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Aortic aneurysm is a life-threatening condition and mechanisms underlying its formation and progression are still incompletely understood. Omics approach has brought new insights to identify a broad spectrum of biomarkers and better understand cellular and molecular pathways involved. Omics generate a large amount of data and several studies have highlighted that artificial intelligence (AI) and techniques such as machine learning (ML)/deep learning (DL) can be of use in analyzing such complex datasets. However, only a few studies have so far reported the use of ML/DL for omics analysis in aortic aneurysms. The aim of this study is to summarize recent advances on the use of ML/DL for omics analysis to decipher aortic aneurysm pathophysiology and develop patient-tailored risk prediction models. In the light of current knowledge, we discuss current limits and highlight future directions in the field., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objective: The impact of sex on outcomes following carotid endarterectomy (CEA) and carotid artery stenting (CAS) is not fully elucidated. The aim of this study was to analyze the association between sex and outcomes of asymptomatic patients who underwent primary carotid interventions in France. Methods: This nationwide retrospective study was performed using the French National Health Insurance Information System and included asymptomatic patients who underwent primary carotid intervention over a 10-year period (1 January 2013 to 31 August 2023). Symptomatic patients and patients who had peri-operative neurologic events were excluded. The primary endpoints were the occurrence of death and stroke/transient ischaemic attack (TIA) at 30 days, 1 and 5 years after patients' discharge. Results: In total, 115,879 patients were admitted for an index CEA (29.4% women) and 6500 for CAS (29.8% women). In the CEA group, no significant sex-related difference was observed for 30-day mortality; however, women had significantly lower 1-year and 5-year mortality rates compared to men (1.9% vs. 2.6%, p < 0.001 and 7.9% vs. 11.1%, p < 0.001). In the CAS group, women had lower 30-day, 1-year and 5-year mortality (0.6% vs. 1.0%, p = 0.040, 3.8% vs. 4.9%, p = 0.048, and 10.4% vs. 15.0%, p < 0.001). A multivariate analysis showed that sex was not associated with the risk of stroke/TIA and mortality at 30 days (OR 0.84 (95% CI 0.67-1.04) and 1.27 (95% CI 0.98-1.64)). Male sex was associated with a higher risk of 1-year and 5-year mortality (OR 1.24 (95% CI 1.13-1.36) and 1.25 (95% CI 1.18-1.31)), but a lower risk of stroke/TIA than female sex. Conclusions: No significant sex-related difference was observed at 30 days in patients being discharged alive and without peri-operative neurologic events. Male sex was associated with a higher risk of mortality but a lower risk of stroke/TIA at 1 and 5 years.

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., Competing Interests: Declaration of conflicting interestThe Authors declare that there is no conflict of interest.

Three-dimensional (3D) printing has been used in medicine with applications in many different fields. 3D printing allows patient education, interventionalists training, preprocedural planning, and assists the interventionalist to improve treatment outcomes. 3D printing represents a potential advancement by allowing the printing of flexible vascular models. In this article, the authors report a clinical case using 3D printing to perform a physician-modified fenestrated endograft. An overview of 3D printing in vascular and endovascular surgery is provided, focusing on its potential applications for training, education, preprocedural planning, and current clinical applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Extended reality has brought new opportunities for medical imaging visualization and analysis. It regroups various subfields, including virtual reality, augmented reality, and mixed reality. Various applications have been proposed for surgical practice, as well as education and training. The aim of this review was to summarize current applications of extended reality and augmented reality in vascular surgery, highlighting potential benefits, pitfalls, limitations, and perspectives on improvement., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 Elsevier Inc. All rights reserved.)

