Elevated Depression Symptoms Predict Long-Term Cardiovascular Mortality in Patients With Atrial Fibrillation and Heart Failure
Bibliographic record
Abstract
BACKGROUND: Depression predicts prognosis in many cardiac conditions, including congestive heart failure (CHF). Despite heightened cardiac risk in patients with comorbid atrial fibrillation (AF) and CHF, depression has not been studied in this group. This substudy, from the AF-CHF Trial of rate- versus rhythm-control strategies, investigated whether depression predicts long-term cardiovascular mortality in patients with left ventricular ejection fraction or=14). Over a mean follow-up of 39 months, there were 246 cardiovascular deaths (111 presumed arrhythmic; 302 all-cause deaths). Cox proportional hazards models adjusted for other prognostic factors (including age, marital status, cause of CHF, creatinine level, left ventricular ejection fraction, paroxysmal AF, previous AF hospitalization, previous electrical conversion, and baseline medications) showed that elevated depression scores significantly predicted cardiovascular mortality (primary outcome), arrhythmic death, and all-cause mortality. The adjusted hazard ratios were 1.57 (95% confidence interval 1.20 to 2.07, P<0.001), 1.69 (95% confidence interval 1.13 to 2.53, P=0.01), and 1.38 (95% confidence interval 1.07 to 1.77, P=0.01), respectively. The risks associated with depression and marital status were additive, with the highest risk in depressed patients who were unmarried. CONCLUSIONS: Elevated depression symptoms are related to cardiovascular mortality even after adjustment for other prognostic indicators in patients with comorbid AF and CHF who receive optimized treatment. Unmarried patients are also at increased risk. Mechanisms and treatment options deserve additional study.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".