How Quickly Do Asymptomatic Infrarenal Abdominal Aortic Aneurysms Grow and What Factors Affect Aneurysm Growth Rates? Analysis of a Single Centre Surveillance Cohort Database

Artificial intelligence-driven multiomics predictive model for abdominal aortic aneurysm subtypes to identify heterogeneous immune cell infiltration and predict disease progression

BACKGROUND

Abdominal aortic aneurysm (AAA) poses a significant health risk and is influenced by various compositional features. This study aimed to develop an artificial intelligence-driven multiomics predictive model for AAA subtypes to identify heterogeneous immune cell infiltration and predict disease progression. Additionally, we investigated neutrophil heterogeneity in patients with different AAA subtypes to elucidate the relationship between the immune microenvironment and AAA pathogenesis.

METHODS

This study enrolled 517 patients with AAA, who were clustered using k-means algorithm to identify AAA subtypes and stratify the risk. We utilized residual convolutional neural network 200 to annotate and extract contrast-enhanced computed tomography angiography images of AAA. A precise predictive model for AAA subtypes was established using clinical, imaging, and immunological data. We performed a comparative analysis of neutrophil levels in the different subgroups and immune cell infiltration analysis to explore the associations between neutrophil levels and AAA. Quantitative polymerase chain reaction, Western blotting, and enzyme-linked immunosorbent assay were performed to elucidate the interplay between CXCL1, neutrophil activation, and the nuclear factor (NF)-κB pathway in AAA pathogenesis. Furthermore, the effect of CXCL1 silencing with small interfering RNA was investigated.

RESULTS

Two distinct AAA subtypes were identified, one clinically more severe and more likely to require surgical intervention. The CNN effectively detected AAA-associated lesion regions on computed tomography angiography, and the predictive model demonstrated excellent ability to discriminate between patients with the two identified AAA subtypes (area under the curve, 0.927). Neutrophil activation, AAA pathology, CXCL1 expression, and the NF-κB pathway were significantly correlated. CXCL1, NF-κB, IL-1β, and IL-8 were upregulated in AAA. CXCL1 silencing downregulated NF-κB, interleukin-1β, and interleukin-8.

CONCLUSION

The predictive model for AAA subtypes demonstrated accurate and reliable risk stratification and clinical management. CXCL1 overexpression activated neutrophils through the NF-κB pathway, contributing to AAA development. This pathway may, therefore, be a therapeutic target in AAA.

Measurement of Abdominal Aortic Aneurysm Strain Using MR Deformable Image Registration: Accuracy and Relationship to Recent Aneurysm Progression

BACKGROUND

Management of asymptomatic abdominal aortic aneurysm (AAA) based on maximum aneurysm diameter and growth rate fails to preempt many ruptures. Assessment of aortic wall biomechanical properties may improve assessment of progression and rupture risk. This study aimed to assess the accuracy of AAA wall strain measured by cine magnetic resonance imaging (MRI) deformable image registration (MR strain) and investigate its relationship with recent AAA progression.

METHODS

The MR strain accuracy was evaluated in silico against ground truth strain in 54 synthetic MRIs generated from a finite element model simulation of an AAA patient's abdomen for different aortic pulse pressures, tissue motions, signal intensity variations, and image noise. Evaluation included bias with 95% confidence interval (CI) and correlation analysis. Association of MR strain with AAA growth rate was assessed in 25 consecutive patients with >6 months of prior surveillance, for whom cine balanced steady-state free-precession imaging was acquired at the level of the AAA as well as the proximal, normal-caliber aorta. Univariate and multivariate regressions were used to associate growth rate with clinical variables, maximum AAA diameter (D max ), and peak circumferential MR strain through the cardiac cycle. The MR strain interoperator variability was assessed using bias with 95% CI, intraclass correlation coefficient, and coefficient of variation.

RESULTS

In silico experiments revealed an MR strain bias of 0.48% ± 0.42% and a slope of correlation to ground truth strain of 0.963. In vivo, AAA MR strain (1.2% ± 0.6%) was highly reproducible (bias ± 95% CI, 0.03% ± 0.31%; intraclass correlation coefficient, 97.8%; coefficient of variation, 7.14%) and was lower than in the nonaneurysmal aorta (2.4% ± 1.7%). D max ( β = 0.087) and MR strain ( β = -1.563) were both associated with AAA growth rate. The MR strain remained an independent factor associated with growth rate ( β = -0.904) after controlling for D max .

