International opinion on priorities in research for small abdominal aortic aneurysms and the potential path for research to impact clinical management

Proprotein convertase subtilisin/kexin type 9 as a drug target for abdominal aortic aneurysm

PURPOSE OF REVIEW

There are no current drug therapies to limit abdominal aortic aneurysm (AAA) growth. This review summarizes evidence suggesting that inhibiting proprotein convertase subtilisin/kexin type 9 (PCSK9) may be a drug target to limit AAA growth.

RECENT FINDINGS

Mendelian randomization studies suggest that raised LDL and non-HDL-cholesterol are causal in AAA formation. PCSK9 was reported to be upregulated in human AAA samples compared to aortic samples from organ donors. PCSK9 gain of function viral vectors promoted aortic expansion in C57BL/6 mice infused with angiotensin II. The effect of altering PCSK9 expression in the aortic perfusion elastase model was reported to be inconsistent. Mutations in the gene encoding PCSK9, which increase serum cholesterol, were associated with increased risk of human AAA. Patients with AAA also have a high risk of cardiovascular death, myocardial infarction and stroke. Recent research suggests that PCSK9 inhibition would substantially reduce the risk of these events.

SUMMARY

Past research suggests that drugs that inhibit PCSK9 have potential as a novel therapy for AAA to both limit aneurysm growth and reduce risk of cardiovascular events. A large multinational randomized controlled trial is needed to test if PCSK9 inhibition limits AAA growth and cardiovascular events.

Systematic Review Examining the Association Between Angiotensin Converting Enzyme Inhibitor or Angiotensin Receptor Blocker Prescription and Abdominal Aortic Aneurysm Growth and Events

OBJECTIVE

Whether angiotensin II blockade is an effective medical treatment for abdominal aortic aneurysms (AAAs) has not been established. This systematic review and meta-analysis aimed to determine the association between angiotensin converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB) prescription and AAA growth and events.

DATA SOURCES

MEDLINE, Embase, Scopus, Web of Science, and the Cochrane Library databases were searched from their inception to 4 January 2024, with no language restrictions.

REVIEW METHODS

The five databases were searched for randomised controlled trials (RCTs) and observational studies reporting the association between ACEi or ARB prescription and AAA growth, repair, or rupture. The primary outcome was AAA growth, with secondary outcomes of AAA rupture, AAA repair, and AAA related events (rupture and repair combined). Risk of bias was assessed using the Risk of Bias 2 tool for RCTs and with a modified Newcastle-Ottawa scale for observational studies. Certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE). Random effects models were used for meta-analyses.

RESULTS

Eleven studies (two RCTs, eight observational studies, and one meta-analysis of individual patient data from seven populations) involving 58 022 patients were included. ACEi prescription was not associated with a statistically significant reduction in AAA growth (standard mean difference 0.01 mm/year, 95% confidence interval [CI] -0.26 - 0.28; p = .93; I2 = 98%) or AAA repair (odds ratio [OR] 0.73, 95% CI 0.50 - 1.09; p = .65; I2 = 61%), but was associated with a statistically significantly lower risk of AAA rupture (OR 0.87, 95% CI 0.81 - 0.93; p < .001; I2 = 26%) and AAA related events (OR 0.82, 95% CI 0.72 - 0.95; p = .006; I2 = 80%). ARB prescription was not associated with significantly reduced AAA growth or a lower risk of AAA related events. The two RCTs had a low risk of bias, with one observational study having low, seven moderate, and one high risk of bias. All of the findings had a very low certainty of evidence based on the GRADE analysis.

CONCLUSION

There was no association between ACEi or ARB prescription and AAA growth, but ACEi prescription was associated with a reduced risk of AAA rupture and AAA related events with very low certainty of evidence.

Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations

Prior studies have shown that computational fluid dynamics (CFD) simulations help assess patient-specific hemodynamics in abdominal aortic aneurysms (AAAs); patient-specific hemodynamic stressors are frequently used to predict an AAA's growth. Previous studies have utilized both laminar and turbulent simulation models to simulate hemodynamics. However, the impact of different CFD simulation models on the predictive modeling of AAA growth remains unknown and is thus the knowledge gap that motivates this study. Specifically, CFD simulations were performed for 70 AAA models derived from 70 patients' computed tomography angiography (CTA) data with known growth status (i.e., fast-growing [> 5 mm/yr] or slowly growing [< 5 mm/yr]). We used laminar and large eddy simulation (LES) models to obtain hemodynamic parameters to predict AAAs' growth status. Predicting the growth status of AAAs was based on morphological, hemodynamic, and patient health parameters in conjunction with three classical machine learning (ML) classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and generalized linear model (GLM). Our preliminary results estimated aneurysmal flow stability and wall shear stress (WSS) were comparable in both laminar and LES flow simulations. Moreover, computed WSS and velocity-related hemodynamic variables obtained from the laminar and LES simulations showed comparable abilities in differentiating the growth status of AAAs. More importantly, the predictive modeling performance of the three ML classifiers mentioned above was similar, with less than a 2% difference observed (p-value > 0.05). In closing, our findings suggest that two different flow simulations investigated did not significantly affect outcomes of computational hemodynamics and predictive modeling of AAAs' growth status, given the data investigated.

Small AAAs: Recommendations for Rodent Model Research for the Identification of Novel Therapeutics

CLINICAL PROBLEM

Most abdominal aortic aneurysms (AAAs) are small with low rupture risk (<1%/y) when diagnosed but slowly expand to ≥55 mm and undergo surgical repair. Patients and clinicians require medications to limit AAA growth and rupture, but drugs effective in animal models have not translated to patients.

RECOMMENDATIONS FOR INCREASING TRANSLATION FROM MOUSE MODELS

Use models that simulate human AAA tissue pathology, growth patterns, and rupture; focus on the clinically relevant outcomes of growth and rupture; design studies with the rigor required of human clinical trials; monitor AAA growth using reproducible ultrasound; and perform studies in both males and females.

SUMMARY OF STRENGTHS AND WEAKNESSES OF MOUSE MODELS

The aortic adventitial elastase oral β-aminopropionitrile model has many strengths including simulating human AAA pathology and modeling prolonged aneurysm growth. The Ang II (angiotensin II) model performed less well as it better simulates acute aortic syndrome than AAA. The elastase plus TGFβ (transforming growth factor-β) blocking antibody model displays a high rupture rate, making prolonged monitoring of AAA growth not feasible. The elastase perfusion and calcium chloride models both display limited AAA growth.

The potential involvement of glycocalyx disruption in abdominal aortic aneurysm pathogenesis

BACKGROUND

Abdominal aortic aneurysm is a weakening and expansion of the abdominal aorta. Currently, there is no drug treatment to limit abdominal aortic aneurysm growth. The glycocalyx is the outermost layer of the cell surface, mainly composed of glycosaminoglycans and proteoglycans.

OBJECTIVE

The aim of this review was to identify a potential relationship between glycocalyx disruption and abdominal aortic aneurysm pathogenesis.

METHODS

A narrative review of relevant published research was conducted.

RESULTS

Glycocalyx disruption has been reported to enhance vascular permeability, impair immune responses, dysregulate endothelial function, promote extracellular matrix remodeling and modulate mechanotransduction. All these effects are implicated in abdominal aortic aneurysm pathogenesis. Glycocalyx disruption promotes inflammation through exposure of adhesion molecules and release of proinflammatory mediators. Glycocalyx disruption affects how the endothelium responds to shear stress by reducing nitric oxide availabilty and adversely affecting the storage and release of several antioxidants, growth factors, and antithromotic proteins. These changes exacerbate oxidative stress, stimulate vascular smooth muscle cell dysfunction, and promote thrombosis, all effects implicated in abdominal aortic aneurysm pathogenesis. Deficiency of key component of the glycocalyx, such as syndecan-4, were reported to promote aneurysm formation and rupture in the angiotensin-II and calcium chloride induced mouse models of abdominal aortic aneurysm.

CONCLUSION

This review provides a summary of past research which suggests that glycocalyx disruption may play a role in abdominal aortic aneurysm pathogenesis. Further research is needed to establish a causal link between glycocalyx disruption and abdominal aortic aneurysm development.

Machine learning algorithms for the prognostication of abdominal aortic aneurysm progression: a systematic review

INTRODUCTION

Abdominal aortic aneurysm (AAA), often characterized by an abdominal aortic diameter over 3.0 cm, is managed through screening, surveillance, and surgical intervention. AAA growth can be heterogeneous and rupture carries a high mortality rate, with size and certain risk factors influencing rupture risk. Research is ongoing to accurately predict individual AAA growth rates for personalized management. Machine learning, a subset of artificial intelligence, has shown promise in various medical fields, including endoleak detection post-EVAR. However, its application for predicting AAA growth remains insufficiently explored, thus necessitating further investigation. Subsequently, this paper aims to summarize the current status of machine learning in predicting AAA growth.

