Endovascular Treatment of Ruptured Abdominal Aortic Aneurysms: Is Now EVAR the First Choice of Treatment?

Identifying abdominal aortic aneurysm size and presence using Natural Language Processing of radiology reports: a systematic review and meta-analysis

BACKGROUND AND AIM

Prior investigations of the natural history of abdominal aortic aneurysms (AAAs) have been constrained by small sample sizes or uneven assessments of aggregated data. Natural language processing (NLP) can significantly enhance the investigation and treatment of patients with AAAs by swiftly and effectively collecting imaging data from health records. This meta-analysis aimed to evaluate the efficacy of NLP techniques in reliably identifying the existence or absence of AAAs and measuring the maximal abdominal aortic diameter in extensive datasets of radiology study reports.

METHOD

The PubMed, Scopus, Web of Science, Embase, and Science Direct databases were searched until March 2024 to obtain pertinent papers. The RAYYAN intelligent tool for systematic reviews was utilized to screen the studies. The meta-analysis was conducted using STATA v18 software. Egger's test was employed to evaluate publication bias. The Newcastle Ottawa Scale was employed to assess the quality of the listed studies. A plot digitizer was employed to extract digital data.

RESULT

A total of 39,094 individuals with AAA were included in this analysis. Twenty-seven thousand three hundred twenty-six patients were male, and 11,383 were female. The mean age of the total participants was 73.1 ± 1.25 years. Analysis results for pooled estimation of performance variables such as: The sensitivity, specificity, precision, and accuracy of the implemented NLP model were analyzed as follows: 0.89(0.88-0.91), 0.88 (0.87-0.89), 0.92 (0.89-0.95), and 0.91 (0.89-0.93) respectively. The aneurysm diameter size difference reported in follow-up before and after NLP implementation in the included studies showed a 0.05 cm reduction in size, which was statistically significant.

CONCLUSION

NLP holds great potential for automating the detection of AAA size and presence in radiology reports, enhancing efficiency and scalability over manual review. However, challenges persist. Variability in report formats, terminology, and unstructured data can compromise accuracy. Additionally, NLP models rely on high-quality, annotated training datasets, which may be incomplete or unrepresentative. While NLP aids in identifying AAA-related data, human oversight is essential to ensure decisions are informed by the patient's broader clinical context. Ongoing algorithm refinement and seamless integration into clinical workflows are key to improving NLP's utility and reliability in this field.

Automatic segmentation of intraluminal thrombosis of abdominal aortic aneurysms from CT angiography using a mixed-scale-driven multiview perception network (M2Net) model

Intraluminal thrombosis (ILT) plays a critical role in the progression of abdominal aortic aneurysms (AAA). Understanding the role of ILT can improve the evaluation and management of AAAs. However, compared with highly developed automatic vessel lumen segmentation methods, ILT segmentation is challenging. Angiographic contrast agents can enhance the vessel lumen but cannot improve boundary delineation of the ILT regions; the lack of intrinsic contrast in the ILT structure significantly limits the accurate segmentation of ILT. Additionally, ILT is not evenly distributed within AAAs; its sparsity and scattered distributions in the imaging data pose challenges to the learning process of neural networks. Thus, we propose a multiview fusion approach, allowing us to obtain high-quality ILT delineation from computed tomography angiography (CTA) data. Our multiview fusion network is named Mixed-scale-driven Multiview Perception Network (M2Net), and it consists of two major steps. Following image preprocessing, the 2D mixed-scale ZoomNet segments ILT from each orthogonal view (i.e., Axial, Sagittal, and Coronal views) to enhance the prior information. Then, the proposed context-aware volume integration network (CVIN) effectively fuses the multiview results. Using contrast-enhanced computed tomography angiography (CTA) data from human subjects with AAAs, we evaluated the proposed M2Net. A quantitative analysis shows that the proposed deep-learning M2Net model achieved superior performance (e.g., DICE scores of 0.88 with a sensitivity of 0.92, respectively) compared with other state-of-the-art deep-learning models. In closing, the proposed M2Net model can provide high-quality delineation of ILT in an automated fashion and has the potential to be translated into the clinical workflow.

Abdominal Compartment Syndrome-When Is Surgical Decompression Needed?

Compartment syndrome occurs when increased pressure inside a closed anatomical space compromises tissue perfusion. The sudden increase in pressure inside these spaces requires rapid decompression by means of surgical intervention. In the case of abdominal compartment syndrome (ACS), surgical decompression consists of a laparostomy. The aim of this review is to identify the landmarks and indications for the appropriate moment to perform decompression laparotomy in patients with ACS based on available published data. A targeted literature review was conducted on indications for decompression laparotomy in ACS. The search was focused on three conditions characterized by a high ACS prevalence, namely acute pancreatitis, ruptured abdominal aortic aneurysm and severe burns. There is still a debate around the clinical characteristics which require surgical intervention in ACS. According to the limited data published from observational studies, laparotomy is usually performed when intra-abdominal pressure reaches values ranging from 25 to 36 mmHg on average in the case of acute pancreatitis. In cases of a ruptured abdominal aortic aneurysm, there is a higher urgency to perform decompression laparotomy for ACS due to the possibility of continuous hemorrhage. The most conflicting recommendations on whether surgical treatment should be delayed in favor of other non-surgical interventions come from studies involving patients with severe burns. The results of the review must be interpreted in the context of the limited available robust data from observational studies and clinical trials.

