- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Background: Due to the heterogeneity of depressive symptoms—which can include depressed mood, anhedonia, negative cognitive biases, and altered activity levels—researchers often use a combination of depression rating scales to assess symptoms. This study sought to identify unidimensional constructs measured across rating scales for depression and to evaluate these constructs across clinical trials of a rapid-acting antidepressant (ketamine). Methods: Exploratory factor analysis (EFA) was conducted on baseline ratings from the Beck Depression Inventory (BDI), the Hamilton Depression Rating Scale (HAM-D), the Montgomery-Asberg Depression Rating Scale (MADRS), and the Snaith-Hamilton Pleasure Rating Scale (SHAPS). Inpatients with major depressive disorder (n = 76) or bipolar depression (n = 43) were participating in clinical ketamine trials. The trajectories of the resulting unidimensional scores were evaluated in 41 subjects with bipolar depression who participated in clinical ketamine trials. Results: The best solution, which exhibited excellent fit to the data, comprised eight factors: Depressed Mood, Tension, Negative Cognition, Impaired Sleep, Suicidal Thoughts, Reduced Appetite, Anhedonia, and Amotivation. Various response patterns were observed across the clinical trial data, both in treatment effect (ketamine versus placebo) and in degree of placebo response, suggesting that use of these unidimensional constructs may reveal patterns not observed with traditional scoring of individual instruments. Limitations: Limitations include: 1) small sample (and related inability to confirm measurement invariance); 2) absence of an independent sample for confirmation of factor structure; and 3) the treatment-resistant nature of the population, which may limit generalizability. Conclusions: The empirical identification of unidimensional constructs creates more refined scores that may elucidate the connection between specific symptoms and underlying pathophysiology.
As interest in identifying biotypes of subjects with mood disorders develops, so will the need for appropriate and specific clinical assessments. The present study has underscored that factor analysis techniques can identify unidimensional constructs from a range of depressive symptoms. By using these unidimensional scores instead of total measure scores, we may be able to increase the precision with which we clinically describe these new and emerging biotypes, as well as measure response to treatment. Such an approach may be an important step towards parsing the heterogeneity of depressive symptoms.