CONCLUSIONS

Deformable image registration analysis can accurately measure the circumferential strain of the AAA wall from standard cine MRI and may offer patient-specific insight regarding AAA progression.

Editor's Choice - Systematic Review and Meta-Analysis of Contemporary Abdominal Aortic Aneurysm Growth Rates

OBJECTIVE

To evaluate the contemporary growth rate of small abdominal aortic aneurysms (AAAs) in view of recent epidemiological changes, such as decreasing smoking rates and establishment of population screening programmes.

DATA SOURCES

MEDLINE, CENTRAL, PsycINFO, Web of Science Core Collection, and OpenGrey databases.

REVIEW METHODS

Systematic review following the PRISMA guidelines. In October 2021, databases were queried for studies reporting on AAA growth rates published from 2015 onwards. The primary outcome was contemporary AAA growth rates in mm/year. Data were pooled in a random effects model meta-analysis, and heterogeneity was assessed through the I2 statistic. GRADE assessment of the findings was performed. The protocol was published in PROSPERO (CRD42022297404).

RESULTS

Of 8 717 titles identified, 43 studies and 28 277 patients were included: 1 241 patients from randomised controlled trials (RCTs), 23 941 from clinical observational studies, and 3 095 from radiological or translational research studies. The mean AAA growth rate was 2.38 mm/year (95% CI 2.16 - 2.60 mm/year; GRADE = low), with meta-regression analysis adjusted for baseline diameter showing an increase of 0.08 mm/year (95% CI 0.024 - 0.137 mm/year; p = .005) for each millimetre of increased baseline diameter. When analysed by study type, the growth rate estimated from RCTs was 1.88 mm/year (95% CI 1.69 - 2.06 mm/year; GRADE = high), while it was 2.31 mm/year (95% CI 1.95 - 2.67 mm/year; GRADE = moderate) from clinical observational studies, and 2.85 mm/year (95% CI 2.44 - 3.26 mm/year; GRADE = low) from translational and radiology based studies (p < .001). Heterogeneity was high, and small study publication bias was present (p = .003), with 27 studies presenting a moderate to high risk of bias. The estimated growth rate from low risk studies was 2.09 mm/year (95% CI 1.87 - 2.32; GRADE = high).

CONCLUSION

This study estimated a contemporaneous AAA growth rate of 2.38 mm/year, being unable to demonstrate any clinically meaningful AAA growth rate reduction concomitant with changed AAA epidemiology. This suggests that the RESCAN recommendations on small AAA surveillance are still valid. However, sub-analysis results from RCTs and high quality study data indicate potential lower AAA growth rates of 1.88 - 2.09 mm/year, findings that should be validated in a high quality prospective registry.

Early Relative Growth Rate of Abdominal Aortic Aneurysms and Future Risk of Rupture or Repair

OBJECTIVE

This study aimed to test whether the relative growth rate of subthreshold abdominal aortic aneurysms (AAAs) in the first 24 months of surveillance predicts the risk of future rupture or repair.

METHODS

This was a single centre retrospective observational analysis of all small (< 45 mm diameter) and medium (45 - 54 mm in men, 45 - 50 mm in women) AAAs entered into ultrasound surveillance between January 2002 and December 2019, which received ≥ 24 months of surveillance. Relative growth rates were calculated from measurements taken in the first 24 months of surveillance. The Kaplan-Meier method was used to estimate intervention and rupture free proportions five years following diagnosis for AAAs growing by < 5% and by ≥ 5% in the first 24 months of surveillance. Multivariable Cox regression analysis was used to further analyse this relationship by adjusting for factors found to be significantly associated with outcome in univariable analysis.

RESULTS

A total of 556 patients with AAAs (409 men, 147 women) were followed for ≥ 24 months. This included 431 small AAAs. Of these, 109 (25.3%) grew by < 5% in the first 24 months of surveillance and had a cumulative event free proportion of 0.98 ± 0.05 at five years compared with 0.78 ± 0.05 for the ≥ 5% growth group (p < .001). Of 125 medium AAAs, 26 (20.8%) grew by < 5% in the first 24 months of surveillance and had a cumulative event free proportion of 0.73 ± 0.11 at five years compared with 0.29 ± 0.13 for the ≥ 5% growth group (p = .024). Baseline diameter and early relative growth rate were strongly and independently predictive of future intervention or rupture with hazard ratios of 9.16 (95% CI 5.98 - 14.03, p < .001) and 4.46 (95% CI 2.45 - 8.14, p < .001), respectively.