e-Health technology holds great promise for improving the management of patients with vascular diseases and offers a unique opportunity to mitigate the environmental impact of vascular care, which remains an under-investigated field. The innovative potential of e-Health operates in a complex environment with finite resources. As the expansion of digital health will increase demand for devices, contributing to the environmental burden of electronics and energy use, the sustainability of e-Health technology is of crucial importance, especially in the context of increasing prevalence of cardiovascular diseases. This review discusses the environmental impact of care related to vascular surgery and e-Health innovation, the potential of e-Health technology to mitigate greenhouse gas emissions generated by the health care sector, and to provide leads to research promoting e-Heath technology sustainability. A multifaceted approach, including ethical design, validated eco-audits methodology and reporting standards, technological refinement, electronic and medical devices reuse and recycling, and effective policies is required to provide a sustainable and optimal level of care to vascular patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Computational surgery (CS) is an interdisciplinary field that uses mathematical models and algorithms to focus specifically on operative planning, simulation, and outcomes analysis to improve surgical care provision. As the digital revolution transforms the surgical work environment through broader adoption of artificial intelligence and machine learning, close collaboration between surgeons and computational scientists is not only unavoidable, but will become essential. In this review, the authors summarize the main advances, as well as ongoing challenges and prospects, that surround the implementation of CS techniques in vascular surgery, with a particular focus on the care of patients affected by abdominal aortic aneurysms (AAAs). Several key areas of AAA care delivery, including patient-specific modelling, virtual surgery simulation, intraoperative imaging-guided surgery, and predictive analytics, as well as biomechanical analysis and machine learning, will be discussed. The overarching goals of these CS applications is to improve the precision and accuracy of AAA repair procedures, while enhancing safety and long-term outcomes. Accordingly, CS has the potential to significantly enhance patient care across the entire surgical journey, from preoperative planning and intraoperative decision making to postoperative surveillance. Moreover, CS-based approaches offer promising opportunities to augment AAA repair quality by enabling precise preoperative simulations, real-time intraoperative navigation, and robust postoperative monitoring. However, integrating these advanced computer-based technologies into medical research and clinical practice presents new challenges. These include addressing technical limitations, ensuring accuracy and reliability, and managing unique ethical considerations associated with their use. Thorough evaluation of these aspects of advanced computation techniques in AAA management is crucial before widespread integration into health care systems can be achieved., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Vascular surgical training is evolving towards simulation-based methods to enhance skill development, ensure patient safety, and adapt to changing regulations. This study aims to investigate the utilization of simulation training among vascular surgeons in France, amidst ongoing shifts in teaching approaches and educational reforms., Methods: A national survey assessed the experiences and perceptions of vascular surgery professionals regarding simulation training. Participation was open to self-reported health professionals specialized (or specializing) in vascular surgery, including interns or fellows. Participants were recruited through various channels, and data were collected via a questionnaire covering participant characteristics, simulation experiences, and perceptions., Results: Seventy-six participants, predominantly male (74%) took part in the survey. While 58% reported access to simulation laboratories, only 17% had organized simulation sessions 1-3 times a year, and 5% had sessions more than 10 times annually. High fidelity simulators were available in 57% of institutions, while low fidelity simulators were available in 50%. Regarding funding, 20% received financial assistance for training, predominantly from industry (18%). One-third of the participants experienced 9 or more sessions (34%), lasting between 1 and 2 hours (34%), 30% expressed satisfaction with access to simulation, while 33% were dissatisfied with communication of simulation training opportunities., Conclusions: Despite recognizing the benefits of simulation training, its integration into vascular surgery education in France remains incomplete. Challenges such as limited access and communication barriers hinder widespread adoption. Collaborative efforts are needed to ensure uniformity and enhance the effectiveness of simulation training in vascular surgery education., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: Large language models and artificial intelligence (AI) based chatbots have brought new insights in healthcare, but they also raise major concerns. Their applications in vascular surgery have scarcely been investigated to date. This international survey aimed to evaluate the perceptions and feedback from vascular surgeons on the use of AI chatbots in vascular surgery., Methods: This international open e-survey comprised 50 items that covered participant characteristics, their perceptions on the use of AI chatbots in vascular surgery, and their user experience. The study was designed in accordance with the Checklist for reporting Results of Internet E-Surveys and was critically reviewed and approved by international members of the European Vascular Research Collaborative (EVRC) prior to distribution. Participation was open to self reported health professionals specialised (or specialising) in vascular surgery, including residents or fellows., Results: Of the 342 individuals who visited the survey page, 318 (93%) agreed to participate; 262 (82.4%) finished the survey and were included in the analysis. Most were consultants or attending physicians (64.1%), most declared not having any training or education related to AI in healthcare (221; 84.4%), and 198 (75.6%) rated their knowledge about the abilities of AI chatbots between average to very poor. Interestingly, 95 participants (36.3%) found that AI chatbots were very useful or somewhat useful in clinical practice at this stage and 229 (87.4%) agreed that they should be systematically validated prior to being used. Eighty participants (30.5%) had specifically tested it for questions related to clinical practice and 59 (73.8%) of them experienced issues or limitations., Conclusion: This international survey provides an overview of perceptions of AI chatbots by vascular surgeons and highlights the need to improve knowledge and training of health professionals to better evaluate, define, and implement their use in vascular surgery., (© 2024 The Author(s).)

