Adjunctive Thrombectomy for Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: In available trials and meta-analyses, adjunctive thrombectomy in acute myocardial infarction (MI) improves markers of myocardial reperfusion but has limited effects on clinical outcomes. Thrombectomy devices simply aspirate thrombus or mechanically fragment it before aspiration. Simple aspiration thrombectomy may offer a distinct advantage. METHODS AND RESULTS: We identified 21 eligible trials (16 that used a simple aspiration thrombectomy device) involving 4299 patients with ST-segment elevation MI randomized to reperfusion therapy by primary percutaneous coronary intervention with or without thrombectomy. By using Bayesian meta-analysis methods, we found that thrombectomy yielded substantially less no-reflow (odds ratio [OR], 0.39; 95% credible interval [CrI], 0.18 to 0.69), more ST-segment resolution > or =50% (OR, 2.22; 95% CrI, 1.60 to 3.23), and more thrombolysis in myocardial infarction/myocardial perfusion grade 3 (OR, 2.50; 95% CrI, 1.48 to 4.41). There was no evidence for a decrease in death (OR, 0.94; 95% CrI, 0.47 to 1.80), death, recurrent MI, or stroke (OR, 1.07; 95% CrI, 0.63 to 1.92) with thrombectomy. Restriction of the analysis to trials that used simple aspiration thrombectomy devices did not yield substantially different results, except for a positive effect on postprocedure thrombolysis in myocardial infarction grade 3 flow (OR, 1.49; 95% CrI, 1.14 to 1.99). CONCLUSIONS: In this Bayesian meta-analysis, adjunctive thrombectomy improves early markers of reperfusion but does not substantially effect 30-day post-MI mortality, reinfarction, and stroke. The use of aspiration thrombectomy devices is not associated with a reduction in post-MI clinical outcomes. Thrombectomy is one of the rare effective preventive measures against no-reflow.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".