Seminars in Vascular Surgery最新文献

This systematic review aimed to identify sex-specific outcomes in men and women after carotid endarterectomy (CEA) and carotid artery stenting (CAS), including transfemoral and transcarotid. A search of literature published from January 2000 through December 2022 was conducted using key terms attributed to carotid interventions on PubMed. Studies comparing outcome metrics post intervention (ie, myocardial infarction [MI], cerebral vascular accident [CVA] or stroke, and long-term mortality) among male and female patients were reviewed. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. Overall, all studies reported low rates of perioperative complications. Among the studies that did not stratify outcomes by the preoperative symptom status, there were no significant sex differences in rates of perioperative strokes or MIs. Two studies, however, noted a higher rate of 30-day mortality in male patients undergoing CEA than in female patients. Analysis of asymptomatic patients undergoing CEA revealed no difference in perioperative MIs (female: 0% to 1.8% v male: 0.4% to 4.3%), similar rates of CVAs (female: 0.8% to 5% v male: 0.8% to 4.9%), and no significant differences in the long-term mortality outcomes. Alternatively, symptomatic patients undergoing CEA reported a higher rate of CVAs in female patients vs. male patients (7.7% v 6.2%) and showed a higher rate of death in female patients (1% v 0.7%). Among studies that did not stratify outcome by symptomatology, there was no difference in the 30-day outcomes between sexes for patients undergoing CAS. Asymptomatic patients undergoing CAS demonstrated similar incident rates across perioperative MIs (female: 0% to 5.9% v male: 0.28% to 3.3%), CVAs (female: 0.5% to 4.1% v male: 0.4% to 6.2%), and long-term mortality outcomes (female: 0% to 1.75% v male: 0.2% to 1.5%). Symptomatic patients undergoing CAS similarly reported higher incidences of perioperative MIs (female: 0.3% to 7.1% v male: 0% to 5.5%), CVAs (female: 0% to 9.9% v male: 0% to 7.6%), and long-term mortality outcomes (female: 0.6% to 7.1% v male: 0.5% to 8.2%). Sex-specific differences in outcomes after major vascular procedures are well recognized. Our review suggests that symptomatic female patients have a higher incidence of neurologic and cardiac events after carotid interventions, but that asymptomatic patients do not.

Thoracic outlet syndrome (TOS) is a rare anatomic condition caused by compression of neurovascular structures as they traverse the thoracic outlet. Depending on the primary structure affected by this spatial narrowing, patients present with one of three types of TOS—venous TOS, arterial TOS, or neurogenic TOS. Compression of the subclavian vein, subclavian artery, or brachial plexus leads to a constellation of symptoms, including venous thrombosis, with associated discomfort and swelling; upper extremity ischemia; and chronic pain due to brachial plexopathy. Standard textbooks have reported a predominance of females patients in the TOS population, with females comprising 70%. However, there have been few comparative studies of sex differences in presentation, treatment, and outcomes for the various types of TOS.

Disparities in outcomes for patients with cardiovascular disease and those undergoing cardiac or vascular operations are well-established. These disparities often span several dimensions and persist despite advancements in medical and surgical care; sex is among the most pervasive. Specifically, females sex has been implicated as a predictor of poor outcomes in both patients with acute type A aortic dissections (ATAADs) and type B aortic dissections (TBADs). For instance, one study, using the International Registry of Acute Aortic Dissection database, found that females with acute aortic dissection—including ATAAD and TBAD that were either medically or surgically managed—had 40% higher odds of in-hospital mortality than men. Notably, both types of acute aortic dissections affect men more commonly than females and can be life-threatening without prompt, appropriate treatment. The underlying mechanisms for these disparities are unclear but are thought to be multifactorial. The association of sex with patterns of disease and outcomes in patients with ATAAD or TBAD remains unclear, with conflicting reports from different studies. Thus, we sought to review the literature regarding sex disparities in patients with ATAAD and TBAD.

The rapid adoption of artificial intelligence (AI) into everyday use has presented multiple issues for surgical educators to consider. In this article, the authors discuss some of the ethical aspects of academic integrity and the use of AI. These issues include the importance of understanding the current limits of AI and the inherent biases of the technology. The authors further discuss the ethical considerations of the use of AI in surgical training and in clinical use, with an emphasis on vascular surgery.

Application of artificial intelligence (AI) has revolutionized the utilization of big data, especially in patient care. The potential of deep learning models to learn without a priori assumption, or without prior learning, to connect seemingly unrelated information mixes excitement alongside hesitation to fully understand AI's limitations. Bias, ranging from data collection and input to algorithm development to finally human review of algorithm output affects AI's application to clinical patient presents unique challenges that differ significantly from biases in traditional analyses. Algorithm fairness, a new field of research within AI, aims to mitigate bias by evaluating the data at the preprocessing stage, optimizing during algorithm development, and evaluating algorithm output at the postprocessing stage. As the field continues to develop, being cognizant of the inherent biases and limitations related to black box decision making, biased data sets agnostic to patient-level disparities, wide variation of present methodologies, and lack of common reporting standards will require ongoing research to provide transparency to AI and its applications.

In the past decade, artificial intelligence (AI)-based applications have exploded in health care. In cardiovascular disease, and vascular surgery specifically, AI tools such as machine learning, natural language processing, and deep neural networks have been applied to automatically detect underdiagnosed diseases, such as peripheral artery disease, abdominal aortic aneurysms, and atherosclerotic cardiovascular disease. In addition to disease detection and risk stratification, AI has been used to identify guideline-concordant statin therapy use and reasons for nonuse, which has important implications for population-based cardiovascular disease health. Although many studies highlight the potential applications of AI, few address true clinical workflow implementation of available AI-based tools. Specific examples, such as determination of optimal statin treatment based on individual patient risk factors and enhancement of intraoperative fluoroscopy and ultrasound imaging, demonstrate the potential promise of AI integration into clinical workflow. Many challenges to AI implementation in health care remain, including data interoperability, model bias and generalizability, prospective evaluation, privacy and security, and regulation. Multidisciplinary and multi-institutional collaboration, as well as adopting a framework for integration, will be critical for the successful implementation of AI tools into clinical practice.

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.

Deep learning, a subset of machine learning within artificial intelligence, has been successful in medical image analysis in vascular surgery. Unlike traditional computer-based segmentation methods that manually extract features from input images, deep learning methods learn image features and classify data without making prior assumptions. Convolutional neural networks, the main type of deep learning for computer vision processing, are neural networks with multilevel architecture and weighted connections between nodes that can “auto-learn” through repeated exposure to training data without manual input or supervision. These networks have numerous applications in vascular surgery imaging analysis, particularly in disease classification, object identification, semantic segmentation, and instance segmentation. The purpose of this review article was to review the relevant concepts of machine learning image analysis and its application to the field of vascular surgery.

In recent years, artificial intelligence (AI) has permeated different aspects of vascular surgery to solve challenges in clinical practice. Although AI in vascular surgery is still in its early stages, there have been promising developments in its applications to vascular diagnosis, risk stratification, and outcome prediction. By establishing a baseline knowledge of AI, vascular surgeons are better equipped to use and interpret the data from these types of projects. This review aims to provide an overview of the fundamentals of AI and highlight its role in helping vascular surgeons overcome the challenges of clinical practice. In addition, we discuss the limitations of AI and how they affect AI applications.