Is There an Association Between External Cardioversions and Long-Term Mortality and Morbidity?
Bibliographic record
Abstract
BACKGROUND: Cardiac electric therapies effectively terminate tachyarrhythmias. Recent data suggest a possible increase in long-term mortality associated with implantable cardioverter-defibrillator shocks. Little is known about the association between external cardioversion episodes (ECVe) and long-term mortality. We sought to assess the safety of repeated ECVe with regard to cardiovascular mortality and morbidity. METHODS AND RESULTS: We analyzed the data of the 4060 patients from the AFFIRM (Atrial Fibrillation Follow-up Investigation of Rhythm Management) trial. In particular, associations of ECVe with all-cause mortality, cardiovascular mortality, and hospitalizations after ECVe were studied. Over an average follow-up of 3.5 years, 660 (16.3%) patients died, 331 (8.2%) from cardiovascular causes. A total of 207 (5.1%) and 1697 (41.8%) patients had low ejection fraction and nonparoxysmal atrial fibrillation, respectively; 2460 patients received no ECVe, whereas 1600 experienced ≥ 1 ECVe. Death occurred in 412 (16.7%), 196 (16.5%), 39 (13.5%), and 13 (10.4%) of patients with 0, 1, 2, and ≥ 3 ECVe, respectively. There was no significant association between ECVe and mortality within any of the 4 subgroups defined by ejection fraction and atrial fibrillation type, although myocardial infarction, coronary artery bypass graft, and digoxin were significantly associated with death (estimated hazard ratios, 1.65, 1.59, and 1.62, respectively; P < 0.0001). ECVe were associated with increased cardiac hospitalization reported at the next follow-up visit (39.3% versus 5.8%; estimated odds ratio, 1.39; P < 0.0001). CONCLUSIONS: In the AFFIRM study, there was no significant association between ECVe and long-term mortality, even though ECVe were associated with increased hospitalizations from cardiac causes. Digoxin, myocardial infarction, and coronary artery bypass graft were significantly associated with mortality.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".