Question · asked by 1 review
Beta‐blockers vs control—non‐cardiac surgery: Bradycardia—non‐cardiac surgery ECG
- Population
- patients with conditions requiring non‐cardiac surgery
- Intervention
- Beta‐blockers
- Comparison
- control—non‐cardiac surgery
- Outcome
- Bradycardia—non‐cardiac surgery ECG
Beta‐blocker probably leads to higher bradycardia—non‐cardiac surgery ECG compared with control—non‐cardiac surgery (moderate certainty).
What each review found
Current versions onlyNo studies found 0 · 0%GRADE not reported 0 · 0%Very low 0 · 0%Low 0 · 0%Moderate 2 · 100%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Beta‐blocker vs control—non‐cardiac surgery Bradycardia—non‐cardiac surgery ECG · patients with conditions requiring non‐cardiac surgery Beta‐blocker probably leads to higher bradycardia—non‐cardiac surgery ECG compared with control—non‐cardiac surgery (moderate certainty). Perioperative beta‐blockers for preventing surgery‐related mortality and morbidity (2018) · Cochrane Headline outcomes only | RR 2.23 [1.48, 3.36] | 23 studies · 11033 participants | Moderate |
Beta‐blocker vs control—non‐cardiac surgery Bradycardia—non‐cardiac surgery ECG · patients with conditions requiring non‐cardiac surgery Beta‐blocker probably leads to higher bradycardia—non‐cardiac surgery ECG compared with control—non‐cardiac surgery (moderate certainty). Perioperative beta‐blockers for preventing surgery‐related mortality and morbidity (2014) · Cochrane Headline outcomes onlyearlier version CD004476.PUB2, superseded | RR 2.24 [1.49, 3.35] | 24 studies · 11083 participants | Moderate |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Perioperative beta‐blockers for preventing surgery‐related mortality and morbidity 2018 · Cochrane Headline outcomes only | Not reported | Not extracted | Not assessed |
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.