Introduction: Endoleaks represent one of the main complications after endovascular aortic repair (EVAR) and can lead to increased re-intervention rates and secondary rupture. Serial lifelong surveillance is required and traditionally involves cross-sectional imaging with manual axial measurements. Artificial intelligence (AI)-based imaging analysis has been developed and may provide a more precise and faster assessment. This study aims to evaluate the ability of an AI-based software to assess post-EVAR morphological changes over time, detect endoleaks, and associate them with EVAR-related adverse events., Methods: Patients who underwent EVAR at a tertiary hospital from January 2017 to March 2020 with at least 2 follow-up computed tomography angiography (CTA) were analyzed using PRAEVAorta 2 (Nurea). The software was compared to the ground truth provided by human experts using Sensitivity (Se), Specificity (Sp), Negative Predictive Value (NPV), and Positive Predictive Value (PPV). Endovascular aortic repair-related adverse events were defined as aneurysm-related death, rupture, endoleak, limb occlusion, and EVAR-related re-interventions., Results: Fifty-six patients were included with a median imaging follow-up of 27 months (interquartile range [IQR]: 20-40). There were no significant differences overtime in the evolution of maximum aneurysm diameters (55.62 mm [IQR: 52.33-59.25] vs 54.34 mm [IQR: 46.13-59.47]; p=0.2162) or volumes (130.4 cm 3 [IQR: 113.8-171.7] vs 125.4 cm 3 [IQR: 96.3-169.1]; p=0.1131) despite a -13.47% decrease in the volume of thrombus (p=0.0216). PRAEVAorta achieved a Se of 89.47% (95% confidence interval [CI]: 80.58 to 94.57), a Sp of 91.25% (95% CI: 83.02 to 95.70), a PPV of 90.67% (95% CI: 81.97 to 95.41), and an NPV of 90.12% (95% CI: 81.70 to 94.91) in detecting endoleaks. Endovascular aortic repair-related adverse events were associated with global volume modifications with an area under the curve (AUC) of 0.7806 vs 0.7277 for maximum diameter. The same trend was observed for endoleaks (AUC of 0.7086 vs 0.6711)., Conclusions: The AI-based software PRAEVAorta enabled a detailed anatomic characterization of aortic remodeling post-EVAR and showed its potential interest for automatic detection of endoleaks during follow-up. The association of aortic aneurysmal volume with EVAR-related adverse events and endoleaks was more robust compared with maximum diameter., Clinical Impact: The integration of PRAEVAorta AI software into clinical practice promises a transformative shift in post-EVAR surveillance. By offering precise and rapid detection of endoleaks and comprehensive anatomic assessments, clinicians can expect enhanced diagnostic accuracy and streamlined patient management. This innovation reduces reliance on manual measurements, potentially reducing interpretation errors and shortening evaluation times. Ultimately, PRAEVAorta's capabilities hold the potential to optimize patient care, leading to more timely interventions and improved outcomes in endovascular aortic repair., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objective: There is a paucity of observational data including long term outcomes after invasive treatment for carotid artery stenosis., Methods: This retrospective study used nationwide insurance claims from the third largest provider in Germany, DAK-Gesundheit. Patients who underwent inpatient carotid endarterectomy (CEA) or carotid artery stenting (CAS) between 1 January 2008 and 31 May 2017 were included. The Elixhauser comorbidity scores from longitudinally linked hospital episodes were used. Kaplan-Meier analysis and the log rank test were used to determine long term stroke free survival. Multivariable regression models were developed to adjust for confounding., Results: A total of 22 637 individual patients (41.6% female, median age 72.5 years) were included, of whom 15 005 (66.3%) were asymptomatic and 17 955 (79.3%) underwent CEA. After a median of 48 months, 5 504 any stroke or death events were registered. The mortality rate varied between 0.4% (CEA for asymptomatic stenosis) and 2.1% (urgent CAS for acute stroke patients) at 30 days, and between 4.1% and 8.4% at one year, respectively. The rate for any stroke varied between 0.6% (CEA for asymptomatic stenosis) and 2.5% (CAS for symptomatic patients) at 30 days, and between 2.5% and 6.4% at one year, respectively. The combined rate for any stroke and mortality at one year was 6.3% (CEA for asymptomatic stenosis), 8.7% (CAS for asymptomatic stenosis), and 12.5% (urgent CAS for acute stroke patients). After five years, the overall stroke rate was 7.4% after CEA and 9.0% after CAS. In adjusted analyses, both older age and van Walraven comorbidity score were associated with events, while treatment of asymptomatic stenosis was associated with lower event rates., Conclusion: The current study revealed striking differences between previous landmark trials and real world practice. It further suggested excess deaths among invasively treated asymptomatic patients., (Copyright © 2023 European Society for Vascular Surgery. Published by Elsevier B.V. All rights reserved.)

