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The aim of this nationwide study was to analyze the impact of diabetes on post-operative mortality in patients undergoing AAA repair in France. This 10-year retrospective, multicenter study based on the French National electronic health data included patients undergoing AAA repair between 2010 and 2019. In-hospital post-operative mortality was analyzed using Kaplan–Meier curve survival and Log-Rank tests. A multivariate regression analysis was performed to calculate Hazard Ratios. Over 79,935 patients who underwent AAA repair, 61,146 patients (76.5%) had at least one hospital-readmission after the AAA repair, for a mean follow-up of 3.5 ± 2.5 years. Total in-hospital mortality over the 10-year study was 16,986 (21.3%) and 4581 deaths (5.8%) occurred during the first hospital stay for AAA repair. Age over 64 years old, the presence of AAA rupture and hospital readmission at 30-day were predictors of post-operative mortality (AdjHR = 1.59 CI 95% 1.51–1.67; AdjHR = 1.49 CI 95% 1.36–1.62 and AdjHR = 1.92, CI 95% 1.84–2.00). The prevalence of diabetes was significantly lower in ruptured AAA compared to unruptured AAA (14.8% vs 20.9%, P < 0.001 for type 2 diabetes and 2.5% vs 4.0%, P < 0.001 for type 1 diabetes). Type 1 diabetes was significantly associated with post-operative mortality (AdjHR = 1.30 CI 95% 1.20–1.40). For type 2 diabetes, the association was not statistically significant (Adj HR = 0.96, CI 95% 0.92–1.01). Older age, AAA rupture and hospital readmission were associated with deaths that occurred after discharge from the first AAA repair. Type 1 diabetes was identified as a risk factor of post-operative mortality. This study highlights the complex association between diabetes and AAA and should encourage institutions to report long-term follow-up after AAA repair to better understand its impact.

The triggering receptor expressed on myeloid cells 1 (TREM-1) drives inflammatory responses in several cardiovascular diseases but its role in abdominal aortic aneurysm (AAA) remains unknown. Our objective was to explore the role of TREM-1 in a mouse model of angiotensin II-induced (AngII-induced) AAA. TREM-1 expression was detected in mouse aortic aneurysm and colocalized with macrophages. Trem1 gene deletion (Apoe-/-Trem1-/-), as well as TREM-1 pharmacological blockade with LR-12 peptide, limited both AAA development and severity. Trem1 gene deletion attenuated the inflammatory response in the aorta, with a reduction of Il1b, Tnfa, Mmp2, and Mmp9 mRNA expression, and led to a decreased macrophage content due to a reduction of Ly6Chi classical monocyte trafficking. Conversely, antibody-mediated TREM-1 stimulation exacerbated Ly6Chi monocyte aorta infiltration after AngII infusion through CD62L upregulation and promoted proinflammatory signature in the aorta, resulting in worsening AAA severity. AngII infusion stimulated TREM-1 expression and activation on Ly6Chi monocytes through AngII receptor type I (AT1R). In human AAA, TREM-1 was detected and TREM1 mRNA expression correlated with SELL mRNA expression. Finally, circulating levels of sTREM-1 were increased in patients with AAA when compared with patients without AAA. In conclusion, TREM-1 is involved in AAA pathophysiology and may represent a promising therapeutic target in humans.

Liver vessel segmentation from routinely performed medical imaging is a useful tool for diagnosis, treatment planning and delivery, and prognosis evaluation for many diseases, particularly liver cancer. A precise representation of liver anatomy is crucial to define the extent of the disease and, when suitable, the consequent resective or ablative procedure, in order to guarantee a radical treatment without sacrificing an excessive volume of healthy liver. Once mainly performed manually, with notable cost in terms of time and human energies, vessel segmentation is currently realized through the application of artificial intelligence (AI), which has gained increased interest and development of the field. Many different AI-driven models adopted for this aim have been described and can be grouped into different categories: thresholding methods, edge- and region-based methods, model-based methods, and machine learning models. The latter includes neural network and deep learning models that now represent the principal algorithms exploited for vessel segmentation. The present narrative review describes how liver vessel segmentation can be realized through AI models, with a summary of model results in terms of accuracy, and an overview on the future progress of this topic.

