Outcome of intracranial flow diversion according to the antiplatelet regimen used: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Thromboembolic complications are not uncommon in patients undergoing neurointerventional procedures. The use of flow diverting stents is associated with higher risks of these complications despite current dual antiplatelet regimens. OBJECTIVE: To explore contemporary evidence on the safety of emerging dual antiplatelet regimens in flow diverting stenting procedures. METHODS: We performed a systematic review and meta-analysis to identify relevant articles in electronic databases, and relevant references. Studies reporting the complications and mortality of flow diverting stenting procedures using acetyl salicylic acid (ASA) + ticagrelor or ASA + prasugrel compared with ASA + clopidogrel were included. RESULTS: Of 452 potentially relevant studies, we identified 49 studies (2526 patients) which reported the safety of ticagrelor or prasugrel for pooled analysis, and five studies (1005 patients) for meta-analysis. The pooled overall mortality in all studies was 2.14%, ischemic complications 6.89%, and hemorrhagic complications 3.68%. The use of ticagrelor or prasugrel was associated with a lower risk of mortality compared with clopidogrel (RR=4.57, 95% CI 1.23 to 16.99; p=0.02). Considering ischemic events, ASA + clopidogrel was as safe as ASA + prasugrel (RR=0.55, 95% CI 0.11 to 2.74; p=0.47) and ASA + ticagrelor (RR=0.74, 95% CI 0.32 to 1.74; p=0.49). ASA +ticagrelor was not associated with a higher risk of hemorrhagic complications (RR=0.92, 95% CI 0.27 to 3.16; p=0.89). CONCLUSIONS: Evidence suggests that dual antiplatelet regimens including ticagrelor or prasugrel are safe for patients undergoing flow diversion procedures. Regimens using ticagrelor were associated with better survival than those using clopidogrel in the included studies.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.047 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".