A history of atrial fibrillation and outcomes in chronic advanced systolic heart failure: a propensity-matched study
Bibliographic record
Abstract
AIMS: Atrial fibrillation (AF)-associated poor outcomes in heart failure (HF) are often attributed to older age, advanced disease, and comorbidity burden of HF patients with AF. Therefore, we examined the effect of AF on outcomes in a propensity-matched study in which patients with and without AF were well balanced on all measured baseline characteristics. METHODS AND RESULTS: Of the 2708 advanced chronic systolic HF patients in the Beta-Blocker Evaluation of Survival Trial, 653 had a history of AF. Propensity scores for AF were calculated for each patient and were used to assemble a cohort of 487 pairs of patients with and without AF who were balanced on 74 baseline characteristics. Matched Cox regression analyses were used to estimate associations of AF with outcomes during 23 months of mean follow-up. All-cause mortality occurred in 187 (rate, 2046/10,000 person-years of follow-up) and 181 (rate, 1885/10,000 person-years) matched patients with and without AF, respectively [matched hazard ratio (HR) when AF was compared with no-AF 1.03, 95% confidence interval (CI) 0.79-1.33; P = 0.84]. Heart failure hospitalization occurred in 215 (rate, 3171/10,000 person-years) and 184 (rate, 2405/10,000 person-years) matched patients with and without AF, respectively (matched HR when AF was compared with no-AF 1.28, 95% CI 1.00-1.63; P = 0.049). Hazard ratios and 95% CIs for AF-associated HF hospitalization for bucindolol and placebo groups were, respectively, 1.08 (0.81-1.43) and 1.54 (1.17-2.03; P for interaction = 0.09). CONCLUSION: A history of AF had no intrinsic association with mortality but was associated with HF hospitalization in chronic systolic HF.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".