Landiolol for the prevention of postoperative atrial fibrillation after cardiac surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
Postoperative atrial fibrillation (POAF) is a common complication following cardiac surgery. Although the evidence suggests that beta blockers prevent POAF, they often cause hypotension. Landiolol, an ultra-short-acting β 1 blocker, may prevent POAF, without adverse hemodynamic consequences. We searched MEDLINE, CENTRAL, Embase, and trial registries between January 1970 and March 2022. We included randomized controlled trials (RCTs) that evaluated the effect of landiolol for the prevention of POAF after cardiac surgery. Two reviewers independently assessed eligibility, extracted data, and assessed risk of bias using the Risk of Bias 2.0 tool. We pooled data using random-effects models. We used the Grading of Recommendations, Assessment, Development and Evaluations framework to assess certainty of evidence. Nine RCTs including 868 participants met the eligibility criteria. Patients randomized to landiolol (56/460) had less POAF compared with controls (133/408) with a relative risk (RR) of 0.40 (95% confidence interval [CI], 0.30 to 0.54; I 2 = 0%;) and an absolute risk of 12.2% vs 32.6% (absolute risk difference, 20.4%; 95% CI, 15.0 to 25.0). Landiolol resulted in a shorter hospital length-of-stay (LOS) (268 patients; mean difference, −2.32 days; 95% CI, −4.02 to −0.57; I 2 = 0%). We found no significant difference in bradycardia (RR, 1.11; 95% CI, 0.48 to 2.56; I 2 = 0%). No hypotension was reported with landiolol. We judged the certainty of evidence as moderate for POAF (because of indirectness as outcomes were not clearly defined) and low for LOS (because of imprecision and concern of reporting bias). In patients undergoing cardiac surgery, landiolol likely reduces POAF and may reduce LOS. A definitive large RCT is needed to confirm these findings. PROSPERO (CRD42021262703); registered 25 July 2021.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".