ArticleQuantitative imaging in medicine and surgery2025
The role of fast approximate estimation displacement forces in prediction of stent-graft-related endoleaks after endovascular abdominal aortic aneurysm repair.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The flow-induced displacement force (DF) exerted on the stent-graft (SG) by the pulsatile blood flow has been considered a potential cause of sealing failure and endoleaks following endovascular aneurysm repair (EVAR). However, its clinical adoption remains limited by time-consuming computational simulations. This study aimed to validate whether rapid morphology-derived DF parameters, quantified from preoperative imaging, can predict stent-graft-related endoleaks (SGELs). Methods: In this retrospective case-control study, we analyzed data from 217 patients who underwent EVAR between July 2011 and May 2020. Sixty-nine patients were diagnosed with SGEL while controls (n=148) were matched. DF parameters were derived from preoperative computed tomography angiography (CTA) images using a simplified momentum-based theory. The predictive performance of models integrating DF with oversizing ratio (OSR) was evaluated using area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: Among 217 patients (mean age 72±8 years; 85% male), 69 (32%) developed SGEL over a median follow-up of 24 months. Higher DF values were independently associated with SGEL [adjusted odds ratio (OR) =2.1; 95% confidence interval (CI): 1.4-3.2; P<0.001]. A model combining DF and OSR achieved superior discrimination (AUC =0.85; 95% CI: 0.80-0.90) compared to traditional anatomical risk factors alone (AUC =0.72; 95% CI: 0.65-0.79; P<0.01), with significant reclassification improvement (NRI =0.613, IDI =0.114; both P<0.001). Conclusions: Morphology-derived DF parameters, combined with OSR, provide a rapid and reproducible method to predict SGEL after EVAR. This approach may enhance preoperative risk stratification without requiring computationally intensive simulations. Further validation in prospective cohorts is needed to confirm its utility across diverse clinical settings.
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