Meta-analysis of Comparative Studies Between Self- and Balloon-Expandable Bridging Stent Grafts in Branched Endovascular Aneurysm Repair
Bibliographic record
Abstract
Purpose: Currently there is no robust evidence which type of bridging stent graft provides better outcomes after branched endovascular aortic repair (BEVAR). Self-expanding (SESG) and balloon-expandable (BESG) stent grafts are both commonly used to connect branches to their respective target vessels (TV). The aim of the current review was to evaluate the impact of the type of bridging stent grafts on TV outcomes during the mid-term follow-up after BEVAR. Materials and Methods: The study protocol was registered to the PROSPERO (CRD42021274766). A search of the English literature was conducted, using PubMed and EMBASE databases via Ovid and Cochrane database via CENTRAL, from inception to June 30, 2021, using the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Only comparative studies on BEVAR reporting TV outcomes related to BESG vs SESG were considered eligible. Individual studies were assessed for risk of bias using the Newcastle Ottawa Scale. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach was used to evaluate the quality of evidence. The primary outcomes were primary patency, freedom from endoleak, TV instability, and re-intervention between BESG and SESG, used as bridging stents in branches. The outcomes were summarized as odds ratio along with their 95% confidence intervals (CI), through a paired meta-analysis. Results: Five out of 609 articles published from 2016 to 2020 were included in the analysis. In total, 1406 TV were revascularized, 547 (38.9 %) with BESGs and 859 with SESGs. The overall pooled primary patency (odds ratio [OR], 0.40; 95% confidence interval [CI], 0.29–1.09; p=.256, I 2 =4.24%) and freedom from branch-related endoleak (OR, 0.65; 95% CI, 0.17–1.48; p<.122, I 2 =0.18%) did not differ between the stent types during the available follow-up (17 months, range = 12–35 months). In 4 studies (619 TV), SESG required fewer secondary interventions (OR, 1.04; 95% CI, 0.23–1.83; p=.009, I 2 =0%) and TV instability rate was lower (OR, 0.99; 95% CI, 0.33–1.65; p=.003, I 2 =0%) compared with BESG during the available follow-up. Conclusion: BESG and SESG seem to perform similarly in terms of primary patency and branch-related endoleak during the mid-term follow-up. Current data from retrospective studies suggest that overall TV instability and re-intervention rates are favorable for SESG as bridging stent grafts in BEVAR.
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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 | 0.031 | 0.079 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.073 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".