The Impact of Premorbid and Postmorbid Depression Onset on Mortality and Cardiac Morbidity Among Patients With Coronary Heart Disease
Bibliographic record
Abstract
BACKGROUND: Depression is associated with increased cardiac morbidity and mortality in the general population and in patients with coronary heart disease (CHD). Recent evidence suggests that patients with new-onset depression post-CHD diagnosis have worse outcomes than do those who had previous or recurrent depression. This meta-analysis investigated the timing of depression onset in established CHD and CHD-free cohorts to determine what time frame is associated with greater mortality and cardiac morbidity. METHODOLOGY/PRINCIPAL FINDINGS: The MEDLINE, EMBASE, and PsycINFO databases were searched systematically to identify articles examining a depression time frame that specified an end point of all-cause mortality, cardiac mortality, rehospitalization, or major adverse cardiac events. A meta-analysis was conducted to estimate effect sizes by time frame of depression. Twenty-two prospective cohort studies were identified. Nine studies investigated premorbid depression in CHD-free cohorts in relation to cardiac death. Thirteen studies in patient samples with CHD examined new-onset depression in comparison with previous or recurrent depression. The pooled effect size (risk ratio) was 0.76 (95% confidence interval (CI) = 0.48-1.19) for history of depression only, 1.79 (95% CI = 1.45-2.21) for premorbid depression onset, 2.11 (95% CI = 1.66-2.68) for postmorbid or new depression onset, and 1.59 (95% CI = 1.08-2.34) for recurrent depression. CONCLUSIONS/SIGNIFICANCE: Both premorbid and postmorbid depression onsets are potentially hazardous, and the question of timing may be irrelevant with respect to adverse cardiac outcomes. However, the combination of premorbid depression with the absence of depression at the time of a cardiac event (i.e., historical depression only) is not associated with such outcomes and deserves further investigation.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.026 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".