Delayed aneurysm rupture after flow-diverter deployment in unruptured aneurysms: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Delayed aneurysm rupture (DAR) is a potentially devastating complication after flow-diverters (FD) deployment and their exact mechanisms are still unclear. The purpose of this meta-analysis is to synthesize the latest evidence on DAR after FD treatment and identify predictive factors. METHODS: A systematic review and meta-analysis were conducted according to established protocols. Searches were conducted in PubMed, Scopus, Web of Science, and Embase databases up to June 2024 using variations of "aneurysm "and "delayed aneurysm rupture" terms. Original studies reporting DAR and including more than five patients were included. All analyses were conducted in R software (version 4.4.2), employing the "meta" and "tableone" packages. RESULTS: 64 studies with 9820 patients were included, large aneurysm size (OR=1.08, 95% CI (1.02, 1.14), P value=0.008), intra-aneurysmal thrombus (OR=1.05, 95% CI (1.01, 1.09), P value=0.010) and posterior circulation location (OR=1.02, 95% CI (1.00, 1.04), P value=0.014) were significant risk factors for DAR. The pooled prevalence of DAR was 1.40%, with a rate of 1.18% after removing outliers. DAR occurred within 30 days in 66.7% of cases, with a mortality rate of 73.3% at discharge. CONCLUSION: DAR following flow diversion treatment is an uncommon but devastating complication and remains a limitation of the technique. It is influenced by factors such as large aneurysm size, intra-aneurysmal thrombosis, and posterior circulation involvement. The first 30 days post-procedure represent a particularly high-risk period. Despite advancements in FD technology, significant gaps remain, highlighting the need for further research to improve risk assessment and treatment strategies.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".