Transmyocardial laser revascularization vs medical treatment: Angina reduction ‐ NYHA scale Decrease in two angina classes or more
Transmyocardial laser revascularization may leads to higher angina reduction Decrease in two angina classes or more compared with medical treatment (low certainty).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Transmyocardial laser revascularization vs medical treatment Angina reduction Decrease in two angina classes or more · patients with refractory angina Transmyocardial laser revascularization may leads to higher angina reduction Decrease in two angina classes or more compared with medical treatment (low certainty). Transmyocardial laser revascularization versus medical therapy for refractory angina (2015) · Cochrane Headline outcomes only | OR 4.63 [3.43, 6.25] | 7 studies · 1051 participants | Low |
Transmyocardial laser revascularization vs medical treatment Angina reduction ‐ NYHA scale Decrease in two angina classes or more · patients with refractory angina Transmyocardial laser revascularization probably leads to higher angina reduction ‐ NYHA scale Decrease in two angina classes or more compared with medical treatment (moderate certainty). Transmyocardial laser revascularization versus medical therapy for refractory angina (2015) · Cochrane Headline outcomes only | OR 68.32 [7.95, 587.38] | 2 studies · 128 participants | Moderate |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Transmyocardial laser revascularization versus medical therapy for refractory angina 2015 · Cochrane Headline outcomes only | Not reported | list not extracted yet | Not assessed |
Studies behind the findings
None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.
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.