Revisiting the Evidence for Dipyridamole in Reducing Restenosis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
ABSTRACT: Atherosclerosis remains a leading cause of morbidity and mortality, with revascularization remaining a cornerstone of management. Conventional revascularization modalities remain challenged by target vessel reocclusion-an event driven by mechanical, thrombotic, and proliferative processes. Despite considerable advancements, restenosis remains the focus of ongoing research. Adjunctive agents, including dipyridamole, offer a multitude of effects that may improve vascular homeostasis. We sought to quantify the potential therapeutic impact of dipyridamole on vascular occlusion. We performed a literature search (EMBASE and MEDLINE) examining studies that encompassed 3 areas: (1) one of the designated medical therapies applied in (2) the setting of a vascular intervention with (3) an outcome including vascular occlusion rates and/or quantification of neointimal proliferation/restenosis. The primary outcome was vascular occlusion rates. The secondary outcome was the degree of restenosis by neointimal quantification. Both human and animal studies were included in this translational analysis. There were 6,839 articles screened, from which 73 studies were included, encompassing 16,146 vessels followed up for a mean of 327.3 days (range 7-3650 days). Preclinical studies demonstrate that dipyridamole results in reduced vascular occlusion rates {24.9% vs. 48.8%, risk ratio 0.53 [95% confidence interval (CI) 0.40-0.70], I2 = 39%, P < 0.00001}, owing to diminished neointimal proliferation [standardized mean differences -1.13 (95% CI -1.74 to -0.53), I2 = 91%, P = 0.0002]. Clinical studies similarly demonstrated reduced occlusion rates with dipyridamole therapy [23.5% vs. 31.0%, risk ratio 0.77 (95% CI 0.67-0.88), I2 = 84%, P < 0.0001]. Dipyridamole may improve post-intervention vascular patency and mitigate restenosis. Dedicated studies are warranted to delineate its role as an adjunctive agent after revascularization.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.029 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".