The Comparison of Effectiveness of Various Potential Predictors of Response to Treatment With SSRIs in Patients With Depressive Disorder
Language English Country United States Media print
Document type Comparative Study, Journal Article
- MeSH
- Depressive Disorder, Treatment-Resistant blood drug therapy physiopathology MeSH
- Depressive Disorder, Major blood drug therapy physiopathology MeSH
- Adult MeSH
- Electroencephalography methods MeSH
- Outcome Assessment, Health Care * MeSH
- Middle Aged MeSH
- Humans MeSH
- Brain-Derived Neurotrophic Factor blood MeSH
- Predictive Value of Tests MeSH
- Prefrontal Cortex physiopathology MeSH
- Prognosis MeSH
- Psychiatric Status Rating Scales MeSH
- Selective Serotonin Reuptake Inhibitors administration & dosage pharmacology MeSH
- Theta Rhythm physiology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Comparative Study MeSH
- Names of Substances
- BDNF protein, human MeSH Browser
- Brain-Derived Neurotrophic Factor MeSH
- Serotonin Uptake Inhibitors MeSH
The substantial non-response rate in depressive patients indicates a continuing need to identify predictors of treatment outcome. The aim of this 6-week, open-label study was (1) to compare the efficacy of a priori defined predictors: ≥20% reduction in MADRS score at week 1, ≥20% reduction in MADRS score at week 2 (RM ≥ 20% W2), decrease of cordance (RC), and increase of serum and plasma level of brain-derived neurotrophic factor at week 1; and (2) to assess whether their combination yields higher efficacy in the prediction of response to selective serotonin re-uptake inhibitors (SSRIs) than when used singly. Twenty-one patients (55%) achieved a response to SSRIs. The RM ≥20% W2 (areas under curve-AUC = 0.83) showed better predictive efficacy compared to all other predictors with the exception of RC. The identified combined model (RM ≥ 20% W2 + RC), which predicted response with an 84% accuracy (AUC = 0.92), may be a useful tool in the prediction of response to SSRIs.
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