Association Between Perioperative Atrial Fibrillation and Long-term Risks of Stroke and Death in Noncardiac Surgery: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background Atrial fibrillation (AF) is frequently reported as a complication of noncardiac surgery. It is unknown whether new-onset perioperative AF is associated with an increased risk of stroke and death beyond the perioperative period. We performed a systematic review and meta-analysis to assess the long-term risks of stroke and mortality associated with new-onset perioperative AF after noncardiac surgery. Methods MEDLINE and EMBASE were searched from inception to March 2020 for studies reporting on the association between perioperative AF and the risk of stroke and death occurring beyond 30 days after noncardiac surgery. Reference screening, study selection, data extraction, and quality assessment were performed in duplicate. Data were pooled using inverse variance-weighted random-effects models and presented as risk ratios (RRs). Results From 7344 citations, we included 31 studies (3,529,493 patients). The weighted mean incidence of perioperative AF was 0.7%. During a mean follow-up of 28.1 ± 9.4 months, perioperative AF was associated with an increased risk of stroke (1.5 vs 0.9 strokes per 100 patient-years; RR: 2.9, 95% confidence interval [CI]: 2.1-3.9, I 2 = 78%). Perioperative AF was also associated with a significantly higher risk of all-cause mortality (21.0 vs 7.6 deaths per 100 patient-years; RR: 1.8, 95% CI: 1.5-2.2, I 2 = 94%). The pooled adjusted hazard ratios for stroke and all-cause mortality were 1.9 (95% CI: 1.6-2.2, I 2 = 31%) and 1.5 (95% CI: 1.3-1.7, I 2 = 20%), respectively. Conclusions Patients who had perioperative AF after noncardiac surgery had a higher long-term risk of stroke and mortality compared with patients who did not. Whether this risk is modifiable with oral anticoagulation therapy should be investigated.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.025 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".