The Impact of Statins on Postdischarge Atrial Fibrillation After Cardiac Surgery: Secondary Analysis from a Randomized Trial
Bibliographic record
Abstract
Background Whether statins reliably reduce the risk of postoperative atrial fibrillation (POAF) in patients undergoing cardiac surgery remains controversial. We sought to determine the impact of statin use on new-onset postdischarge POAF in the Post- S urgical E nh a nced Monito r ing for C ardiac Arr h ythmias and A trial F ibrillation (SEARCH-AF) CardioLink-1 randomized controlled trial. Methods We randomized 336 patients with risk factors for stroke (CHA 2 DS 2 -VASc score ≥ 2) and no history of preoperative atrial fibrillation (AF) to 30-day continuous cardiac rhythm monitoring after discharge from cardiac surgery with a wearable, patched-based device or to usual care. The primary endpoint was the occurrence of cumulative AF and/or atrial flutter lasting for ≥ 6 minutes detected by continuous monitoring, or AF and/or atrial flutter documented by a 12-lead electrocardiogram within 30 days of randomization. Results The 260 patients (77.4%) discharged on statins were more likely to be male ( P = 0.018) and to have lower CHA 2 DS 2 -VASc scores ( P = 0.011). Patients treated with statins at discharge had a 2-fold lower rate of POAF than those who were not treated with statins in the entire cohort (18.4% vs 8.1%, log-rank P = 0.0076). On multivariable Cox regression including the CHA 2 DS 2 -VASc score adjustment, statin use was associated with a lower risk of POAF (hazard ratio 0.43, 95% confidence interval: 0.25-0.98, P = 0.043). Use of statins at a higher intensity was associated with lower risk of POAF, suggestive of a dose–response effect (log-rank P trend = 0.0082). Conclusions The use of statins was associated with a reduction in postdischarge POAF risk among patients undergoing cardiac surgery. The routine use of high-intensity statin to prevent subacute POAF after discharge deserves further 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".