Invasive versus conservative management in spontaneous coronary artery dissection: a meta-analysis and meta-regression study
Bibliographic record
Abstract
Abstract Background There is a paucity of data regarding the best treatment for spontaneous coronary artery dissection (SCAD). Purpose To compare the prognostic impact of conservative versus invasive treatment in patients with SCAD. Methods We systematically searched the literature for studies evaluating the comparative efficacy and safety of invasive revascularization versus medical therapy for the treatment of SCAD from 1990 to 2019. Random-effect meta-analysis was performed comparing clinical outcomes between the two groups. Results 24 observational studies with 1720 patients were included. After 28±14 months, a conservative approach reduced target vessel revascularization rate compared with invasive treatment (OR=0.50; 95% CI 0.28–0.90; P=0.02). No difference was found regarding all-cause mortality (OR=0.81; 95% CI 0.31–2.08; P=0.66), cardiovascular mortality (OR=0.89; 95% CI 0.15–5.40; P=0.89), myocardial infarction (OR=0.95; 95% CI 0.50–1.81; P=0.87), heart failure (OR 0.96; 95% CI 0.41–2.22; P=0.92) and SCAD recurrence (OR=0.94; 95% CI 0.52–1.72; P=0.85). The meta-regression analysis suggested that male gender, diabetes mellitus, smoking habit, prior coronary artery disease, left main coronary artery involvement and lower ejection fraction at admission are related with higher overall mortality, whereas SCAD recurrence was higher among patients with fibromuscular dysplasia. Conclusion A conservative approach provides similar clinical outcomes and lower target vessel revascularization rates compared to an invasive strategy in the setting of SCAD; therefore, when feasible, it should be preferred in this scenario. Forest plots on the study outcomes Funding Acknowledgement Type of funding source: None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.050 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".