CONCLUSION

The results suggest that slow expansion of small (< 45 mm) AAAs observed over an isolated 24 month period is indicative of a very low risk of rupture or repair in the medium term. Isolated growth rates may be a useful tool with which to triage low risk AAAs and prevent unnecessary surveillance.

Dynamic Contrast-Enhanced MRI in Abdominal Aortic Aneurysms as a Potential Marker for Disease Progression

BACKGROUND

Abdominal aortic aneurysms (AAAs) may rupture before reaching maximum diameter (Dmax ) thresholds for repair. Aortic wall microvasculature has been associated with elastin content and rupture sites in specimens, but its relation to progression is unknown.

PURPOSE

To investigate whether dynamic contrast-enhanced (DCE) MRI of AAA is associated with Dmax or growth.

STUDY TYPE

Prospective.

POPULATION

A total of 27 male patients with infrarenal AAA (mean age ± standard deviation = 75 ± 5 years) under surveillance with DCE MRI and 2 years of prior follow-up intervals with computed tomography (CT) or MRI.

FIELD STRENGTH/SEQUENCE

A 3-T, dynamic three-dimensional (3D) fast gradient-echo stack-of-stars volumetric interpolated breath-hold examination (Star-VIBE).

ASSESSMENT

Wall voxels were manually segmented in two consecutive slices at the level of Dmax . We measured slope to 1-minute and area under the curve (AUC) to 1 minute and 4 minutes of the signal intensity change postcontrast relative to that precontrast arrival, and, Ktrans , a measure of microvascular permeability, using the Patlak model. These were averaged over all wall voxels for association to Dmax and growth rate, and, over left/right and anterior/posterior quadrants for testing circumferential homogeneity. Dmax was measured orthogonal to the aortic centerline and growth rate was calculated by linear fit of Dmax measurements.

STATISTICAL TESTS

Pearson correlation and linear mixed effects models. A P value <0.05 was considered statistically significant.

RESULTS

In 44 DCE MRIs, mean Dmax was 45 ± 7 mm and growth rate in 1.5 ± 0.4 years of prior follow-up was 1.7 ± 1.2 mm per year. DCE measurements correlated with each other (Pearson r = 0.39-0.99) and significantly differed between anterior/posterior versus left/right quadrants. DCE measurements were not significantly associated with Dmax (P = 0.084, 0.289, 0.054 and 0.255 for slope, AUC at 1 minute and 4 minutes, and Ktrans , respectively). Slope and 4 minutes AUC significantly associated with growth rate after controlling for Dmax .

CONCLUSION

Contrast uptake may be increased in lateral aspects of the AAA. Contrast enhancement 1-minute slope and 4-minutes AUC may be associated with a period of recent AAA growth that is independent of Dmax .

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

Contrast-enhanced CT radiomics improves the prediction of abdominal aortic aneurysm progression

OBJECTIVES

To determine if three-dimensional (3D) radiomic features of contrast-enhanced CT (CECT) images improve prediction of rapid abdominal aortic aneurysm (AAA) growth.

METHODS

This longitudinal cohort study retrospectively analyzed 195 consecutive patients (mean age, 72.4 years ± 9.1) with a baseline CECT and a subsequent CT or MR at least 6 months later. 3D radiomic features were measured for 3 regions of the AAA, viz. the vessel lumen only; the intraluminal thrombus (ILT) and aortic wall only; and the entire AAA sac (lumen, ILT, and wall). Multiple machine learning (ML) models to predict rapid growth, defined as the upper tercile of observed growth (> 0.25 cm/year), were developed using data from 60% of the patients. Diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) in the remaining 40% of patients.

