Society for Vascular Surgery implementation of guidelines in abdominal aortic aneurysms: preoperative surveillance and threshold for repair

A preliminary ex vivo diffusion tensor imaging study of distinct aortic morphologies

Changes in the microstructure of the aortic wall precede the progression of various aortic pathologies, including aneurysms and dissection. Current clinical decisions with regards to surgical planning and/or radiological intervention are guided by geometric features, such as aortic diameter, since clinical imaging lacks tissue microstructural information. The aim of this proof-of-concept work is to investigate a non-invasive imaging method, diffusion tensor imaging (DTI), in ex vivo aortic tissue to gain insights into the microstructure. This study examines healthy, aneurysm and a type B chronic dissection aortae, via DTI. DTI-derived metrics, such as the fractional anisotropy, mean diffusivity, helical angle and tractography, were examined in each morphology. The results from this work highlighted distinct differences in fractional anisotropy (healthy, 0.24 ± 0.008; aneurysmal, 0.19 ± 0.002; dissected, 0.13 ± 0.006) and a larger variation in the helical angle in the dissected aorta compared to healthy (39.28 ± 11.93° vs. 26.12 ± 4.60°, respectively). These differences were validated by histological characterisation. This study demonstrates the sensitivity of DTI to pathological changes in aortic tissue, highlighting the potential of this methodology to provide improved clinical insight.

Testing ChatGPT's Ability to Provide Patient and Physician Information on Aortic Aneurysm

INTRODUCTION

Our objective was to test the ability of ChatGPT 4.0 to provide accurate information for patients and physicians about abdominal aortic aneurysms (AAA) and to assess its alignment with Society for Vascular Surgery (SVS) clinical practice guidelines (CPG) for AAA care.

MATERIAL AND METHODS

Fifteen patient-level questions, 37 questions selected to reflect 28 SVS CPGs and 4 questions regarding AAA rupture risk were posed to ChatGPT 4.0. Single responses were recorded and graded for accuracy and quality by ten board-certified vascular surgeons as well as two fellow trainees using a 5-point Likert scale; 1 = very poor, 2 = poor, 3 = fair, 4 = good, and 5 = excellent.

RESULTS

The mean of the means (MoM) accuracy rating across all 15 patient-level questions was 4.4 (SD 0.4, quartile range (QR) 4.2-4.7). ChatGPT 4.0 demonstrated good alignment with SVS practice guidelines (MoM: 4.2, SD: 0.4, QR: 3.9-4.5). The accuracy of responses was consistent across guideline categories; screening or surveillance (4.2), indications for surgery (4.5), preoperative risk assessment (4.5), perioperative coronary revascularization (4.1), and perioperative management (4.2). The generative artificial intelligence bot demonstrated only fair performance in answering the annual AAA rupture risk (MoM: 3.4, SD: 1.2, QR: 2.3-4.3).

CONCLUSIONS

ChatGPT 4.0 provided accurate responses to a variety of patient-level questions regarding AAA. Responses were well-aligned with current SVS CPGs except for inaccuracies in the risk of AAA rupture at varying diameters. The emergence of generative artificial intelligence bots presents an opportunity for study of applications in patient education and to determine their ability to augment the vascular specialist's knowledge base.

Artificial Intelligence-Based Predictive Modeling for Aortic Aneurysms

Abdominal aortic aneurysms (AAAs) remain a major concern to the global society because of the associated risk of rupture and death. Currently, the management of AAAs entails clinical and imaging risk factors, which are not precise and accurate in terms of patient-specific risk assessment. Over the last decade, the utilization of artificial intelligence (AI) and machine learning (ML) algorithms has transformed the process of decision-making in the field of medicine by allowing for the creation of personalized models based on the patient's characteristics. This review aims to discuss the current state and future directions of AI in the form of predictive modeling for aortic aneurysms, stressing the versatility and progression of the ML approaches in risk assessment, screening, and prognosis. We expand on the various strategies used in AI-based solutions and the differences between general and specific approaches such as supervised and unsupervised learning, deep learning, and others. Furthermore, we bring forward the problem of incorporating clinical, imaging, and genomic data into AI/ML to improve its predictiveness and applicability to clinical practice. In addition, we discuss the difficulties and prospects of turning the developed AI-based forecasting models into clinical practice, as well as the problems associated with data quality, model explainability, and legal and ethical concerns. This review aims to reveal the opportunities of AI and ML in enhancing the risk assessment and management of AAAs to shift the paradigm of cardiovascular care toward precision medicine.

