Use of digoxin in atrial fibrillation: One step further in the mortality controversy from the AFFIRM study
Bibliographic record
Abstract
BACKGROUND: Whether there is a causal association between digoxin and mortality among patients with atrial fibrillation (AF), with or without congestive heart failure (HF), has been controversial; in particular, two prior analyses of data from the Atrial Fibrillation Follow-up Investigation of Rhythm Management (AFFIRM) trial have yielded conflicting results. We sought to investigate how digoxin impacts mortality, in the full AFFIRM cohort and for various subgroups, by applying marginal structural modeling (MSM) to AFFIRM data. METHODS: MSM is a newer statistical approach, which estimates causal association in the absence of randomization. MSM more effectively accounts for time-varying treatment and mitigates potential biases, in contrast to the two statistical approaches used in prior analyses of the AFFIRM data. RESULTS: Among 4,060 patients in AFFIRM, 660 (16.3%) died during follow-up. Digoxin was associated with significantly higher mortality in the full cohort (estimated hazard ratio [HR] 1.33, 95% confidence interval [CI] 1.11-1.60, P = 0.002) and in 3,121 patients without HF (HR 1.36, 95% CI 1.07-1.72, P = 0.011). There was a trend toward higher mortality with digoxin in 939 patients with HF (HR 1.29, 95% CI 0.96-1.72, P = 0.090). Associations were nonsignificant in 463 patients with HF and left ventricular ejection fraction (EF) ≥40% and in 155 patients with EF ≤30%. CONCLUSIONS: Digoxin is associated with significantly increased mortality among AFFIRM patients collectively, as determined by MSM statistical methodology. However, the impact of digoxin among AFFIRM patients with coexisting HF is inconclusive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.153 | 0.202 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| 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".