Depression and the Risk of Myocardial Infarction and Coronary Death
Bibliographic record
Abstract
Findings regarding the association between depression and risk of coronary heart disease are inconsistent. We aimed to assess the association between depression and risk of myocardial infarction (MI) and coronary death through a meta-analysis.We performed an electronic literature search of MEDLINE, EMBASE, PsycINFO, ISI Web of Science, and Scopus databases through August 1, 2015, and manual search of the references of the eligible papers and related review articles. Two investigators independently conducted study selection and data abstraction. Disagreement was resolved by consensus. Confounder-adjusted hazard ratios (HRs) were pooled using a random-effects model. Heterogeneity was evaluated using the Cochran Q statistic and Higgins index. Publication bias was assessed by funnel plot and Egger test. Study quality was appraised with the Newcastle-Ottawa Scale.Among 19 eligible cohort studies including 323,709 participants, 8447 cases of MI and coronary death were reported during follow-up ranging from 4 to 37 years. The pooled adjusted HRs for patients with depression (vs those without) were 1.22 (95% CI, 1.13-1.32) for combined MI and coronary death, 1.31 (95% CI, 1.09-1.57) for MI alone (9 studies), and 1.36 (95% CI, 1.14-1.63) for coronary death alone (8 studies). The increased risk of MI and coronary death associated with depression was consistent using modified inclusion criteria, across most subgroups, and after adjusting for possible publication bias.Depression is associated with a significantly increased risk of MI and coronary death. Effective prevention and treatment of depression may decrease such risk.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".