Objective: The use of Natural Language Processing (NLP) has attracted increased interest in healthcare with various potential applications including identification and extraction of health information, development of chatbots and virtual assistants. The aim of this comprehensive literature review was to provide an overview of NLP applications in vascular surgery, identify current limitations, and discuss future perspectives in the field., Data Sources: The MEDLINE database was searched on April 2023., Review Methods: The database was searched using a combination of keywords to identify studies reporting the use of NLP and chatbots in three main vascular diseases. Keywords used included Natural Language Processing, chatbot, chatGPT, aortic disease, carotid, peripheral artery disease, vascular, and vascular surgery., Results: Given the heterogeneity of study design, techniques, and aims, a comprehensive literature review was performed to provide an overview of NLP applications in vascular surgery. By enabling identification and extraction of information on patients with vascular diseases, such technology could help to analyse data from healthcare information systems to provide feedback on current practice and help in optimising patient care. In addition, chatbots and NLP driven techniques have the potential to be used as virtual assistants for both health professionals and patients., Conclusion: While Artificial Intelligence and NLP technology could be used to enhance care for patients with vascular diseases, many challenges remain including the need to define guidelines and clear consensus on how to evaluate and validate these innovations before their implementation into clinical practice., Competing Interests: None., (© 2023 The Author(s).)

Introduction: The use of natural language processing (NLP) for a literature search has been poorly investigated in vascular surgery so far. The aim of this pilot study was to test the applicability of an artificial intelligence (AI) based mobile application for literature searching in a topic related to vascular surgery., Technique: A focused scientific question was defined to evaluate the performance of the AI application for a literature search and compare the results with the ground truth provided via a traditional literature search performed by human experts. Using pre-defined keywords, the literature search was performed automatically by the AI application through different steps, including quality assessment based on evaluation of the information available and quality filters using indicators of level of evidence, selection of publications based on relevancy filters using NLP, summarisation, and visualisation of the publications via the mobile app. A traditional literature search performed by human experts required 10 hours to check 154 original articles, among which 26 (16.9%) were truly related to the question, 63 (40.9%) related to the field but not to the specific question, and 65 (42.2%) were unrelated. The AI based search was performed in less than one hour, and, compared with traditional search, the method identified 17 original articles (48.6%) truly related to the question ( p < .010), 18 (51.4%) related to the field but not to the specific question ( p  = .26), and no unrelated publications ( p < .001). Fifteen truly related articles (88.2%) were identified jointly by the two methods. No significant difference was observed regarding the median number of citations, year of publications, and impact factor of journals., Discussion: The AI based method enabled a targeted, focused, and time saving literature search, although the selection of publications was not completely exhaustive. These results suggest that such an AI driven application is a complementary tool to help researchers and clinicians for continuous education and dissemination of knowledge., Competing Interests: None., (© 2023 The Author(s).)

