Efficacy and Tolerability of Antidepressants for Treatment of Depression in Coronary Artery Disease: A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Depression occurs in 18% to 45% of patients with coronary artery disease (CAD) where it is associated with an increased risk of acute coronary events and mortality. Our objective was to quantitatively summarize the data on the efficacy and tolerability of antidepressant (AD) treatment for depression in CAD. METHODS: We performed a meta-analysis of randomized, placebo-controlled, double-blind trials with a database search of the English literature (to March 2008) and manual search of references. RESULTS: Four clinical trials with ADs (mirtazapine, citalopram, fluoxetine, and sertraline) of a 9- to 24-week duration involving 798 subjects (402 ADs, 396 placebo) with documented CAD and meeting the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria for depression were included. ADs were superior to placebo for decreasing Hamilton Depression Rating Scale (HDRS) scores (402 ADs, 396 placebo; weighted mean difference 1.41, 95% CI 0.53 to 2.29, P = 0.002) and Beck Depression Inventory (BDI) scores (373 ADs, 369 placebo; weighted mean difference 2.27, 95% CI 0.60 to 3.94, P = 0.008). The proportion of patients (216 ADs, 213 placebo) who responded (a 50% or more reduction in HDRS scores, OR 1.72, 95% CI 1.17 to 2.54) and remitted (HDRS of 8 or less at final assessment, OR 1.80, 95% CI 1.18 to 2.74), were also significantly higher with AD, compared with placebo, with no significant differences between the 2 groups for overall dropouts (OR 0.84, 95% CI 0.42 to 1.68) or dropout owing to adverse events (OR 1.30, 95% CI 0.75 to 2.25). The combined studies were homogeneous except for overall dropout rate (P = 0.01). CONCLUSION: Treatment with ADs for depression in CAD results in significant therapeutic effects without substantially increased rates of discontinuation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.045 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".