EVIDENCE ACQUISITION

A systematic database search of Embase, MEDLINE, Cochrane, PubMed and Google Scholar from inception till December 2022 was conducted of original articles that discussed the use of machine learning in predicting AAA growth using the aforementioned databases.

EVIDENCE SYNTHESIS

Overall, 2742 articles were extracted, of which seven retrospective studies involving 410 patients were included using a predetermined criteria. Six out of seven studies applied a supervised learning approach for their machine learning (ML) models, with considerable diversity observed within specific ML models. The majority of the studies concluded that machine learning models perform better in predicting AAA growth in comparison to reference models. All studies focused on predicting AAA growth over specified durations. Maximal luminal diameter was the most frequently used indicator, with alternative predictors being AAA volume, ILT (intraluminal thrombus) and flow-medicated diameter (FMD).

CONCLUSIONS

The nascent field of applying machine learning (ML) for Abdominal Aortic Aneurysm (AAA) expansion prediction exhibits potential to enhance predictive accuracy across diverse parameters. Future studies must emphasize evidencing clinical utility in a healthcare system context, thereby ensuring patient outcome improvement. This will necessitate addressing key ethical implications in establishing prospective studies related to this topic and collaboration among pivotal stakeholders within the AI field.

Radiomic-based Textural Analysis of Intraluminal Thrombus in Aortic Abdominal Aneurysms: A Demonstration of Automated Workflow

Our main objective is to investigate how the structural information of intraluminal thrombus (ILT) can be used to predict abdominal aortic aneurysms (AAA) growth status through an automated workflow. Fifty-four human subjects with ILT in their AAAs were identified from our database; those AAAs were categorized as slowly- (< 5 mm/year) or fast-growing (≥ 5 mm/year) AAAs. In-house deep-learning image segmentation models were used to generate 3D geometrical AAA models, followed by automated analysis. All features were fed into a support vector machine classifier to predict AAA's growth status.The most accurate prediction model was achieved through four geometrical parameters measuring the extent of ILT, two parameters quantifying the constitution of ILT, antihypertensive medication, and the presence of co-existing coronary artery disease. The predictive model achieved an AUROC of 0.89 and a total accuracy of 83%. When ILT was not considered, our prediction's AUROC decreased to 0.75 (P-value < 0.001).

Computerized Differentiation of Growth Status for Abdominal Aortic Aneurysms: A Feasibility Study

Fast-growing abdominal aortic aneurysms (AAA) have a high rupture risk and poor outcomes if not promptly identified and treated. Our primary objective is to improve the differentiation of small AAAs' growth status (fast versus slow-growing) through a combination of patient health information, computational hemodynamics, geometric analysis, and artificial intelligence. 3D computed tomography angiography (CTA) data available for 70 patients diagnosed with AAAs with known growth status were used to conduct geometric and hemodynamic analyses. Differences among ten metrics (out of ninety metrics) were statistically significant discriminators between fast and slow-growing groups. Using a support vector machine (SVM) classifier, the area under receiving operating curve (AUROC) and total accuracy of our best predictive model for differentiation of AAAs' growth status were 0.86 and 77.50%, respectively. In summary, the proposed analytics has the potential to differentiate fast from slow-growing AAAs, helping guide resource allocation for the management of patients with AAAs.

AI-powered assessment of biomarkers for growth prediction of abdominal aortic aneurysms

Objective

The purpose of this study was to employ biomechanics-based biomarkers to locally characterize abdominal aortic aneurysm (AAA) tissue and investigate their relation to local aortic growth by means of an artificial intelligence model.

Methods

The study focused on a population of 36 patients with AAAs undergoing serial monitoring with electrocardiogram-gated multiphase computed tomography angiography acquisitions. The geometries of the aortic lumen and wall were reconstructed from the baseline scans and used for the baseline assessment of regional aortic weakness with three functional biomarkers, time-averaged wall-shear stress, in vivo principal strain, and intra-luminal thrombus thickness. The biomarkers were encoded as regional averages on axial and circumferential sections perpendicularly to the aortic centerline. Local diametric growth was obtained as difference in diameter between baseline and follow-up at the level of each axial section. An artificial intelligence model was developed to predict accelerated aneurysmal growth with the Extra Trees algorithm used as a binary classifier where the positive class represented regions that grew more than 2.5 mm/year. Additional clinical biomarkers, such as maximum aortic diameter at baseline, were also investigated as predictors of growth.

