Improvements in patient outcomes with next generation endovascular aortic repair devices in the ENGAGE Global Registry and the EVAR-1 clinical trial
Bibliographic record
Abstract
BACKGROUND: The outcomes from the randomized controlled trials (RCTs) comparing endovascular aortic aneurysm repair (EVAR) to open surgical repair (OSR) may no longer be reflective of currently technology. Here the EVAR-1 trial and the ENGAGE registry are examined to assess potential improvements in outcomes with modern stent graft systems. METHODS: EVAR-1 was a multicenter, prospective, randomized controlled trial in the UK and patients were enrolled between 1999 and 2004 and treated with first- and second-generation devices. ENGAGE is an observational, nonrandomized, prospective registry that completed enrollment between 2009 and 2011. All ENGAGE patients were treated with the Endurant AAA Stent Graft System. A descriptive comparison of the published four-year outcomes of all-cause mortality (ACM), aneurysm-related mortality (ARM), rupture after elective EVAR, and reinterventions are reported. RESULTS: Through the four-year timepoint, freedom from ACM was 74.4% in the EVAR-1 Trial and 74.6% in the ENGAGE registry. ARM in the EVAR-1 trial was 4.2% and in the ENGAGE registry was 1.9%. Death due to rupture through four years was 1.6% (10/626) and 0.5% (6/1263) in the EVAR-1 and ENGAGE patients, respectively. In the EVAR-1 trial, the proportion of patients requiring at least one reintervention through the four-year timepoint was 19.3% (121/626) whereas in the ENGAGE registry, reinterventions occurred in 10.9% (138/1263) of patients. CONCLUSIONS: EVAR patient outcomes have improved since the time of the original EVAR vs. OSR trials and data from real-world registries should be considered a primary resource for developing new guidelines for patient selection and management.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".