Despite advances in prevention, detection, and treatment, cardiovascular disease is a leading cause of mortality and represents a major health problem worldwide. Artificial intelligence and machine learning have brought new insights to the management of vascular diseases by allowing analysis of huge and complex datasets and by offering new techniques to develop advanced imaging analysis. Artificial intelligence-based applications have the potential to improve prognostic evaluation and evidence-based decision making and contribute to vascular therapeutic decision making. In this scoping review, we provide an overview on how artificial intelligence could help in vascular surgical clinical decision making, highlighting potential benefits, current limitations, and future challenges., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Cardiovascular disease represents a source of major health problems worldwide, and although medical and technical advances have been achieved, they are still associated with high morbidity and mortality rates. Personalized medicine would benefit from novel tools to better predict individual prognosis and outcomes after intervention. Artificial intelligence (AI) has brought new insights to cardiovascular medicine, especially with the use of machine learning techniques that allow the identification of hidden patterns and complex associations in health data without any a priori assumptions. This review provides an overview on the use of artificial intelligence-based prediction models in vascular diseases, specifically focusing on aortic aneurysm, lower extremity arterial disease, and carotid stenosis. Potential benefits include the development of precision medicine in patients with vascular diseases. In addition, the main challenges that remain to be overcome to integrate artificial intelligence-based predictive models in clinical practice are discussed., Competing Interests: Declaration of Competing Interest The authors declare no competing interest, (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: Antithrombotic strategies are currently recommended for the treatment of lower extremity artery disease (LEAD) but specific scores to assess the risk of bleeding in these patients are scarce. To fill the gap, the OAC3-PAD bleeding score was recently developed and validated in German cohorts. The aim of this study was to determine whether this score performs appropriately in another real world nationwide cohort., Methods: This 10 year retrospective, multicentre study based on French national electronic health data included patients who underwent revascularisation for LEAD between January 2013 and June 2022. The OAC3-PAD score was calculated and from this, the population was classified into four groups: low, low to moderate, moderate to high and high risk. A binary logistic regression model was applied, with major bleeding occurring at one year (defined using the International Classification of Diseases ICD-10) as the dependent variable. The performance of the OAC3-PAD bleeding score was investigated using a receiver operating characteristic curve., Results: Among 161 205 patients hospitalised for LEAD treatment in French institutions, the one year incidence of major bleeding was 13 672 patients (8.5%). The distribution of the population according to the OAC3-PAD bleeding score was: 88 835 patients (55.1%), 34 369 (21.3%), 27 914 (17.3%), and 10 087 (6.3%) in the low, low to moderate, moderate to high, and high risk groups, respectively; with an incidence of one year major bleeding of 5.0%, 9.8%, 13.2%, and 21.3%. The OAC3-PAD model achieved an AUC of 0.650 to predict one year major bleeding following LEAD repair (95% CI 0.645 - 0.655), with a sensitivity of 0.67 and a specificity of 0.57., Conclusion: This nationwide analysis confirmed the accuracy of the OAC3-PAD model to predict one year major bleeding and served as external validation. Although further studies are required, it adds evidence and perspectives to further generalise its use to guide the management of patients with LEAD., (Copyright © 2023 European Society for Vascular Surgery. Published by Elsevier B.V. All rights reserved.)

Introduction: Visceral arterial aneurysms (VAAs) are life threatening. Due to the paucity of symptoms and rarity of the disease, VAAs are underdiagnosed and underestimated. Artificial intelligence (AI) offers new insights into segmentation of the vascular system, and opportunities to better detect VAAs. This pilot study aimed to develop an AI based method to automatically detect VAAs from computed tomography angiography (CTA)., Methods: A hybrid method combining a feature based expert system with a supervised deep learning algorithm (convolutional neural network) was used to enable fully automatic segmentation of the abdominal vascular tree. Centrelines were built and reference diameters of each visceral artery were calculated. An abnormal dilatation (VAAs) was defined as a substantial increase in diameter at the pixel of interest compared with the mean diameter of the reference portion. The automatic software provided 3D rendered images with a flag on the identified VAA areas. The performance of the method was tested in a dataset of 33 CTA scans and compared with the ground truth provided by two human experts., Results: Forty-three VAAs were identified by human experts (32 in the coeliac trunk branches, eight in the superior mesenteric artery, one in the left renal, and two in the right renal arteries). The automatic system accurately detected 40 of the 43 VAAs, with a sensitivity of 0.93 and a positive predictive value of 0.51. The mean number of flag areas per CTA was 3.5 ± 1.5 and they could be reviewed and checked by a human expert in less than 30 seconds per CTA., Conclusion: Although the specificity needs to be improved, this study demonstrates the potential of an AI based automatic method to develop new tools to improve screening and detection of VAAs by automatically attracting clinicians' attention to suspicious dilatations of the visceral arteries., Competing Interests: None., (© 2023 The Author(s).)