Results

The area under the curve for the constructed receiver operating characteristic curve for the Extra Trees classifier showed a very good performance in predicting relevant aortic growth (area under the curve = 0.92), with the three biomechanics-based functional biomarkers being objectively selected as the main predictors of growth.

Conclusions

The use of features based on the functional and local characterization of the aortic tissue resulted in a superior performance in terms of growth prediction when compared with models based on geometrical assessments. With rapid growth linked to increasing risk for patients with AAAs, the ability to access functional information related to tissue weakening and disease progression at baseline has the potential to support early clinical decisions and improve disease management.

Pathogenesis and management of abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) causes ∼170 000 deaths annually worldwide. Most guidelines recommend asymptomatic small AAAs (30 to <50 mm in women; 30 to <55 mm in men) are monitored by imaging and large asymptomatic, symptomatic, and ruptured AAAs are considered for surgical repair. Advances in AAA repair techniques have occurred, but a remaining priority is therapies to limit AAA growth and rupture. This review outlines research on AAA pathogenesis and therapies to limit AAA growth. Genome-wide association studies have identified novel drug targets, e.g. interleukin-6 blockade. Mendelian randomization analyses suggest that treatments to reduce low-density lipoprotein cholesterol such as proprotein convertase subtilisin/kexin type 9 inhibitors and smoking reduction or cessation are also treatment targets. Thirteen placebo-controlled randomized trials have tested whether a range of antibiotics, blood pressure-lowering drugs, a mast cell stabilizer, an anti-platelet drug, or fenofibrate slow AAA growth. None of these trials have shown convincing evidence of drug efficacy and have been limited by small sample sizes, limited drug adherence, poor participant retention, and over-optimistic AAA growth reduction targets. Data from some large observational cohorts suggest that blood pressure reduction, particularly by angiotensin-converting enzyme inhibitors, could limit aneurysm rupture, but this has not been evaluated in randomized trials. Some observational studies suggest metformin may limit AAA growth, and this is currently being tested in randomized trials. In conclusion, no drug therapy has been shown to convincingly limit AAA growth in randomized controlled trials. Further large prospective studies on other targets are needed.

Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerized Tomography Images Acquired During the Aneurysm Surveillance Period

OBJECTIVE

We investigated the utility of geometric features for future AAA growth prediction.

BACKGROUND

Novel methods for growth prediction of AAA are recognized as a research priority. Geometric feature have been used to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth.

METHODS

Computerized tomography (CT) scans from patients with infra-renal AAAs were analyzed. Aortic volumes were segmented using an automated pipeline to extract AAA diameter (APD), undulation index (UI), and radius of curvature (RC). Using a prospectively recruited cohort, we first examined the relation between these geometric measurements to patients' demographic features (n = 102). A separate 192 AAA patients with serial CT scans during AAA surveillance were identified from an ongoing clinical database. Multinomial logistic and multiple linear regression models were trained and optimized to predict future AAA growth in these patients.

RESULTS

There was no correlation between the geometric measurements and patients' demographic features. APD (Spearman r = 0.25, P < 0.05), UI (Spearman r = 0.38, P < 0.001) and RC (Spearman r =-0.53, P < 0.001) significantly correlated with annual AAA growth. Using APD, UI, and RC as 3 input variables, the area under receiver operating characteristics curve for predicting slow growth (<2.5 mm/yr) or fast growth (>5 mm/yr) at 12 months are 0.80 and 0.79, respectively. The prediction or growth rate is within 2 mm error in 87% of cases.

CONCLUSIONS

Geometric features of an AAA can predict its future growth. This method can be applied to routine clinical CT scans acquired from patients during their AAA surveillance pathway.

