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Record W4407694692 · doi:10.1016/j.ejvs.2025.02.017

External Validation of Eight Ruptured Abdominal Aortic Aneurysm Mortality Prediction Models Demonstrates Limited Predictive Accuracy

2025· article· en· W4407694692 on OpenAlexaboutno aff
Shimena Li, Muhammad S Mazroua, Katherine M. Reitz, Amanda R. Phillips, Edith Tzeng, Nathan L. Liang

Bibliographic record

VenueEuropean Journal of Vascular and Endovascular Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicAortic aneurysm repair treatments
Canadian institutionsnot available
FundersNational Institute on AgingNational Institutes of HealthNational Heart, Lung, and Blood InstituteUniversity of PittsburghBurroughs Wellcome Fund
KeywordsMedicinePredictive valueAbdominal aortic aneurysmPredictive value of testsAortic aneurysmPredictive modellingInternal medicineCardiologyRadiologyAneurysmStatistics

Abstract

fetched live from OpenAlex

Objective Over a dozen ruptured abdominal aortic aneurysm (rAAA) mortality risk prediction models currently exist; however, lack of external validation limits their applicability. This study aimed to evaluate the accuracy of eight common rAAA mortality risk prediction models in a large, contemporary, external validation cohort. Methods A retrospective review of rAAA repairs at a multicentre integrated regional healthcare system with large central quaternary referral facility (2010 – 2020) was performed. Eight models were used to predict 30 day post-operative death, including the Updated Glasgow Aneurysm Score (GAS), Vascular Study Group of New England rAAA Risk Score, Harborview Pre-operative rAAA Risk Score, Modified Harborview Risk Score, Vancouver Scoring System (VSS), Artificial Neural Network Score, Dutch Aneurysm Score, and Edinburgh Ruptured Aneurysm Score. The models were assessed for discrimination, calibration, and clinical utility using receiver operating characteristic curves (area under the curve [AUC]), Hosmer–Lemeshow χ 2 test, Brier scores, and decision curve analysis. The proportion of unexpected survivors (survival despite > 80% predicted 30 day death) to expected deaths was compared across calculators, and both groups were compared using the model demonstrating the highest unexpected survival frequency. Results Three hundred and fifteen rAAA repairs were included (mean age 73.6 ± 10.0 years; 72.1% male; 49.8% open repair) with a 30 day mortality rate of 32.1%. Three models had fair discrimination (AUC ≥ 0.70), with GAS having the highest AUC (0.74, 95% confidence interval 0.68 – 0.79). All models demonstrated poor to adequate calibration. Using VSS, unexpected survivors ( n = 25) had less pre-operative shock (72% vs. 96%; p = .050) and statistically significantly less coagulopathy (median international normalised ratio 1.2 [interquartile range 1.1, 1.5] vs. 1.8 [1.3, 2.2]; p = .015) compared with expected deaths ( n = 23). Conclusion Current rAAA risk prediction models demonstrated only fair discrimination and poor to adequate calibration. These findings suggest that existing risk prediction models have not sufficiently captured important physiological characteristics associated with rAAA death and should be applied cautiously to clinical practice.

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 imitation

Not 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.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.057
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.257
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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