Mycotic aortic and left iliac ruptured aneurysm due to Escherichia Coli: a case report and literature overview.

Mycotic aneurysm is a life-threatening disease often caused by Salmonella, Staphylococci and Streptococci species. Interestingly, Escherichia Coli (E. Coli) is described as a rare causative agent. We report the case of a patient who developed a mycotic aortic and ruptured left iliac aneurysm due to E. Coli. The patient developed a secondary aortic graft infection due to a mesenteric ischemia with fecal peritonitis. A literature overview of the current knowledge on mycotic aortic aneurysms specifically due to E. Coli is discussed including the clinical characteristics of patients, the management of the disease and the post-operative outcomes.

Artificial intelligence in abdominal aortic aneurysm.

OBJECTIVE: Abdominal aortic aneurysm (AAA) is a life-threatening disease, and the only curative treatment relies on open or endovascular repair. The decision to treat relies on the evaluation of the risk of AAA growth and rupture, which can be difficult to assess in practice. Artificial intelligence (AI) has revealed new insights into the management of cardiovascular diseases, but its application in AAA has so far been poorly described. The aim of this review was to summarize the current knowledge on the potential applications of AI in patients with AAA. METHODS: A comprehensive literature review was performed. The MEDLINE database was searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search strategy used a combination of keywords and included studies using AI in patients with AAA published between May 2019 and January 2000. Two authors independently screened titles and abstracts and performed data extraction. The search of published literature identified 34 studies with distinct methodologies, aims, and study designs. RESULTS: AI was used in patients with AAA to improve image segmentation and for quantitative analysis and characterization of AAA morphology, geometry, and fluid dynamics. AI allowed computation of large data sets to identify patterns that may be predictive of AAA growth and rupture. Several predictive and prognostic programs were also developed to assess patients' postoperative outcomes, including mortality and complications after endovascular aneurysm repair. CONCLUSIONS: AI represents a useful tool in the interpretation and analysis of AAA imaging by enabling automatic quantitative measurements and morphologic characterization. It could be used to help surgeons in preoperative planning. AI-driven data management may lead to the development of computational programs for the prediction of AAA evolution and risk of rupture as well as postoperative outcomes. AI could also be used to better evaluate the indications and types of surgical treatment and to plan the postoperative follow-up. AI represents an attractive tool for decision-making and may facilitate development of personalized therapeutic approaches for patients with AAA.

Evaluation of the Impact of Sarcopenia in Patients with Acute Mesenteric Ischemia.

BACKGROUND: Sarcopenia has been identified as a prognostic factor in several diseases. The aim of this study was to investigate the impact of sarcopenia in patients with acute mesenteric ischemia. METHODS: Consecutive patients admitted for acute mesenteric ischemia were retrospectively included at the University Hospital of Nice. Sarcopenia was assessed by the measurement of total psoas area normalized for height (TPA/H) on CT-scan and was defined as a TPA/H inferior to the lowest sex-specific quartile. The management of the patients and the 30-day outcomes were compared between sarcopenic and nonsarcopenic patients. Correlations between the TPA/H and biological characteristics were investigated. RESULTS: Among the 80 patients included, the lowest quartile of TPA/H that defined sarcopenia was 406.1 mm2/m2 for men and 307 mm2/m2 for women. The rate of revascularization or the need of intestinal resection did not significantly differ between sarcopenic and nonsarcopenic patients (10.5% vs. 26.2%, P = 0.214 and 26.3% vs. 47.5%, P = 0.118 respectively). The 30-day mortality did not significantly differ between the two groups (63.2% vs. 47.5%, P = 0.297). The TPA/H was significantly negatively correlated with the neutrophil, thrombocyte, and monocyte counts (r = -0.283; -0.288, -0.225, P < 0.05) and positively correlated with the hemoglobin concentration and the glomerular filtration rate (r = 0.368; 0.261, P < 0.05). CONCLUSIONS: Further studies on longer follow-up period would be of interest to fully understand the prognostic value of sarcopenia in patients with acute mesenteric ischemia.

Large language models and artificial intelligence chatbots in vascular surgery.

Natural language processing is a subfield of artificial intelligence that aims to analyze human oral or written language. The development of large language models has brought innovative perspectives in medicine, including the potential use of chatbots and virtual assistants. Nevertheless, the benefits and pitfalls of such technology need to be carefully evaluated before their use in health care. The aim of this narrative review was to provide an overview of potential applications of large language models and artificial intelligence chatbots in the field of vascular surgery, including clinical practice, research, and education. In light of the results, we discuss current limits and future directions.

Applications of Artificial Intelligence in Non-cardiac Vascular Diseases: A Bibliographic Analysis.

Research output related to artificial intelligence (AI) in vascular diseases has been poorly investigated. The aim of this study was to evaluate scientific publications on AI in non-cardiac vascular diseases. A systematic literature search was conducted using the PubMed database and a combination of keywords and focused on three main vascular diseases (carotid, aortic and peripheral artery diseases). Original articles written in English and published between January 1995 and December 2020 were included. Data extracted included the date of publication, the journal, the identity, number, affiliated country of authors, the topics of research, and the fields of AI. Among 171 articles included, the three most productive countries were USA, China, and United Kingdom. The fields developed within AI included: machine learning (n = 90; 45.0%), vision (n = 45; 22.5%), robotics (n = 42; 21.0%), expert system (n = 15; 7.5%), and natural language processing (n = 8; 4.0%). The applications were mainly new tools for: the treatment (n = 52; 29.1%), prognosis (n = 45; 25.1%), the diagnosis and classification of vascular diseases (n = 38; 21.2%), and imaging segmentation (n = 38; 21.2%). By identifying the main techniques and applications, this study also pointed to the current limitations and may help to better foresee future applications for clinical practice.

Platelet to lymphocyte ratio as a predictive factor of 30-day mortality in patients with acute mesenteric ischemia.

INTRODUCTION: 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. MATERIAL AND METHODS: 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. RESULTS: 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. CONCLUSION: The PLR, but not the NLR, is a predictive factor of 30-day mortality in patients with acute mesenteric ischemia.