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Endoleaks are the most common complication after endovascular aortic repair (EVAR). Their correct identification is one of the main objectives of surveillance protocols after EVAR. So far, computed tomography angiography (CTA), contrast-enhanced (CEUS) and Duplex ultrasound (DUS), as well as magnetic resonance angiography, have been investigated for their ability to detect endoleaks. In general, all technologies have distinct benefits and disadvantages, with CTA and CEUS emerging as the reference standard for surveillance after EVAR. However, they are both contrast-enhancer-dependent, and CTA additionally exposes patients to ionizing radiation. In the present study, we investigated B-Flow, a type of coded-excitation ultrasound that was specifically designed to optimize the visualization of blood flow, for its ability to detect endoleaks, and compared its performance to CEUS, CTA, and DUS. In total, 34 patients were included in the analysis that accumulated in 43 distinct B-Flow investigations. They underwent a total of 132 imaging investigations. Agreement between B-Flow and other imaging modalities was high (>80.0%), while inter-method reliability can be interpreted as good. However, with B-Flow, six and one endoleaks would have been missed compared to CEUS and CTA, respectively. Regarding endoleak classification, all metrics were lower but retained an adequate level of comparison. In a subset of patients requiring intervention, B-Flow had 100% accuracy regarding both endoleak detection and classification. Ultrasonography enables endoleak detection and classification without the need for pharmaceutical contrast enhancement or radiation. Ultrasound coded-excitation imaging in the application of B-Flow could further simplify surveillance after EVAR by offering adequate accuracy without requiring intravenous contrast enhancement. Our findings may promote subsequent investigations of coded-excitation imaging for endoleak detection and classification in the surveillance after EVAR.

Screening and diagnosing abdominal aortic aneurysms (AAA) are currently dependent on imaging studies such as ultrasound or computed tomography angiography. All imaging studies offer distinct advantages but also suffer from inherent limitations such as examiner dependency or ionizing radiation. Bioelectrical impedance analysis has previously been investigated with respect to its use in the detection of several cardiovascular and renal pathologies. The present pilot study assessed the feasibility of AAA detection based on bioimpedance analysis. In this single-center exploratory pilot study, measurements were conducted among three different cohorts: patients with AAA, end-stage renal disease patients without AAA, and healthy controls. The device used in the study, CombynECG, is an open-market accessible device for segmental bioelectrical impedance analysis. The data was preprocessed and used to train four different machine learning models on a randomized training sample (80% of the full dataset). Each model was then evaluated on a test set (20% of the full dataset). The total sample included 22 patients with AAA, 16 chronic kidney disease patients, and 23 healthy controls. All four models showed strong predictive performance in the test partitions. Specificity ranged from 71.4 to 100%, while sensitivity ranged from 66.7 to 100%. The best-performing model had 100% accuracy for classification when applied to the test sample. Additionally, an exploratory analysis to approximate the maximum AAA diameter was conducted. An association analysis revealed several impedance parameters that might possess predictive ability with respect to aneurysm size. AAA detection via bioelectrical impedance analysis is technically feasible and appears to be a promising technology for large-scale clinical studies and routine clinical screening assessments.