Safety of Short-Term Discontinuation of Antiplatelet Therapy in Patients With Drug-Eluting Stents
Bibliographic record
Abstract
BACKGROUND: Antiplatelet therapy is often discontinued in patients with drug-eluting stents who are undergoing surgical procedures. However, discontinuation of antiplatelet therapy is an important risk factor for late stent thrombosis. Our objective was to examine the safety of short-term discontinuation of antiplatelet therapy. METHODS AND RESULTS: We systematically searched Medline for reported cases of late stent thrombosis and very late stent thrombosis published between January 2001 and July 2008. We restricted our search to Academic Research Consortium-defined definite cases. We identified 161 cases of late stent thrombosis or very late stent thrombosis from 84 articles (79 from case reports, 61 from registries, and 21 from randomized clinical trials). Patients had a mean age of 58.4+/-13.4 years, and 88% were male. A total of 19 cases occurred in patients who were receiving dual antiplatelet therapy at the time of the event. If patients stopped both antiplatelet agents simultaneously, the median time to event was 7 days. If patients had previously stopped a thienopyridine with no ill effect and subsequently stopped acetylsalicylic acid, the median time to event was also 7 days from the time of acetylsalicylic acid cessation. If the thienopyridine was stopped but acetylsalicylic acid was maintained, the median time to event was 122 days. Among the 48 patients who stopped both agents, 36 cases (75%) occurred within 10 days. Among the 94 patients who discontinued a thienopyridine but continued acetylsalicylic acid, only 6 cases (6%) occurred within 10 days. CONCLUSIONS: If acetylsalicylic acid therapy is maintained, short-term discontinuation of a thienopyridine may be relatively safe in patients with drug-eluting stents.
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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".