Objective: Applications of artificial intelligence (AI) have been reported in several cardiovascular diseases but its interest in patients with peripheral artery disease (PAD) has been so far less reported. The aim of this review was to summarize current knowledge on applications of AI in patients with PAD, to discuss current limits, and highlight perspectives in the field., Methods: We performed a narrative review based on studies reporting applications of AI in patients with PAD. The MEDLINE database was independently searched by two authors using a combination of keywords to identify studies published between January 1995 and December 2021. Three main fields of AI were investigated including natural language processing (NLP), computer vision and machine learning (ML)., Results: NLP and ML brought new tools to improve the screening, the diagnosis and classification of the severity of PAD. ML was also used to develop predictive models to better assess the prognosis of patients and develop real-time prediction models to support clinical decision-making. Studies related to computer vision mainly aimed at creating automatic detection and characterization of arterial lesions based on Doppler ultrasound examination or computed tomography angiography. Such tools could help to improve screening programs, enhance diagnosis, facilitate presurgical planning, and improve clinical workflow., Conclusions: AI offers various applications to support and likely improve the management of patients with PAD. Further research efforts are needed to validate such applications and investigate their accuracy and safety in large multinational cohorts before their implementation in daily clinical practice., (Copyright © 2022 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Objective: Contrast-enhanced computed tomography angiography (CTA) is commonly used to investigate acute abdominal conditions, but the risk of contrast-induced acute kidney injury (CI-AKI) has been poorly investigated in patients with acute mesenteric ischemia. The aim of the present study was to evaluate the incidence of CI-AKI in these patients and identify potential predictive factors., Methods: Patients admitted for acute mesenteric ischemia who had a diagnostic CTA with contrast medium and a follow-up of creatinine concentration were retrospectively included., Results: Among 53 patients included, 9 (16.9%) developed CI-AKI. The prevalence of chronic kidney disease did not differ significantly between those who developed CI-AKI and those who did not (33.3 vs 18.2%, p =.372). Plasma total bilirubin and conjugated bilirubin levels were significantly higher in patients who developed CI-AKI (17.5 vs 8.0 μmol/L, p =.013 and 8.0 vs 3.0 μmol/L, p =.031, respectively). The proportion of patients who had revascularization was similar between patients who developed CI-AKI and those who did not (11.1 vs 20.5%, p >.999). No significant difference was observed for 30-day mortality and all-cause mortality for a median follow-up of 168 days (22.2 vs 13.6%, p =.611; and 33.3 vs 61.4%, p =.153, respectively)., Conclusion: This study reports the incidence of CI-AKI in patients with acute mesenteric ischemia after diagnostic CTA with contrast medium. Plasma bilirubin levels were a predictive factor of CI-AKI in these patients. The administration of contrast media during revascularization was not associated with an increased risk of CI-AKI.

Background: Telemedicine has the potential to revolutionize healthcare. While the development of digital health technologies for the management of patients with cardiovascular diseases has been increasingly reported, applications in vascular surgery have been far less specifically investigated. The aim of this review is to summarize applications related to telemedicine in vascular surgery, highlighting expected benefits, current limits and future directions., Methods: The MEDLINE database was searched using a combination of keywords to identify studies related to telehealth/telemedicine in three main pathologies, including aortic, peripheral artery and carotid disease. A comprehensive literature review was performed to identify the type of digital application, intended use, expected benefits, strengths and limitations., Results: Telemedicine can improve the management of patients through digital platforms allowing teleconsultation, telemonitoring or telecoaching. Intended use involved remote consultation with a vascular surgeon, applications to enhance education, self-management, follow-up or adherence to treatment or lifestyle changes., Conclusion: Telemedicine offers innovative perspectives to improve access to care in distant locations and optimize care through patients' empowerment and personalized follow-up, contributing to the development of precision medicine. Huge efforts remain necessary for its implementation in daily clinical practice and involve ethical, legal, technical, economic and cultural considerations., Competing Interests: The authors declare no conflict of interest.

Chimney endovascular aortic aneurysm repair (ch-EVAR) has become a valid alternative to treat complex aneurysms but the occurrence of contrast-induced kidney injury (CI-AKI) is poorly known. This study investigated the incidence and the impact of CI-AKI on post-operative outcomes after ch-EVAR. Consecutive patients who underwent ch-EVAR between July 2010 and 2021 were retrospectively included. CI-AKI was defined based on plasma creatinine levels within 7 days after the intervention according to the "Kidney Disease Improving Global Outcomes" (KDIGO) classification. Among 102 patients included, CI-AKI occurred in 14 cases (13.7%). The 30-day post-operative mortality and complications were significantly higher in patients who developed CI-AKI compared with those who did not (50 vs 9.1%, P = .001 and 57.1 vs 20.5%, P = .007). Over a median follow-up of 24 months (3-39), overall mortality was also significantly higher (78.6 vs 33.0%, P = .002). The pre-operative platelet-to-lymphocyte ratio (PLR) was significantly higher in patients who developed CI-AKI (224.5 vs 147.6, P = .008). CI-AKI is frequent after ch-EVAR and is associated with worse post-operative outcomes. This should increase awareness of clinicians to optimize preventive and therapeutic strategies.