Size thresholds for repair of abdominal aortic aneurysms warrant reconsideration

BACKGROUND

The historical size threshold for abdominal aortic aneurysm (AAA) repair is widely accepted to be 5.5 cm for men and 5.0 cm for women. However, contemporary AAA rupture risks may be lower than historical benchmarks, which has implications for when AAAs should be repaired. Our objective was to use contemporary AAA rupture rates to inform optimal size thresholds for AAA repair.

METHODS

We used a Markov chain analysis to estimate life expectancy for patients with AAA. The primary outcome was AAA-related mortality. We estimated survival using Social Security Administration life tables and published contemporary AAA rupture estimates. For those undergoing repair, we modified survival estimates using data from the Vascular Quality Initiative and Medicare on complications, late rupture, and open conversion. We used this model to estimate the AAA repair size threshold that minimizes AAA-related mortality for 60-year-old average-health men and women. We performed a sensitivity analysis of poor-health patients and 70- and 80-year-old base cases.

RESULTS

The annual risk of all-cause mortality under surveillance for a 60-year-old woman presenting with a 5.0 cm AAA using repair thresholds of 5.5 cm, 6.0 cm, 6.5 cm, and 7.0 cm was 1.7%, 2.3%, 2.7%, and 2.8%, respectively. The corresponding risk for a man was 2.3%, 2.9%, 3.3%, and 3.4% for the same repair thresholds, respectively. For a 60-year-old average-health woman, an AAA repair size of 6.1 cm was the optimal threshold to minimize AAA-related mortality. Life expectancy varied by <2 months for repair at sizes from 5.7 cm to 7.1 cm. For a 60-year-old average-health man, an AAA repair size of 6.9 cm was the optimal threshold to minimize AAA-related mortality. Life expectancy varied by <2 months for repair at sizes from 6.0 cm to 7.4 cm. Women in poor health, at various age strata, had optimal AAA repair size thresholds that were >6.5 cm, whereas men in poor health, at all ages, had optimal repair size thresholds that were >8.0 cm.

CONCLUSIONS

The optimal threshold for AAA repair is more nuanced than a discrete size. Specifically, there appears to be a range of AAA sizes for which repair is reasonable to minmized AAA-related mortality. Notably, they all are greater than current guideline recommendations. These findings would suggest that contemporary AAA size thresholds for repair should be reconsidered.

The controlling nutritional status score predicts postoperative mortality in patients with ruptured abdominal aortic aneurysm: a retrospective study

Background

Ruptured abdominal aortic aneurysms (rAAAs) are challenging for vascular surgeons because they have a high mortality rate. In many diseases, nutritional status is closely associated with prognosis. The Controlling Nutritional Status (CONUT) screening tool score is a prognostic factor in some malignant and chronic diseases; however, the impact of nutritional status on rAAA has not yet been reported. In this study, we explored the relationship between the CONUT score and the postoperative prognosis of patients with rAAA.

Methods

This was a retrospective review of 39 patients with rAAA who underwent surgical treatment from March 2018 to September 2021 at one center. Patient characteristics, nutritional status (CONUT score), and postoperative status were recorded. The patients were divided into groups A and B based on the CONUT score. The baseline characteristics of the two groups were compared, and Cox proportional hazards and logistic regression analyses were used to determine independent predictors of mid-term mortality and complications, respectively.

Results

The overall mid-term mortality rate was 28.21% (11/39). Compared with group A, group B had higher intraoperative (P = 0.047) and mid-term mortality (P = 0.033) rates. The univariate analysis showed that age [hazard ratio (HR), 1.098; 95% confidence interval (CI), 1.019-1.182; P = 0.014], CONUT score (HR, 1.316; 95% CI, 1.027-1.686; P = 0.03), and surgical procedure (HR, 0.127; 95% CI, 0.016-0.992; P = 0.049) were associated with mid-term mortality, whereas the multivariate analysis showed that the CONUT score (HR, 1.313; 95% CI, 1.009-1.710; P = 0.043) was an independent predictor of mid-term mortality. The multivariate logistic regression analysis did not reveal any associations with complications. The Kaplan-Meier curves showed that group B had a lower mid-term survival rate (log-rank P = 0.024).

Conclusion

Malnutrition is closely associated with the prognosis of patients with rAAA, and the CONUT score can be used to predict mid-term mortality.

