Safety and efficacy of low-dose intracoronary thrombolysis during primary percutaneous coronary intervention in patients with ST elevation myocardial infarction: A meta-analysis of randomized trials
Bibliographic record
Abstract
In patients with ST elevation myocardial infarction (STEMI), intracoronary thrombolysis (ICT) may reduce thrombotic burden and microvascular obstruction in the infarct-related artery. We performed a meta-analysis to evaluate the role of adjunctive low-dose ICT during primary percutaneous coronary intervention (PPCI) in improving clinical outcomes and indices of microvascular function. We searched electronic databases (Cochrane, EMBASE, Medline; inception to October 2023) for randomized controlled trials (RCTs) evaluating the effects of adjunctive ICT in STEMI patients undergoing PPCI, compared with placebo or usual care. Study-level data on efficacy and safety outcomes were pooled using a fixed-effect model. The primary outcome was major adverse cardiovascular events (MACE). A total of 8 RCTs were included, comprising a total of 1,208 patients. Compared with placebo or usual care, ICT was associated with a trend towards lower MACE (11.3% vs. 15.1%; odds ratio [OR] 0.73, 95% confidence interval [CI] 0.51 to 1.04). Infarct size (mean difference [MD] -1.98, 95% CI -3.68 to -0.27; p=0.02), ST-segment resolution (MD: 6.06, 95% CI: 0.69 to 11.43; p=0.03) and corrected TIMI frame count (MD: -2.26, 95% CI: -4.03 to -0.48; p=0.01; I2=78%). The odds for major (0.7% vs. 0.7%; OR 0.94, 95% CI 0.24 to 3.7; p=0.93) and minor bleeding (7.7% vs. 4.3%; OR 1.81, 95% CI 0.87 to 3.76; p=0.11) were similar between the two groups. Adjunctive low-dose ICT during PPCI is safe, associated with a trend towards lower MACE, and may improve surrogate markers of microvascular function. These hypothesis-generating findings warrant validation in larger, adequately powered randomized trials.
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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.045 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".