Recurrence of new-onset post-operative AF after cardiac surgery detected by implantable loop recorders: A systematic review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is one of the most common complications after cardiac surgery. New-onset post-operative AF may signal an elevated risk of AF and associated outcomes in long-term follow-up. We aimed to estimate the rate of AF recurrence as detected by an implantable loop recorder (ILR) in patients experiencing post-operative AF within 30 days after cardiac surgery. METHODS: We searched MEDLINE, Embase and Cochrane CENTRAL to April 2023 for studies of adults who did not have known AF, experienced new-onset AF within 30 days of cardiac surgery and received an ILR. We pooled individual participant data on timing of AF recurrence using a random-effects model with a frailty model applied to a Cox proportional hazard analysis. RESULTS: From 8671 citations, 8 single-centre prospective cohort studies met eligibility criteria. Data were available from 185 participants in 7 studies, with a median follow-up of 1.7 (IQR: 1.3-2.8) years. All included studies were at a low risk of bias. Pooled AF recurrence rates following 30 post-operative days were 17.8% (95% CI 11.9%-23.2%) at 3 months, 24.4% (17.7%-30.6%) at 6 months, 30.1% (22.8%-36.7%) at 12 months and 35.3% (27.6%-42.2%) at 18 months. CONCLUSIONS: In patients who experience new-onset post-operative AF after cardiac surgery, AF recurrence lasting at least 30 s occurs in approximately 1 in 3 in the first year after surgery. The optimal frequency and modality to use for monitoring for AF recurrence in this population remain uncertain.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".