RESULTS

The median AAA maximum diameter was 3.9 cm (interquartile range [IQR], 3.3-4.4 cm) at baseline and 4.4 cm (IQR, 3.7-5.4 cm) at the mean follow-up time of 3.2 ± 2.4 years (range, 0.5-9 years). A logistic regression model using 7 radiomic features of the ILT and wall had the highest AUC (0.83; 95% confidence interval [CI], 0.73-0.88) in the development cohort. In the independent test cohort, this model had a statistically significantly higher AUC than a model including maximum diameter, AAA volume, and relevant clinical factors (AUC = 0.78, 95% CI, 0.67-0.87 vs AUC = 0.69, 95% CI, 0.57-0.79; p = 0.04).

CONCLUSION

A radiomics-based method focused on the ILT and wall improved prediction of rapid AAA growth from CECT imaging.

KEY POINTS

• Radiomic analysis of 195 abdominal CECT revealed that an ML-based model that included textural features of intraluminal thrombus (if present) and aortic wall improved prediction of rapid AAA progression compared to maximum diameter. • Predictive accuracy was higher when radiomic features were obtained from the thrombus and wall as opposed to the entire AAA sac (including lumen), or the lumen alone. • Logistic regression of selected radiomic features yielded similar accuracy to predict rapid AAA progression as random forests or support vector machines.

Why do guidelines recommend screening for abdominal aortic aneurysms, but not for asymptomatic carotid stenosis? A plea for a randomized controlled trial

BACKGROUND

Current guidelines do not recommend screening for asymptomatic carotid artery stenosis (AsxCS). The rationale behind this recommendation is that detection of AsxCS may lead to an unnecessary carotid intervention. In contrast, screening for abdominal aortic aneurysms is strongly recommended.

METHODS

A critical analysis of the literature was performed to evaluate the implications of detecting AsxCS.

RESULTS

Patients with AsxCS are at high risk for future stroke, myocardial infarction and vascular death. Population-wide screening for AsxCS should not be recommended. Additionally, screening of high-risk individuals for AsxCS with the purpose of identifying candidates for a carotid intervention is inappropriate. Instead, selective screening for AsxCS should be considered and should be viewed as an opportunity to identify individuals at high risk for atherosclerotic cardiovascular disease and future cardiovascular events for the timely initiation of intensive medical therapy and risk factor modification.

CONCLUSIONS

Although mass screening should not be recommended, there are several arguments suggesting that selective screening for AsxCS should be considered. The rationale supporting such selective screening is to optimize risk factor control and to initiate intensive medical therapy for prevention of future cardiovascular events, rather than to identify candidates for an intervention.

Antithrombotic Therapy for Aortic Aneurysms: A Systematic Review and Meta-Analysis

OBJECTIVES

The role of antithrombotic therapy in the management of aortic and peripheral aneurysms is unclear. This systematic review and meta-analysis aimed to assess the impact of antithrombotics on clinical outcomes for aortic and peripheral aneurysms.

METHODS

Medline, Embase, and CENTRAL databases were searched. Randomised controlled trials and observational studies investigating the effect of antithrombotic therapy on clinical outcomes for patients with any aortic or peripheral artery aneurysm were included.

RESULTS

Fifty-nine studies (28 with antiplatelet agents, 12 anticoagulants, two intra-operative heparin, and 16 any antithrombotic agent) involving 122 102 patients were included. Abdominal aortic aneurysm (AAA) growth rate was not significantly associated with the use of antiplatelet therapy (SMD -0.36 mm/year; 95% CI -0.75 - 0.02; p = .060; GRADE certainty: very low). Antithrombotics were associated with increased 30 day mortality for patients with AAAs undergoing intervention (OR 2.30; 95% CI 1.51 - 3.51; p < .001; GRADE certainty: low). Following intervention, antiplatelet therapy was associated with reduced long term all cause mortality (HR 0.84; 95% CI 0.76 - 0.92; p < .001; GRADE certainty: moderate), whilst anticoagulants were associated with increased all cause mortality (HR 1.64; 95% CI 1.14 - 2.37; p = .008; GRADE certainty: very low), endoleak within three years (OR 1.99; 95% CI 1.10 - 3.60; p = .020; I² = 60%; GRADE certainty: very low), and an increased re-intervention rate at one year (OR 3.25; 95% CI 1.82 - 5.82; p < .001; I² = 35%; GRADE certainty: moderate). Five studies examined antithrombotic therapy for popliteal aneurysms. Meta-analysis was not possible due to heterogeneity.

