Adjusted prognostic association of depression following myocardial infarction with mortality and cardiovascular events: individual patient data meta-analysis
Bibliographic record
Abstract
BACKGROUND: The association between depression after myocardial infarction and increased risk of mortality and cardiac morbidity may be due to cardiac disease severity. AIMS: To combine original data from studies on the association between post-infarction depression and prognosis into one database, and to investigate to what extent such depression predicts prognosis independently of disease severity. METHOD: An individual patient data meta-analysis of studies was conducted using multilevel, multivariable Cox regression analyses. RESULTS: Sixteen studies participated, creating a database of 10 175 post-infarction cases. Hazard ratios for post-infarction depression were 1.32 (95% CI 1.26-1.38, P<0.001) for all-cause mortality and 1.19 (95% CI 1.14-1.24, P<0.001) for cardiovascular events. Hazard ratios adjusted for disease severity were attenuated by 28% and 25% respectively. CONCLUSIONS: The association between depression following myocardial infarction and prognosis is attenuated after adjustment for cardiac disease severity. Still, depression remains independently associated with prognosis, with a 22% increased risk of all-cause mortality and a 13% increased risk of cardiovascular events per standard deviation in depression z-score.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".