The Relationship Between Frailty and Postoperative Atrial Fibrillation in Patients Undergoing Cardiac Surgery: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background Postoperative atrial fibrillation (POAF) occurs in roughly one‐third of patients undergoing cardiac surgery, increasing mortality, morbidity, and healthcare costs. Frailty, a syndrome of physiological decline, is associated with poor outcomes after cardiac surgery. The link between frailty and POAF has not been clearly established. Methods A systematic search of PubMed and EMBASE was conducted up to January 2025 for studies reporting preoperative frailty scores and POAF rates postcardiac surgery. Transcatheter aortic valve replacement (TAVR) studies were assessed separately. Pooled risk ratios (RRs) and 95% confidence intervals (CIs) for frail vs. nonfrail patients were calculated using a random‐effect model. Results Eighteen studies involving 9098 patients (undergoing coronary artery bypass, surgical valve replacement, or both) were included. Meta‐analysis indicated that frailty was associated with a higher risk of POAF (RR 1.20; 95% CI 1.08–1.33, p = 0.0008). Subgroup analyses showed consistent frailty effects across different patient ages, study designs, surgery types, and sample sizes. Notably, POAF risk was higher in studies using clinical frailty scales (RR 1.33; 95% CI 1.15–1.54, p < 0.0001) compared to those using surrogate imaging/lab‐based methods (RR 1.10; 95% CI 0.92–1.33, p = 0.24) though comparison between subgroups did not reach statistical significance ( p = 0.08). In 3 studies for patients undergoing TAVR, there was no association between frailty and POAF (RR 1.24; 95% CI 0.81–1.88, p = 0.32). Conclusion This meta‐analysis underscores the association between frailty and increased POAF risk in cardiac surgery patients, highlighting the need for comprehensive clinical frailty assessments in preoperative evaluations to identify high‐risk patients and optimize perioperative management.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".