Acute mesenteric ischemia is associated with high rates of mortality. The aim of this study was to investigate the prognostic value of the neutrophil to lymphocyte ratio (NLR) and the platelet to lymphocyte ratio (PLR) on 30-day outcomes in patients with acute mesenteric ischemia. Consecutive patients who were admitted for an acute mesenteric ischemia were retrospectively included. The full white blood count at the time of admission to the hospital was recorded. The population was divided into 4 subgroups according to the quartiles of the NLR and the PLR. The 30-day outcomes including the mortality and the complications were compared among the subgroups. In total, 106 patients were included. A surgical treatment including revascularization and/or digestive resection was performed for 56 patients (52.8%). The 30-day all-cause mortality was 72 patients (67.9%). Patients with higher PLR value (PLR >429.3) had significantly higher rate of mortality compared to the other groups (80.8% vs 46.2%, 66.7% and 77.8%, p = 0.03). No significant difference on 30-day outcome was observed among the subgroups divided according to the NLR. The PLR, but not the NLR, is a predictive factor of 30-day mortality in patients with acute mesenteric ischemia.

IntroductionEndovascular aortic aneurysm repair (EVAR) requires long-term surveillance to detect and treat postoperative complications. However, prediction models to optimise follow-up strategies are still lacking. The primary objective of this study is to develop predictive models of post-operative outcomes following elective EVAR using Artificial Intelligence (AI)-driven analysis. The secondary objective is to investigate morphological aortic changes following EVAR.Methods and analysisThis international, multicentre, observational study will retrospectively include 500 patients who underwent elective EVAR. Primary outcomes are EVAR postoperative complications including deaths, re-interventions, endoleaks, limb occlusion and stent-graft migration occurring within 1 year and at mid-term follow-up (1 to 3 years). Secondary outcomes are aortic anatomical changes. Morphological changes following EVAR will be analysed and compared based on preoperative and postoperative CT angiography (CTA) images (within 1 to 12 months, and at the last follow-up) using the AI-based software PRAEVAorta 2 (Nurea). Deep learning algorithms will be applied to stratify the risk of postoperative outcomes into low or high-risk categories. The training and testing dataset will be respectively composed of 70% and 30% of the cohort.Ethics and disseminationThe study protocol is designed to ensure that the sponsor and the investigators comply with the principles of the Declaration of Helsinki and the ICH E6 good clinical practice guideline. The study has been approved by the ethics committee of the University Hospital of Patras (Patras, Greece) under the number 492/05.12.2024. The results of the study will be presented at relevant national and international conferences and submitted for publication to peer-review journals.

Artificial intelligence is gaining increasing interest in medical image segmentation, including liver cancer. However, the literature lacks model implementation in the setting of colorectal liver metastases for treatment planning. We collected the portal phase abdominal CT scan images from the Nice University Hospital hepatobiliary oncologic multidisciplinary discussion of 80 patients with colorectal liver metastases, before treatment. Data from 70 patients was exploited to train and test the nnU-Net model to automatically perform parenchyma, portal vein, hepatic veins, cava vein, and colorectal liver metastases segmentation. Data from the remaining 10 patients was used for external validation. The Dice score for parenchyma segmentation was 0,964 and 0,955 in the test and validation dataset, respectively. For portal vein segmentation, a centerline Dice (clDice) of 0,758 and 0,736 was highlighted, while for hepatic veins it resulted to be 0,758 and 0,577. Cava vein segmentation showed a clDice of 0,805 and 0,734. Concerning colorectal liver metastases, the Dice score was 0,693 and 0,61. The nnU-Net showed promising segmentation results, especially for liver parenchyma. Its task could be useful to help physicians decide which is the best treatment strategy based on individual anatomical characteristics and disease extension. Training the model on a larger dataset with the same characteristics could help improve segmentation performances.

Type 2 diabetes patients are less likely to develop an abdominal aortic aneurysm (AAA). Since macrophages play a crucial role in AAA development, we hypothesized that this decrease in AAA risk in diabetic patients might be due to diabetes-induced changes in macrophage biology. To test this hypothesis, we treated primary macrophages obtained from healthy human volunteers with serum from non-diabetic vs. diabetic AAA patients and observed differences in extracellular acidification and the expression of genes involved in glycolysis and lipid oxidation. These results suggest an increase in metabolism in macrophages treated with serum from diabetic AAA patients. Since serum samples used did not differ in glucose content, these changes are not likely to be caused by differences in glycemia. Macrophage functions have been shown to be linked to their metabolism. In line with this, our data suggest that this increase in macrophage metabolism is accompanied by a shift towards an anti-inflammatory state. Together, these results support a model where diabetes-induced changes in metabolism in macrophages might lead to a reduced risk for AAA development.