A systematic review summarizing local vascular characteristics of aneurysm wall to predict for progression and rupture risk of abdominal aortic aneurysms

OBJECTIVE

At present, the rupture risk prediction of abdominal aortic aneurysms (AAAs) and, hence, the clinical decision making regarding the need for surgery, is determined by the AAA diameter and growth rate. However, these measures provide limited predictive information. In the present study, we have summarized the measures of local vascular characteristics of the aneurysm wall that, independently of AAA size, could predict for AAA progression and rupture.

METHODS

We systematically searched PubMed and Web of Science up to September 13, 2021 to identify relevant studies investigating the relationship between local vascular characteristics of the aneurysm wall and AAA growth or rupture in humans. A quality assessment was performed using the ROBINS-I (risk of bias in nonrandomized studies of interventions) tool. All included studies were divided by four types of measures of arterial wall characteristics: metabolism, calcification, intraluminal thrombus, and compliance.

RESULTS

A total of 20 studies were included. Metabolism of the aneurysm wall, especially when measured by ultra-small superparamagnetic iron oxide uptake, and calcification were significantly related to AAA growth. A higher intraluminal thrombus volume and thickness had correlated positively with the AAA growth in one study but in another study had correlated negatively. AAA compliance demonstrated no correlation with AAA growth and rupture. The aneurysmal wall characteristics showed no association with AAA rupture. However, the metabolism, measured via ultra-small superparamagnetic iron oxide uptake, but none of the other measures, showed a trend toward a relationship with AAA rupture, although the difference was not statistically significant.

CONCLUSIONS

The current measures of aortic wall characteristics have the potential to predict for AAA growth, especially the measures of metabolism and calcification. Evidence regarding AAA rupture is scarce, and, although more work is needed, aortic wall metabolism could potentially be related to AAA rupture. This highlights the role of aortic wall characteristics in the progression of AAA but also has the potential to improve the prediction of AAA growth and rupture.

N1-Methyladenosine (m1A) Regulation Associated With the Pathogenesis of Abdominal Aortic Aneurysm Through YTHDF3 Modulating Macrophage Polarization

Objectives

This study aimed to identify key AAA-related m1A RNA methylation regulators and their association with immune infiltration in AAA. Furthermore, we aimed to explore the mechanism that m1A regulators modulate the functions of certain immune cells as well as the downstream target genes, participating in the progression of AAA.

Methods

Based on the gene expression profiles of the GSE47472 and GSE98278 datasets, differential expression analysis focusing on m1A regulators was performed on the combined dataset to identify differentially expressed m1A regulatory genes (DEMRGs). Additionally, CIBERSORT tool was utilized in the analysis of the immune infiltration landscape and its correlation with DEMRGs. Moreover, we validated the expression levels of DEMRGs in human AAA tissues by real-time quantitative PCR (RT-qPCR). Immunofluorescence (IF) staining was also applied in the validation of cellular localization of YTHDF3 in AAA tissues. Furthermore, we established LPS/IFN-γ induced M1 macrophages and ythdf3 knockdown macrophages in vitro, to explore the relationship between YTHDF3 and macrophage polarization. At last, RNA immunoprecipitation-sequencing (RIP-Seq) combined with PPI network analysis was used to predict the target genes of YTHDF3 in AAA progression.

Results

Eight DEMRGs were identified in our study, including YTHDC1, YTHDF1-3, RRP8, TRMT61A as up-regulated genes and FTO, ALKBH1 as down-regulated genes. The immune infiltration analysis showed these DEMRGs were positively correlated with activated mast cells, plasma cells and M1 macrophages in AAA. RT-qPCR analysis also verified the up-regulated expression levels of YTHDC1, YTHDF1, and YTHDF3 in human AAA tissues. Besides, IF staining result in AAA adventitia indicated the localization of YTHDF3 in macrophages. Moreover, our in-vitro experiments found that the knockdown of ythdf3 in M0 macrophages inhibits macrophage M1 polarization but promotes macrophage M2 polarization. Eventually, 30 key AAA-related target genes of YTHDF3 were predicted, including CD44, mTOR, ITGB1, STAT3, etc.

Conclusion

Our study reveals that m1A regulation is significantly associated with the pathogenesis of human AAA. The m1A “reader,” YTHDF3, may participate in the modulating of macrophage polarization that promotes aortic inflammation, and influence AAA progression by regulating the expression of its target genes.