Prognostic association of depression following myocardial infarction with mortality and cardiovascular events: a meta-analysis of 25 years of research
Bibliographic record
Abstract
OBJECTIVE: A meta-analysis of over 25 years of research into the relationship between post-myocardial infarction (MI) depression and cardiac prognosis was conducted to investigate changes in this association over time and to investigate subgroup effects. METHOD: A systematic literature search was performed (Medline, Embase and PsycINFO; 1975–2011) without language restrictions. Studies investigating the impact of post-MI depression on cardiovascular outcome, defined as all-cause mortality, cardiac mortality and cardiac events within 24 months after the index MI, were identified. Depression had to be assessed within 3 months after MI using established instruments. Pooled odds ratios (ORs) were calculated using a random effects model. RESULTS: A total of 29 studies were identified, resulting in 41 comparisons. Follow-up (on average 16 months) was described for 16,889 MI patients. Post-MI depression was associated with an increased risk of all-cause mortality [(OR), 2.25; 95% confidence interval [CI], 1.73-2.93; P<.001], cardiac mortality (OR, 2.71; 95% CI, 1.68–4.36; P<.001) and cardiac events (OR, 1.59; 95% CI, 1.37-1.85; P<.001). ORs proved robust in subgroup analyses but declined over the years for cardiac events. CONCLUSIONS: Post-MI depression is associated with a 1.6- to 2.7-fold increased risk of impaired outcomes within 24 months. This association has been relatively stable over the past 25 years.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.023 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".