Deep Learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction

Prediction of abdominal aortic aneurysm (AAA) growth is of essential importance for the early treatment and surgical intervention of AAA. Capturing key features of vascular growth, such as blood flow and intraluminal thrombus (ILT) accumulation play a crucial role in uncovering the intricated mechanism of vascular adaptation, which can ultimately enhance AAA growth prediction capabilities. However, local correlations between hemodynamic metrics, biological and morphological characteristics, and AAA growth rates present high inter-patient variability that results in that the temporal-spatial biochemical and mechanical processes are still not fully understood. Hence, this study aims to integrate the physics-based knowledge with deep learning with a patch-based convolutional neural network (CNN) approach by incorporating important multiphysical features relating to its pathogenesis for validating its impact on AAA growth prediction. For this task, we observe that the unstructured multiphysical features cannot be directly employed in the kernel-based CNN. To tackle this issue, we propose a parameterization of features to leverage the spatio-temporal relations between multiphysical features. The proposed architecture was tested on different combinations of four features including radius, intraluminal thrombus thickness, time-average wall shear stress, and growth rate from 54 patients with 5-fold cross-validation with two metrics, a root mean squared error (RMSE) and relative error (RE). We conduct extensive experiments on AAA patients, the results show the effect of leveraging multiphysical features and demonstrate the superiority of the presented architecture to previous state-of-the-art methods in AAA growth prediction.

Protocol for the Metformin Aneurysm Trial (MAT): a placebo-controlled randomised trial testing whether metformin reduces the risk of serious complications of abdominal aortic aneurysm

Background

Multiple observational studies have associated metformin prescription with reduced progression of abdominal aortic aneurysm (AAA). The Metformin Aneurysm Trial (MAT) will test whether metformin reduces the risk of AAA rupture-related mortality or requirement for AAA surgery (AAA events) in people with asymptomatic aneurysms.

Methods

MAT is an international, multi-centre, prospective, parallel-group, randomised, placebo-controlled trial. Participants must have an asymptomatic AAA measuring at least 35 mm in maximum diameter, no diabetes, no contraindication to metformin and no current plans for surgical repair. The double-blind period is preceded by a 6-week, single-blind, active run-in phase in which all potential participants receive metformin. Only patients tolerating metformin by taking at least 80% of allocated medication will enter the trial and be randomised to 1500 mg of metformin XR or an identical placebo. The primary outcome is the proportion of AAA events defined as rupture-related mortality or need for surgical repair. Secondary outcomes include AAA growth, major adverse cardiovascular events and health-related quality of life. In order to test if metformin reduced the risk of AAA events by at least 25%, 616 primary outcome events will be required (power 90%, alpha 0.05).

Discussion

Currently, there is no drug therapy for AAA. Past trials have found no convincing evidence of the benefit of multiple blood pressure lowering, antibiotics, a mast cell inhibitor, an anti-platelet drug and a lipid-lowering medication on AAA growth. MAT is one of a number of trials now ongoing testing metformin for AAA. MAT, unlike these other trials, is designed to test the effect of metformin on AAA events. The international collaboration needed for MAT will be challenging to achieve given the current COVID-19 pandemic. If this challenge can be overcome, MAT will represent a trial unique within the AAA field in its large size and design.

Trial registration

Australian Clinical Trials ACTRN12618001707257. Registered on 16 October 2018

The role of dipeptidyl peptidase-IV in abdominal aortic aneurysm pathogenesis: A systematic review

Abdominal aortic aneurysm (AAA) is an important vascular disease carrying significant mortality implications due to the risk of aneurysm rupture. Current management relies exclusively on surgical repair as there is no effective medical therapy. A key element of AAA pathogenesis is the chronic inflammation mediated by inflammatory cells releasing proteases, including the enzyme dipeptidyl peptidase IV (DPP-IV). This review sought to recapitulate available evidence on the involvement of DPP-IV in AAA development. Further, we assessed the experimental use of currently available DPP-IV inhibitors for AAA management in murine models. Embase, Medline, PubMed, and Web of Science databases were utilised to access the relevant studies. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). A narrative synthesis approach was used. Sixty-four studies were identified from the searched databases; a final 11 were included in the analysis. DPP-IV was reported to be significantly increased in both AAA tissue and plasma of patients and correlated with AAA growth. DPP-IV inhibitors (sitagliptin, vildagliptin, alogliptin, and teneligliptin) were all shown to attenuate AAA formation in murine models by reducing monocyte differentiation, the release of reactive oxygen species (ROS), and metalloproteinases (MMP-2 and MMP-9). DPP-IV seems to play a role in AAA pathogenesis by propagating the inflammatory microenvironment. This is supported by observations of decreased AAA formation and reduction in macrophage infiltration, ROS, matrix MMPs, and interleukins following the use of DPP-IV inhibitors in murine models. There is an existing translational gap from preclinical observations to clinical trials in this important and novel mechanism of AAA pathogenesis. This prior literature highlights the need for further research on molecular targets involved in AAA formation.

