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Record W4410552777 · doi:10.1016/j.jvsvi.2025.100253

Geographic disparities in perioperative and mid-term outcomes after elective infrarenal endovascular aneurysm repair

2025· article· en· W4410552777 on OpenAlexafffundabout
Lisa Vi, Rashi Gupta, Naomi Eisenberg, Miranda Witheford

Bibliographic record

VenueJVS-Vascular Insights · 2025
Typearticle
Languageen
FieldMedicine
TopicAortic aneurysm repair treatments
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersCanadian Society for Vascular Surgery
KeywordsPerioperativeMedicineEndovascular aneurysm repairAneurysmTerm (time)CardiologyInternal medicineSurgeryAbdominal aortic aneurysm

Abstract

fetched live from OpenAlex

Objective This study aimed to assess the impact of geographic location on mid-term postoperative outcomes after EVAR across 4 Canadian tertiary care institutions. We hypothesized that patients from rural or non-local areas may experience worse perioperative outcomes because of loss to follow-up, care transfers, and suboptimal surveillance. These factors could contribute to increased non-index readmissions and higher mortality rates following elective endovascular aneurysm repair (EVAR). Methods Elective infrarenal EVAR patients from four Canadian tertiary care institutions were assessed through a retrospective analysis of the Vascular Quality Initiative (VQI) database, stratified by rural versus urban postal codes. A subgroup analysis was also conducted using a 10-year retrospective dataset from Toronto General Hospital (TGH) and the VQI to evaluate EVAR outcomes based on patient geography. Primary outcomes included differences in loss to follow-up, overall survival, and reintervention-free survival. Secondary outcomes included differences in baseline characteristics, perioperative outcomes, and imaging surveillance. Statistical analyses consisted of t-tests for continuous variables, chi-square tests for categorical variables, and Kaplan-Meier survival analysis with log-rank tests. Results Elective EVAR outcomes from four Canadian hospitals (n=1,342 patients) were analyzed by rural versus urban status. Rural patients demonstrated higher postoperative imaging rates but were more likely to be lost to follow-up. Aggregate data revealed a survival advantage for urban patients at 12 months postoperatively. A subgroup analysis from a single tertiary care institution included 491 patients who underwent elective infrarenal EVAR. Patients were categorized by postal code as local versus non-local (80.9% and 19.1%, respectively) and urban versus rural (94.9% and 5.1%, respectively). The median follow-up duration was 32 months (IQR 51). Local and urban patients had significantly higher rates of follow-up within the first two months (local: 84.6% vs. non-local: 57.4%, p<0.001; urban: 80.2% vs. rural: 64.0%, p=0.050) and more frequent imaging between 6 and 12 months (local: 69.5% vs. non-local: 43.6%, p<0.001; urban: 65.9% vs. rural: 40.0%, p=0.008). However, survival analysis did not demonstrate significant differences in estimated mean postoperative survival between groups (urban: 9.19 years vs. rural: 9.87 years, p=0.162; local: 9.15 years vs. non-local: 9.72 years, p=0.219). Conclusions At the national level, four Canadian VQI centres demonstrated higher rates of loss to follow-up and decreased one-year survival among patients from rural communities. In contrast, a subgroup analysis from our institution revealed that rural and non-local patients received fewer follow-up appointments and underwent less postoperative imaging; however, no significant differences in mortality or reintervention were observed. The influence of geographic distance and rurality status on follow-up and imaging highlights a potential vulnerability to aneurysm-related mortality, warranting further investigation.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.254
Teacher spread0.247 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
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

Citations0
Published2025
Admission routes3
Has abstractyes

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