Abdominal compartment syndrome after r-EVAR: a systematic review with meta-analysis on incidence and mortality

INTRODUCTION

Endovascular aneurysm repair for ruptured abdominal aortic aneurysms (r-EVAR) sometimes complicates with abdominal compartment syndrome (ACS) due to extensive retroperitoneal hematoma, with significant prognostic implications. This systematic review aimed to analyze the incidence of the syndrome and assess the impact of ACS on mortality. Mortality after decompressive laparotomy was also assessed.

EVIDENCE ACQUISITION

Two databases were searched: Medline and Web of Science. The search was conducted through October 2019. The titles and abstracts of the retrieved articles were independently reviewed. All studies reporting on the ACS incidence after r-EVAR were initially included. From each study, eligibility was determined and descriptive, methodological, and outcome data was extracted. The incidence was calculated with summary proportion. Odds ratio was used to compare the mortality rate. Meta-analysis was performed with fixed effect model when calculating the ACS incidence in r-EVAR patients and when assessing the impacts of ACS and DL in the mortality rate.

EVIDENCE SYNTHESIS

A total of 46 studies were included, with a cumulative cohort of 3064 patients. Two hundred and fifty-two (8.2%) patients developed ACS. The ACS pooled incidence was 9% with a 95% confidence interval of [0.08; 0.11]. Among the 46 included studies, 19 studies reported data on the mortality rate, corresponding to 1825 of the 3064 patients. Of these, 169 (9.3%) had developed ACS and 94 (55.6%) of them died by multi organ failure. Among the 1656 patients without ACS, 328 died (19.8%). The mortality odds ratio meta-analysis was 6.25 with a 95% confidence interval of [4.44, 8.80]. Decompressive laparotomy was performed in 41 patients, decreasing mortality in 47%.

CONCLUSIONS

ACS affects approximately 9% of patients submitted to r-EVAR, and significantly increases perioperative mortality. Close postoperative surveillance to clinical signs of ACS is vital in these patients.

Systematic review and meta-analysis of the risk of bowel ischemia after ruptured abdominal aortic aneurysm repair

OBJECTIVE

Outcomes after repair of ruptured abdominal aortic aneurysm (RAAA) have improved in the last decade. It is unknown whether this has resulted in a reduction of postoperative bowel ischemia (BI). The primary objective was to determine BI prevalence after RAAA repair. Secondary objectives were to determine its major sequelae and differences between open repair (OR) and endovascular aneurysm repair (EVAR).

METHODS

This systematic review (PROSPERO CRD42017055920) followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines. MEDLINE and Embase were searched for studies published from 2005 until 2018. The methodologic quality of observational studies was assessed with the Methodological Index for Non-Randomized Studies (MINORS) tool. The quality of the randomized controlled trials (RCTs) was assessed with the Cochrane Collaboration's tool for assessing risk of bias. BI prevalence and rates of BI as cause of death, reoperation, and bowel resection were estimated with meta-analyses with a random-effects model. Differences between OR and EVAR were estimated with pooled risk ratios with 95% confidence intervals (CIs). Changes over time were assessed with Spearman rank test (ρ). Publication bias was assessed with a funnel plot analysis.

RESULTS

A total of 101 studies with 52,670 patients were included; 72 studies were retrospective cohort studies, 14 studies were prospective cohort studies, 12 studies were retrospective administrative database studies, and 3 studies were RCTs. The overall methodologic quality of the RCTs was high, but that of observational studies was low. The pooled prevalence of BI ranged from of 0.08 (95% CI, 0.07-0.09) in database studies to 0.10 (95% CI, 0.08-0.12) in cohort studies. The risk of BI was higher after OR than after EVAR (risk ratio, 1.79; 95% CI, 1.25-2.57). The pooled rate of BI as cause of death was 0.04 (95% CI, 0.03-0.05), and that of BI as cause of reoperation and bowel resection ranged between 0.05 and 0.07. BI prevalence did not change over time (ρ, -0.01; P = .93). The funnel plot analysis was highly suggestive of publication bias.

CONCLUSIONS

The prevalence of clinically relevant BI after RAAA repair is approximately 10%. Approximately 5% of patients undergoing RAAA repair suffer from severe consequences of BI. BI is less prevalent after EVAR than after OR.

Isolated iliac artery aneurysms: a single-centre experience

PURPOSE

This paper reviews our experience in endovascular treatment of isolated iliac artery aneurysms (IIAAs) with a large number of patients.

MATERIALS AND METHODS

From May 2005 to September 2013, 45 patients (43 men and two women; mean age, 74 ± 10 years) with a total of 59 IIAAs underwent endovascular treatment at our institute. We evaluated technical success, long-term patency, early and late complications and overall mortality. Patients were divided into two groups: emergency-treatment group and elective-treatment group.

RESULTS

At a median follow-up of 34.3 months, we achieved a technical success of 97.8 %, a primary patency of 95.5 % and a secondary patency of 100 %, with complete exclusion of the aneurysm in 84.5 % of cases. The incidence of endoleaks was of 15.5 %: eight were type II and one was type III; perioperative mortality was 4.7 %.

CONCLUSIONS

Our study documents the effectiveness, in both emergency and elective settings, of the endovascular treatment of iliac aneurysms (EVIAR), which has become the first-choice treatment at our institute. In particular cases, it is also possible to avoid embolisation of the internal iliac artery.