Machine learning approaches to surrogate multifidelity Growth and Remodeling models for efficient abdominal aortic aneurysmal applications

Computational Growth and Remodeling (G&R) models have been widely used to capture the pathological development of arterial diseases and have shown promise for aiding clinical diagnosis, prognosis prediction, and staging classification. However, due to the high complexity of the arterial adaptation mechanism, high-fidelity arterial G&R simulation usually takes hours or even days, which hinders its application in clinical practice. To remedy this problem, we develop a computationally efficient arterial G&R simulation framework that comprehensively combines the physics-based G&R simulations and data-driven machine learning approaches. The proposed framework greatly enhances the computational efficiency of arterial G&R simulations, thereby enabling more time-consuming arterial applications, including personalized parameter estimation and arterial disease progression prediction. In particular, we achieve significant computational cost reduction mainly through two methods: (1) constructing a Multifidelity Surrogate (MFS) to approximate multifidelity G&R simulations by using a cokriging approach and (2) developing a novel iterative optimization algorithm for personalized parameter estimation. The proposed framework is demonstrated by estimating G&R model parameters and predicting individual aneurysm growth using follow-up CT images of Abdominal Aortic Aneurysms (AAAs) from 21 patients. Results show that the personalized parameters are satisfactorily estimated and the growth of AAAs is predicted within the clinically relevant time frame, i.e., less than 2 h, without a loss of accuracy.

Integrated Plasma and Tissue Proteomics Reveals Attractin Release by Intraluminal Thrombus of Abdominal Aortic Aneurysms and Improves Aneurysm Growth Prediction in Humans

OBJECTIVE

Discovery of novel biomarkers for abdominal aortic aneurysm growth (AAA) prediction.

BACKGROUND

Novel biomarker of AAA growth is a recognised priority in research. Our prior work implicated intraluminal thrombus (ILT) in AAAs to be a potential source of systemic mediators during AAA progression. Here we applied a mass spectrometry proteomics pipeline to discover novel biomarkers for AAA growth prediction.

METHODS

Patients were prospectively recruited. Plasma samples were collected at baseline (n = 62). AAA growth was recorded at 12 months. In Experiment 1, plasma samples from the fastest and slowest growth patients (n = 10 each) were compared. In Experiment 2, plasma samples were collected before and at 10-12 weeks after surgery (n = 29). In Experiment 3, paired ILT and omental biopsies were collected intra-operatively during open surgical repair (n = 3). In Experiment 4, tissue secretome was obtained from ex-vivo culture of these paired tissue samples. Samples were subjected to a liquid chromatography tandem mass spectrometry (LC-MS/MS) workflow to discover novel biomarkers.

RESULTS

We discovered 3 proteins that are: (i) present in ILT; (ii) released by ILT; (iii) reduced in circulation after AAA surgery; (iv) differs between fast and slow growth AAAs. One of these is Attractin. Plasma Attractin correlates significantly with future AAA growth (Spearman r = 0.35, P < 0.005). Using Attractin and AAA diameter as input variables, the AUROC for predicting no growth and fast growth of AAA at 12 months is 85% and 76%, respectively.

CONCLUSION

We show that ILT of AAAs releases mediators during the natural history of AAA growth. These are novel biomarkers for AAA growth prediction in humans.

Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference

OBJECTIVE

For small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically important for planning treatment. This study aims to build enhanced Bayesian inference methods to predict maximum aneurysm diameter.

METHODS

106 CT scans from 25 Korean AAA patients were retrospectively obtained. A two-step approach based on Bayesian calibration was used, and an exponential abdominal aortic aneurysm growth model (population-based) was specified according to each individual patient's growth (patient-specific) and morphologic characteristics of the aneurysm sac (enhanced). The distribution estimates were obtained using a Markov Chain Monte Carlo (MCMC) sampler.