CONCLUSIONS

There was a lack of high quality data examining antithrombotic therapy for patients with aneurysms. Antiplatelet therapy was associated with a reduction in post-intervention all cause mortality for AAA, whilst anticoagulants were associated with an increased risk of all cause mortality, endoleak, and re-intervention. Large, well designed trials are still required to determine the therapeutic benefits of antithrombotic agents in this setting.

Management of Concomitant Abdominal Aortic Aneurysm and Intra-abdominal, Retroperitoneal Malignancy

BACKGROUND/AIM

As the population ages, there are increasing findings of coincidental diseases such as abdominal aortic aneurysm (AAA) and intra-abdominal, retroperitoneal malignancy. The aim of this study was to propose an optimal treatment procedure for these patients.

PATIENTS AND METHODS

Over a twenty-year-period, surgery was performed on a total of 1,098 patients with AAA and 32 (2.9%) patients with AAA and intra-abdominal, retroperitoneal malignancy: 18 renal, 6 colorectal carcinomas, 3 carcinomas of the small intestine, 3 primary liver tumours, 1 stomach carcinoma and 1 teratoma. The median age of patients was 72.5 years, there were 20 men (62.5%) and 12 women (37.5%). A one-stage procedure was performed on 19 patients (59.4%), and a two-stage procedure on 13 (40.6%) patients.

RESULTS

The average time of hospitalization was 12.4±6.9 days (median=11.0 days) for one-stage procedure, for a two-stage procedure 21.3±9.3 days (median=20.0 days), p=0.0045. Seven patients (21.9%) died within 30 days after the operation. All the deaths were in the group of one-stage procedures (p=0.0252). The 1-, 3- and 5-year overall survival for patients following one-stage and twostage procedures was 61.0/56.3/51.5% and 89.0/79.9/53.0% respectively (p=0.1199).

CONCLUSION

Symptomatic disease must be resolved first. Two-stage procedures are the method of choice and offer better short-term results compared to one-stage procedures.

Abdominal aortic aneurysms

Abdominal aortic aneurysms (AAA) are prevalent among older adults and can cause significant morbidity and mortality if not addressed in a timely fashion. Their etiology remains the topic of continued investigation. Known causes include trauma, infection, and inflammatory disorders. Risk factors include cigarette smoking, advanced age, dyslipidemia, hypertension, and coronary artery disease. The pathophysiology of the disease is related to an initial arterial insult causing a cascade of inflammation and extracellular matrix protein breakdown by proteinases leading to arterial wall weakening. When identified early, aneurysms must be monitored for size, growth rate, and other factors which could increase the risk of rupture. Factors predisposing to rupture include size, active smoking, rate of growth, aberrant biomechanical properties of the aneurysmal sac, and female sex. Medical management includes the control of risk factors that may prevent growth, stabilize the aneurysm, and prevent rupture. Surgical management prevents rupture of high risk aneurysms, most commonly predicted by size. Less frequently, surgical management is required when the aneurysm has ruptured. Surgery involves a multidisciplinary approach to evaluate the patient's risk profile and to develop an operative plan involving either an endovascular or an open surgical repair. The patient must be carefully monitored post-operatively for complications and, in the case of endovascular repairs, for endoleaks. AAA management has evolved rapidly in recent years. Technical and technological advances have transformed the diagnosis and treatment of this disease.

Validation of natural language processing to determine the presence and size of abdominal aortic aneurysms in a large integrated health system

OBJECTIVE

Previous studies of the natural history of abdominal aortic aneurysms (AAAs) have been limited by small cohort sizes or heterogeneous analyses of pooled data. By quickly and efficiently extracting imaging data from the health records, natural language processing (NLP) has the potential to substantially improve how we study and care for patients with AAAs. The aim of the present study was to test the ability of an NLP tool to accurately identify the presence or absence of AAAs and detect the maximal abdominal aortic diameter in a large dataset of imaging study reports.

METHODS

Relevant imaging study reports (n = 230,660) from 2003 to 2017 were obtained for 32,778 patients followed up in a prospective aneurysm surveillance registry within a large, diverse, integrated healthcare system. A commercially available NLP algorithm was used to assess the presence of AAAs, confirm the absence of AAAs, and extract the maximal diameter of the abdominal aorta, if stated. A blinded expert manual review of 18,000 randomly selected imaging reports was used as the reference standard. The positive predictive value (PPV or precision), sensitivity (recall), and the kappa statistics were calculated.

