical remission (defined as total HAM-D17 score </= 7). The present results demonstrate that using just two items of the HAM-D17 can be very useful in assessing treatment response, differentiating between treatment groups and predicting remission rates.

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