Selective β1-Antagonism with Bisoprolol Is Associated with Fewer Postoperative Strokes than Atenolol or Metoprolol
Bibliographic record
Abstract
BACKGROUND: Perioperative metoprolol increases postoperative stroke. Animal studies indicate that the mechanism may be related to attenuated β(2)-adrenoreceptor-mediated cerebral vasodilatation. The authors therefore conducted a cohort to study whether the highly β(1)-specific β-blocker (bisoprolol) was associated with a reduced risk of postoperative stroke compared with less selective β-blockers (metoprolol or atenolol). METHODS: The authors conducted a single-center study on 44,092 consecutive patients with age 50 yr or more having noncardiac, nonneurologic surgery. The primary outcome was stroke within 7 days of surgery. The secondary outcome was a composite of all-cause mortality, postoperative myocardial injury, and stroke. A propensity score-matched cohort was created to assess the independent association between bisoprolol and less β(1)-selective agents metoprolol or atenolol. A secondary analysis using logistic regression, based on previously identified confounders, also compared selective β(1)-antagonism. RESULTS: Twenty-four percent (10,756) of patients were exposed to in-hospital β-blockers. A total of 88 patients (0.2%) suffered a stroke within 7 days of surgery. The matched cohort consisted of 2,462 patients, and the pairs were well matched for all variables. Bisoprolol was associated with fewer postoperative strokes than the less selective agents (odds ratio = 0.20; 95% CI, 0.04-0.91). Multivariable risk-adjustment in the β-blockers-exposed patients comparing bisoprolol with the less selective agents was associated with a similarly reduced stroke rate. CONCLUSIONS: The use of metoprolol and atenolol is associated with increased risks of postoperative stroke, compared with bisoprolol. These findings warrant confirmation in a pragmatic randomized trial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".