Incidence, Risk Factors, and Outcomes of Perioperative Atrial Fibrillation following Noncardiothoracic Surgery: A Systematic Review and Meta-Regression Analysis of Observational Studies
Bibliographic record
Abstract
Background. Atrial fibrillation (AF) occurs in 16–30% of patients after cardiac and thoracic surgery and can lead to serious complications like hypoperfusion of vital organs, pulmonary edema, and myocardial infarction. The evidence on risk factors and complications associated with perioperative AF after noncardiothoracic surgery is limited. Methods. The primary objective was to determine demographic and clinical risk factors for new-onset atrial fibrillation associated with noncardiothoracic surgery. A secondary aim was to identify the incidence and odds of perioperative complications associated with the new-onset atrial fibrillation. A systematic search within multiple databases was conducted for studies that explicitly reported on new-onset atrial fibrillation after noncardiothoracic surgery. We reported data on demographics, comorbidities, and perioperative complications as mean difference (MD) or odds ratios (OR) and corresponding 95% confidence interval (CI) using random effects models. A two-sided P value of less than 0.05 was considered significant. We performed meta-regression and sensitivity analysis of various subgroups to confirm the inference of our findings. Results. Eleven studies reporting on 121,517 patients were included, of whom 2,944 developed perioperative AF (incidence rate: 3.7%; 95% CI: 2.3%––6.2%). Advanced age (AF group versus control group: 69.36 ± 10.5 versus 64.37 ± 9.53 years; MD: 4.06; 95% CI: 1.67––6.44; P = 0.0009 ), male gender (52.85% versus 43.59%; OR: 1.08; 95% CI: 0.54 to 1.62; I2: 84%; P < 0.0001 ), preoperative hypertension (60.42% versus 56.51%; OR: 1.15; 95% CI: 1.08 to 1.23; I2: 0%; P < 0.00001 ), diabetes mellitus (22.6% versus 23.04%; OR: 0.97; 95% CI: 0.89 to 1.05; I2: 0; P < 0.00001 ), and cardiac disease (30.64% versus 8.49%; OR: 2.3; 95% CI: 0.28 to 4.31; I2: 93%; P = 0.03 ) were found to be significant predictors for perioperative AF. The AF group was at increased odds of developing postoperative cardiac complications (34.1% versus 5%; OR: 5.44; 95% CI: 0.49 to 10.39; I2: 82%; P = 0.03 ), postoperative stroke (0.5% versus 0.1%; OR: 3; 95% CI: 0.65 to 5.35; I2: 0%; P = 0.01 ), and mortality (7.40% versus 1.92%; OR: 3.58; 95% CI: 0.14 to 7.02; I2: 0%; P = 0.04 ). Study quality assessment by meta-regression and sensitivity analysis of the various subgroups did not affect the final inference of the results. Conclusion. We identified advanced age, male gender, preoperative hypertension, diabetes mellitus, and cardiac disease as important risk factors for perioperative atrial fibrillation. The atrial fibrillation group was at increased odds for postoperative cardiac complications, stroke, and higher mortality, emphasizing the need for risk stratification and close monitoring.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".