RESULTS

The follow-up diameters were predicted satisfactorily (i.e. the true follow-up diameter was in the 95% prediction interval) for 79% of the scans using the population-based growth model, and 83% of the scans using the patient-specific growth model. Among the evaluated geometric measurements, centerline tortuosity was a significant (p = 0.0002) predictor of growth for AAAs with accelerated and stable expansion rates. Using the enhanced prediction model, 86% of follow-up scans were predicted satisfactorily. The average prediction errors of population-based, patient-specific, and enhanced models were ±2.67, ±2.61 and ± 2.79 mm, respectively.

CONCLUSION

A computational framework using patient-oriented growth models provides useful tools for per-patient basis treatment and enables better prediction of AAA growth.

Pharmacologic Management of Aneurysms

Current management of aortic aneurysms relies exclusively on prophylactic operative repair of larger aneurysms. Great potential exists for successful medical therapy that halts or reduces aneurysm progression and hence alleviates or postpones the need for surgical repair. Preclinical studies in the context of abdominal aortic aneurysm identified hundreds of candidate strategies for stabilization, and data from preoperative clinical intervention studies show that interventions in the pathways of the activated inflammatory and proteolytic cascades in enlarging abdominal aortic aneurysm are feasible. Similarly, the concept of pharmaceutical aorta stabilization in Marfan syndrome is supported by a wealth of promising studies in the murine models of Marfan syndrome-related aortapathy. Although some clinical studies report successful medical stabilization of growing aortic aneurysms and aortic root stabilization in Marfan syndrome, these claims are not consistently confirmed in larger and controlled studies. Consequently, no medical therapy can be recommended for the stabilization of aortic aneurysms. The discrepancy between preclinical successes and clinical trial failures implies shortcomings in the available models of aneurysm disease and perhaps incomplete understanding of the pathological processes involved in later stages of aortic aneurysm progression. Preclinical models more reflective of human pathophysiology, identification of biomarkers to predict severity of disease progression, and improved design of clinical trials may more rapidly advance the opportunities in this important field.

Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans

Objective

Accurate prediction of abdominal aortic aneurysm (AAA) growth in an individual can allow personalised stratification of surveillance intervals and better inform the timing for surgery. The authors recently described the novel significant association between flow mediated dilatation (FMD) and future AAA growth. The feasibility of predicting future AAA growth was explored in individual patients using a set of benchmark machine learning techniques.

Methods

The Oxford Abdominal Aortic Aneurysm Study (OxAAA) prospectively recruited AAA patients undergoing the routine NHS management pathway. In addition to the AAA diameter, FMD was systemically measured in these patients. A benchmark machine learning technique (non-linear Kernel support vector regression) was applied to predict future AAA growth in individual patients, using their baseline FMD and AAA diameter as input variables.

Results

Prospective growth data were recorded at 12 months (360 ± 49 days) in 94 patients. Of these, growth data were further recorded at 24 months (718 ± 81 days) in 79 patients. The average growth in AAA diameter was 3.4% at 12 months, and 2.8% per year at 24 months. The algorithm predicted the individual's AAA diameter to within 2 mm error in 85% and 71% of patients at 12 and 24 months.

Conclusions

The data highlight the utility of FMD as a biomarker for AAA and the value of machine learning techniques for AAA research in the new era of precision medicine.

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Flow mediated dilatation of brachial artery is a biomarker of AAA progression.

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It is feasible to predict future AAA growth in individuals using machine learning techniques.

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Endothelial dysfunction is a key feature in human AAA disease.

Data for the Oxford Abdominal Aortic Aneurysm Study international survey of vascular surgery professionals

As part of the Oxford Abdominal Aortic Aneurysm (OxAAA) Study, we conducted an international survey of vascular surgery professionals. One aspect of the survey is as published in the International Journal of Cardiology: “International Opinion on Priorities in Research for Small Abdominal Aortic Aneurysms and the Potential Path for Research to Impact Clinical Management”.

This Data-in-Brief article contains a detailed method for the conduct of this survey and additional original data. In this survey, we also provided vascular surgery colleagues with contemporary epidemiologic and surgical outcome data. This was followed by a hypothetical scenario whereby a patient had just been diagnosed with a small (40 mm) AAA and a novel biomarker predicted it to be fast growing in the coming years. We assessed the vascular professionals' perception of the patient's preference for management in this scenario, and their willingness to refer patients for a surgical trial that investigates the outcome of early versus late surgery in this setting. The survey then asked the vascular professionals to assume the role of the patient, and provided their own preferences in such a scenario.