An Expert Delphi Consensus on Risk Factors for Adverse Events After Endovascular Aortic Aneurysm Repair: Tier 1 Study From the International RIsk Stratification in EVAR (IRIS-EVAR) Working Group
Bibliographic record
Abstract
PURPOSE: Tier 1 of the International RIsk Stratification in EndoVascular Aneurysm Repair (IRIS-EVAR) project aimed to identify important risk factors for adverse events following endovascular aneurysm repair (EVAR). MATERIALS AND METHODS: Initially, the steering committee proposed a number of risk factors for adverse events following EVAR. A Delphi consensus was performed as expert panelists were presented with risk factors and provided the opportunity to propose additional risk factors during the process. Experts in EVAR completed an online survey via 3 structured rounds. The first round opened in July 2022, and the third round closed in December 2022. Panelists rated risk factors using a 4-point Likert scale. Consensus was defined as >70% of participants agreeing/strongly agreeing or disagreeing/strongly disagreeing with a statement in each round. RESULTS: Thirty-five panelists from 12 countries completed the 3 rounds of surveys. Of a total of 64 individual risk factors assessed by the panelists, 37 (58%) had consensus that they were important for adverse events following EVAR. Risk factors were stratified in 4 domains: 14 (38%) were related to preoperative anatomy, 3 (8%) related to the aortic device selection, 8 (22%) related to the procedure performance, and 12 (32%) related to postoperative surveillance. Factors with the highest consensus in each domain were as follows: proximal aortic neck length <15 mm (98% consensus), anatomy non-compliant with instructions for use (94% consensus), length of achieved proximal aortic neck post implantation <10 mm (98% consensus), and non-satisfactory seal at landing or overlapping zones/sac expansion/kink or stenosis (100% consensus each), respectively. CONCLUSIONS: Clinically important risk factors for adverse events after EVAR were identified via expert consensus. These factors will be used to develop an expert consensus-informed risk stratification and surveillance strategies.Clinical impactThis is the first study to apply an in-depth Delphi methodology to achieve an expert consensus on risk factors for adverse events after endovascular aneurysm repair (EVAR). Important risk factors were stratified in 4 domains: preoperative anatomy (14 factors), aortic device (3 factors), EVAR procedure (8 factors), and postoperative surveillance (12 factors). This study will potentially influence future clinical practice by providing evidence informed by experts regarding predictors of adverse events following EVAR that can be taken into account during decision making and developing post-EVAR surveillance strategies. These findings will inform a risk stratification tool for everyday use by vascular surgeons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".