RESULTS

Of the randomly selected 18,000 studies that underwent expert manual review, 48.7% were positive for AAAs. In confirming the presence of an AAA, the interrater reliability of the NLP compared with the expert review showed a kappa value of 0.84 (95% confidence interval [CI], 0.83-0.85), with a PPV of 95% and sensitivity of 88.5%. The NLP algorithm showed similar results for confirming the absence of an AAA, with a kappa of 0.79 (95% CI, 0.799-0.80), PPV of 77.7%, and sensitivity of 91.9%. The kappa, PPV, and sensitivity of the NLP for correctly identifying the maximal aortic diameter was 0.88 (95% CI, 0.87-0.89), 88.8%, and 88.2% respectively.

CONCLUSIONS

The use of NLP software can accurately analyze large volumes of radiology report data to detect AAA disease and assemble a contemporary aortic diameter-based cohort of patients for longitudinal analysis to guide surveillance, medical management, and operative decision making. It can also potentially be used to identify from the electronic medical records pre- and postoperative AAA patients "lost to follow-up," leverage human resources engaged in the ongoing surveillance of patients with AAAs, and facilitate the construction and implementation of AAA screening programs.

Rationale for screening selected patients for asymptomatic carotid artery stenosis

Stroke is a leading cause of death and disability worldwide. Approximately 15% of all first-ever strokes occur due to atheroembolism from a previously undetected/untreated asymptomatic carotid stenosis (ACS). Despite that, international guidelines do not recommend screening for ACS. The rationale for not recommending screening include: (a) the harm associated with screening, (b) the questionable clinical benefit associated with surgery, (c) the lack of proven reduction in the risk of stroke, (d) the large number of false positive/false negative tests, and (e) the cost-effectiveness of such screening programs. A critical analysis of each of these arguments is presented. Patients with ACS have a very high risk of all-cause and cardiac mortality. Detection of ACS should not be viewed as an indication for surgery, but rather as an opportunity to implement best medical treatment (BMT) and lifestyle changes to prevent not only strokes, but also cardiac events. The implementation of screening programs for abdominal aortic aneurysms (AAAs) has led to a considerable reduction in the number of ruptured AAAs and AAA-related deaths. Similarly, screening high-risk individuals for ACS would enable timely identification of patients with ACS and implementation of BMT and lifestyle measures to prevent future strokes and cardiac events.

YouTube as a source of patient information for abdominal aortic aneurysms

OBJECTIVE

Patients are increasingly referring to the Internet after a diagnosis of vascular disease. This study was performed to quantitatively define the accuracy and reliability of information on YouTube regarding abdominal aortic aneurysms (AAA).

METHODS

A systematic search of YouTube was conducted using multiple AAA-specific keywords. The default YouTube search setting of "relevance" was used to replicate an average search attempt, and the first 50 results from each keyword search were reviewed and analyzed by two independent reviewers. Descriptive characteristics, Journal of the American Medical Association Score, modified DISCERN score, Video Power Index, and a novel scoring system for the management of AAAs, the AAA-Specific Score (AAASS), were used to record data. Inter-rater agreement was analyzed using intraclass correlation coefficient estimates and the Kruskal-Wallis test was used for intergroup comparisons.

RESULTS

Fifty-one videos were included for analysis. The mean Journal of the American Medical Association Score, DISCERN, and AAASS values among videos were 1.74/4.00 (standard deviation [SD], 0.84), 2.37/5.00 (SD, 0.97), and 6.63/20.00 (SD, 3.23), respectively. Of all the included videos, 78% were educational in nature, 14% were patient testimonials, and 8% were news programs. Based on the AAASS, the majority of analyzed videos fell into the poor category (41%), followed next by the very poor (31%), moderately useful (25%), very useful (2%), and exceptional (0%) categories. Videos by nonphysicians were significantly more popular (P < .05) than vascular surgeon sources.

CONCLUSIONS

Although variable in source and content, the completeness and reliability of information offered on YouTube for AAA diagnosis and treatment is poor. Patients watching YouTube for information on their AAA diagnosis are receiving an incomplete and perhaps misleading picture of available diagnostic and treatment options. Given that vascular surgeons are likely to be affected by unrealistic treatment expectations from patients accessing online materials regarding AAA, it is important to acknowledge the nature of content on these platforms.