Key Points Most of the macrophages that accumulate in the aneurysmal aortic wall originate from circulating monocytes; however, some macrophages in abdominal aortic aneurysms (AAAs) might originate from aortic tissue-resident macrophages The main factors involved in macrophage accumulation in the AAA wall include chemokines and cytokines produced in response to tissue injury, products of extracellular matrix degradation, and microenvironmental conditions Monocytes and macrophages have distinct phenotypes during the development and progression of AAA, with major implications for monocyte and macrophage activation and biological functions in AAA Macrophages have both pathogenic and reparative roles in AAA through their involvement in extracellular matrix remodelling, in promotion and resolution of inflammation, and in various aspects of the tissue-healing response State-of-the-art translational applications are available that can be improved and harnessed for the use of monocytes and macrophages as diagnostic and prognostic biomarkers, and as therapeutic targets in AAA Inflammatory processes have a crucial role in abdominal aortic aneurysm and aortic wall remodelling. This Review focuses on the involvement of monocytes and macrophages, summarizing current knowledge on their origin and the roles of distinct monocyte and macrophage subsets in AAA development and complications, and highlighting potential translational applications targeting monocytes and macrophages. Abdominal aortic aneurysm (AAA) is a life-threatening disease associated with high morbidity, and high mortality in the event of aortic rupture. Major advances in open surgical and endovascular repair of AAA have been achieved during the past 2 decades. However, drug-based therapies are still lacking, highlighting a real need for better understanding of the molecular and cellular mechanisms involved in AAA formation and progression. The main pathological features of AAA include extracellular matrix remodelling associated with degeneration and loss of vascular smooth muscle cells and accumulation and activation of inflammatory cells. The inflammatory process has a crucial role in AAA and substantially influences many determinants of aortic wall remodelling. In this Review, we focus specifically on the involvement of monocytes and macrophages, summarizing current knowledge on the roles, origin, and functions of these cells in AAA development and its complications. Furthermore, we show and propose that distinct monocyte and macrophage subsets have critical and differential roles in initiation, progression, and healing of the aneurysmal process. On the basis of experimental and clinical studies, we review potential translational applications to detect, assess, and image macrophage subsets in AAA, and discuss the relevance of these applications for clinical practice.

Imaging software have become critical tools in the diagnosis and the treatment of abdominal aortic aneurysms (AAA). The aim of this study was to develop a fully automated software system to enable a fast and robust detection of the vascular system and the AAA. The software was designed from a dataset of injected CT-scans images obtained from 40 patients with AAA. Pre-processing steps were performed to reduce the noise of the images using image filters. The border propagation based method was used to localize the aortic lumen. An online error detection was implemented to correct errors due to the propagation in anatomic structures with similar pixel value located close to the aorta. A morphological snake was used to segment 2D or 3D regions. The software allowed an automatic detection of the aortic lumen and the AAA characteristics including the presence of thrombus and calcifications. 2D and 3D reconstructions visualization were available to ease evaluation of both algorithm precision and AAA properties. By enabling a fast and automated detailed analysis of the anatomic characteristics of the AAA, this software could be useful in clinical practice and research and be applied in a large dataset of patients.

Background: Computed tomography angiography (CTA) is one of the most commonly used imaging technique for the management of vascular diseases. Here, we aimed to develop a hybrid method combining a feature-based expert system with a supervised deep learning (DL) algorithm to enable a fully automatic segmentation of the abdominal vascular tree. Methods: We proposed an algorithm based on the hybridization of a data-driven convolutional neural network and a knowledge-based model dedicated to vascular system segmentation. By using two distinct datasets of CTA from patients to evaluate independence to training dataset, the accuracy of the hybrid method for lumen and thrombus segmentation was evaluated compared to the feature-based expert system alone and to the ground truth provided by a human expert. Results: The hybrid approach demonstrated a better accuracy for lumen segmentation compared to the expert system alone (volume similarity: 0.8128 vs. 0.7912, p = 0.0006 and Dice similarity coefficient: 0.8266 vs. 0.7942, p < 0.0001). The accuracy for thrombus segmentation was also enhanced using the hybrid approach (volume similarity: 0.9404 vs. 0.9185, p = 0.0027 and Dice similarity coefficient: 0.8918 vs. 0.8654, p < 0.0001). Conclusions: By enabling a robust and fully automatic segmentation, the method could be used to develop real-time decision support to help in the management of vascular diseases.